• This will involve no change in blog name (Kellblog) or URL (https://kellblog.com) so web readers should be unaffected.
• Subscribers may be affected. I will attempt to migrate my email subscription list — which they say will work — but if that fails, please re-subscribe in about a week or two.
• Posts will come from a new email so please be sure to check spam, mark the new sender safe, etc.
• I’m not sure of the new email sender, but it will likely be newsletter@kellblog.com or kellblog@ghost.io. I will update things here once I know for sure.
• The feed address should remain: https://kellblog.com/rss/
• Some things will get mangled in the process. For example, Ghost does not support something like the WordPress slideshow carousel so embedded slides seem to end up one giant image. Happily, I always link to PDF versions as well.
• I am not sure if comments are migrating. If they don’t, thank you for making them and I hope you enjoyed the discussion. We’ll have new ones on the new platform.
• Categories and tags will get mangled. WordPress supports both, but Ghost only supports tags. This will likely be a big manually clean-up on my end. Yipee.
• I’m only going to manually check older posts for errors if they’re popular. Thus, older more obscure posts may end up with formatting errors. Please let me know (via comments) if you see one.
• I will also delete some posts in the process as I do from time to time anyway.
• Thank you for your patience in this process. If you have questions or comments, please contact me at my Balderton email address (dkellogg@balderton.com) which I’m currently using as my public email. Alternatively, reach me on X at @kellblog or LinkedIn at /in/kelloggdave.

For those interested in why I’m changing.

• I want to have a more modern look and simpler, responsive design that works better on mobile
• I am running on a long-unsupported blog WordPress template. As a former tech support rep, this makes me uncomfortable.
• WordPress is sufficiently complicated that I use only a subset its functionality and often with great trepidation.
• Editing within WordPress is a house of cards. The experience reminds me of the hilarious Move a Photo in Word meme videos.
• I was unable to hire either third-party or WordPress consultant who could successfully move me to a more modern template. No small amount of money was ignited.
• I am considering charging for subscriptions so I wanted a platform where I could do that easily, with flexible options, and a few clicks. (I have not decided yet and will try to have a freemium model if I do.)
• My experience with Substack’s migration tools made me feel like it was hopelessly ill-equipped. I tried, but gave up fairly quickly — which didn’t make me that sad because they have something of checkered reputation.

Thank you for reading. This will hopefully be seamless for all of you. I appreciate your support. Here’s a preview of the new site.

The post Upcoming Kellblog Platform Migration appeared first on Kellblog.

Let me start with what I think is the easy part. Using AI to draft a meeting agenda? Fine. Using it to generate a board deck? Also fine, though you’ll probably go to manual edits after the first or second draft. Using it to produce a document summary? Fine [1]. These are tasks where AI is essentially doing the grunt work of organizing information you already possess, and where the human judgment — yours — is the thing that actually matters.

Using AI to produce final documents? That’s dicier today — ask anyone in legal — but I think there’s a simple rule that applies to all of these examples.

That rule? Use AI to do whatever you want, but you own the output. Not the AI. You. If it’s wrong, that’s on you. If it misses something important, that’s on you. The moment you present something to a meeting, a customer, or a board, you are vouching for it. Saying “well, AI generated that part” is not a defense. It’s an abdication of duty.

The “Ad Hominem” Problem

Here’s something that bothers me about the discourse around AI-generated content: people hear, or even suspect [2], that AI wrote something and it’s immediately dismissed — not because of anything wrong with the content, but because of how it was produced. That’s a logical fallacy. Specifically, it’s a variant of ad hominem: attacking the source rather than the argument [3].

I frequently need to remind people of this. Judge what was said, not who — or what — said it [4] [5]. If the analysis is sound, the framing is useful, and the questions raised are the right ones, then the mechanism of production is largely beside the point. The quality of the thinking is what matters and what should be challenged.

That said — and this is important — the inverse is also true. Producing AI-generated content and presenting it as your own thinking is not okay. The problem isn’t that AI helped. The problem is the pretense that you did the thinking when you didn’t. Ownership means you’ve read it, challenged it, corrected it where it was wrong, and can defend it. If you can’t do that, you haven’t done your job.

AI as Calculator

I’ve always thought the right analogy for AI is the calculator. A wildly more powerful calculator, obviously, but a calculator nonetheless.

When calculators became ubiquitous, people lamented the loss of slide rule proficiency. And yes, something was lost. But the point of mathematics was never arithmetic. It was reasoning. If the calculator handled the arithmetic error-free, you could spend more time on the part that actually matters. The same logic applies here: there’s a lot more to argument and strategy than copywriting or slide formatting. If AI can handle the scaffolding, you should be able to spend more time on the substance.

The complication — and it’s a real one — is that AI can start to approximate thinking in ways a calculator never could. A calculator doesn’t write your memo. It doesn’t suggest your strategy. It doesn’t synthesize twenty pages of board material into five crisp questions. AI does all of that. And that creates a temptation toward laziness that calculators simply didn’t. The laziness is the problem, not the temptation toward it.

There’s also research starting to emerge suggesting that relying too heavily on AI can actually impair your own reasoning. You offload the synthesis, and you stop synthesizing. You offload the framing, and you stop framing. The cognitive muscle atrophies.

I was not surprised when I read reports that people with long streaks in Duolingo couldn’t speak well in practice. In my view, as a half-decent French speaker: if it doesn’t feel like work, you’re probably not learning [6]. Corrolary: if it doesn’t feel like work, you’re definitely not working.

How I Use AI in Board Work

Here’s what I often do with AI today. I sit on several boards, and I’ll sometimes load a board deck into a generative AI tool before the meeting. I ask for a summary. Then I’ll ask how it thinks the company is doing. I’ll then ask for the top 5 questions to ask in the meeting. Then, I’ll go read the deck with an eye toward what’s been extracted [7].

And then I’ll go back and challenge the AI. I think issue three is more important than issue one. I think it missed issue seven totally. I think issue two isn’t an issue; the company’s fixed it already. Often, I’ll bring competition into the picture because (in my humble opinion) most boards don’t spend enough time thinking about competition [8].

And here’s the question I’ve been wrestling with: should I be transparent about using AI to help generate those issues (or questions) when I bring it to a board meeting?

My instinct is yes. If I want to send the CEO a list of top five issues facing the company before the meeting, I have two choices:

• I can pretend I wrote it myself, unassisted. Complete with typos and hyphens instead of em-dashes.
• I can say, “here’s what I generated with Claude after iterating on your board deck” and copy/paste the final transcript.

Now, I know what management can think: “Well, we could have asked Claude, too” [9]. And I’m okay with that. My response would be: “Well, then, why didn’t you?” I just want the best topics list.

To me, the question isn’t where the list came from. The question is whether it’s the right list. That’s the only question that matters. Boards have very limited time together. We should think hard and use all available tools to ensure that we’re spending that time on the right issues.

The point isn’t the slides or the questions list or the agenda or the summary. The point is the conversation. To maximize value, we need to be having the right conversation. No talking about things easy to talk about. Not going through the motions. Not death marches through templates, much as I love both templates and death marches.

This takes me back to calculators. I can check the math on your board slides using pencil and paper. Or I can use a calculator. Or I can upload your table and ask Claude to check the math. We can take a test with our calculators secretly on our laps or with them in plain sight on our desks.

I vote for the second option. Use all available tools. Don’t use them clandestinely. Use them out in the open. But don’t abdicate to them. Own the output. This isn’t Claude’s list of our top five challenges. It’s my list, built using Claude [10]. Better yet, it’s my list, period. (But I’m not going to hide that I used Claude to help build it much as I wouldn’t hide that I used a computer and a keyboard.)

That’s where I am. I’m curious where you are. Is there a line you’ve drawn in your own work? Do you think transparency about AI assistance is a norm we should be enforcing, or are we creating a two-tiered standard we’d never apply to other tools? Let me know in the comments.

# # #

Notes

[1] Just as long as you also read it or are prepared to say, “I didn’t read it, I only read a summary.” FWIW, I find it useful to generate a summary, read it, and then read the document. And sometimes, then go back to the summary. The summary ends up serving as a reading guide.

[2] Thanks to tells like the dreaded em-dash.

[3] I had to air quote ad hominem because — thanks to my high school Latin teacher, Mr. Maddaloni — I know that ad hominem means literally “toward the man.” There is thus not only gendered language (heck, it was nearly 3,000 years ago) but considerable irony in speaking of ad hominem attacks on a machine. Ad machina, anyone?

[4] By the way, this is the exact opposite of most social media behavior.

[5] This isn’t just good intellectual hygiene — it’s a reliable way to reduce or eliminate bias. When you evaluate an argument on its merits rather than its source, you sidestep a whole class of distortions: the tendency to over-credit ideas from high-status people, to dismiss ideas from unexpected sources, or to reject a perfectly sound analysis because you don’t like the messenger. It’s a discipline worth practicing whether the source is a junior analyst, a competitor, or a language model. The argument either holds up or it doesn’t. That’s the only test that matters.

[6] This is a critique on gamification, but also is highly related to the topic of customer value metrics, about which I’ve written with my Balderton EIR colleague Dan Teodosiu.

[7] By the way, the wordier the board deck, the more this process helps.

[8] This itself could be a long discussion but remember three things: my first job in marketing was competitive analyst; I believe strategy is either “the plan to win” (Burgelman) or “the way to overcome our biggest challenge” (Rumelt), and ergo it cannot be done without looking at the market. North Stars are great, but they don’t tell you about the army you’re going to face when you hit latitude 55 degrees 45 minutes.

[9] And this is probably the kindest thought. Others might include:

• Perfect — now the monkeys have flamethrowers
• Fantastic — it’s like giving toddlers espresso and a whiteboard
• Great — now the VCs can skip even faster to the wrong conclusion
• Terrific — now it’s gut feel with citations
• Right — so now we’re pattern matching with turbo-autocomplete

(And those are manual em-dashes.)

[10] I assume that we are not all going to have the same AI conversation or all use the same tools. The way I push Claude is going to be different from the way another person does.

The post On the Socially Acceptable Use of AI in Business appeared first on Kellblog.

Everyone in M&A has heard the expression “great companies are bought, not sold.” It gets knowing nods in board meetings, but is then promptly ignored in practice. The reason it’s hard to internalize isn’t that the idea is obscure — it’s that acting on it requires you to behave in ways that feel completely wrong. It requires you to slow down, stay deliberately vague, and resist the urge to pitch. For a founder who has spent years getting good at pitching, that turns out to be genuinely difficult to do. Knowing something and behaving consistently with it are two different things. [2]

The Magic Box

Roizen’s central metaphor is the “magic box.” Some things are popsicles — they have known, comparable value, and you can auction them with reasonable confidence in the outcome. Startups are not popsicles. They’re magic boxes. A startup’s value isn’t fixed or objectively discoverable; it depends almost entirely on who’s opening the box and what they plan to build once they have it. The same company can be worth $50M to one acquirer and $500M to another — not because of negotiating leverage, but because of genuine strategic fit. Which means the job isn’t to run a wide process and let the market discover your price. It’s to find the buyer for whom your value is highest and help them see it — ideally before you ever hire a banker.

The Trail of Tears

The best part of the book is the start of Chapter 6, describing what Roizen calls the sad path. I call it the Trail of Tears, because founders walk it constantly — and the thing that makes it a tragicomedy is that every single step feels reasonable at the time.

These two pages are worth the price of the book alone (highlighting mine).

Tragicomedy.

For those who can’t read the images, it goes like this. One of startup Alpha’s investors happens to meet GiantCo’s head of corporate development at a conference. Corpdev thinks Alpha might be worth a look. The investor, delighted to add value, makes an introduction. Value added! [3]

The next day Alpha’s CEO gets an email from Corpdev asking for a deck he can socialize with the relevant product teams. The CEO panics slightly — what do I send? — and settles on his most recent investor presentation. It worked great for raising a big round. It’s got product detail, market sizing, competitive positioning, go-to-market strategy. Should do just fine.

Corpdev reviews the deck. He sees some potential but no clarity on where Alpha’s products might fit into GiantCo’s portfolio. He forwards it to a few product leads and a general manager. The deck is salesy. It was designed to solicit investment in Alpha as a standalone company. The deck’s salesy quality is read by GiantCo as a sign that Alpha is trying to sell itself.

A presentation is scheduled. The relevant GM — probably the best potential internal champion — can’t make it. The demo goes well. GiantCo’s attendees are engaged. The meeting ends with enthusiasm and a commitment to follow up.

Corpdev follows up by sending Alpha’s CEO a list of boilerplate diligence questions: financials, cap table, customer concentration, licensing. The kind of get-to-know-ya questions that corporate development types like. Roizen’s line here is worth remembering: Corpdev is using an X-ray when a telescope is what’s needed. Alpha’s CEO, under pressure from his investor for updates, has his finance team pull together a packet in response. Everything is proceeding mechnically at this point.

Corpdev now takes a critical look. Revenue is concentrated. Burn is high. Valuation expectations are probably rich given the cap table. Alpha is too early and GiantCo is too busy. The eventual reply: we really like what you’re doing, but it doesn’t map to any current priorities. Let’s stay in touch and try to connect again at next year’s conference. In short, you’re a nice guy/gal, but let’s be friends.

The investor wants an update. The CEO has to explain that nothing happened.

The wrong deck. The wrong follow-up. The right GM missing from the meeting. An X-ray instead of a telescope. No chance for the idea to form inside GiantCo. And now the bad news needs to be broken to an investor who was just trying to help.

It’s a sad path indeed. And the reason it works so well as a teaching device is that the CEO didn’t do anything stupid. They made reasonable calls at every step. That’s the point.

This literally happens every day. It wastes time. It’s demotivates founders, raising and then dashing expectations. Worse yet, its leaves the company with a residual “those guys are for sale” taint — a mark that’s hard to see and even harder to erase. [4]

The Partner Big Idea

What should have happened instead? Roizen’s answer is what he calls the Partner Big Idea (PBI). The mechanics of building a PBI are more involved than I’ll go into here — read the book — but the core principle is this: the deal has to become their idea, not yours.

The investor presentation was the original sin. It accidentally signaled that Alpha was for sale, which put GiantCo in evaluation mode rather than strategy-building mode. What Alpha needed wasn’t a buyer to evaluate it. It needed a champion within GiantCo — ideally that GM who missed the meeting — to develop a strategic vision that Alpha was necessary to execute. Not “Alpha is an interesting acquisition target” but “here’s a thing that we need that we can’t build without Alpha.”

Building that requires a totally different set of behaviors. It means getting to the right person quickly — the GM or product leader whose roadmap would actually change — and not spending lots of time with Corpdev. It means asking more questions than you answer. It means leaving the story incomplete enough that the other side has room to build it with you. Incompleteness, in this context, is a feature. It gives the champion something to build and own. [5]

Corpdev is not that person. Corpdev manages process and filters opportunities. They can help once a deal is real, but they rarely create the reason for the deal to happen. If your primary relationships are with Corpdev, you’re operating inside a system designed to evaluate, not to originate. And it’s a system that, left to its own devices, will evaluate your company on a financial, not a strategic, basis.

An Investment Banker Weighs In

I asked an investment banker friend, who works regularly with top strategic buyers, about the book and its relevance today. He had three key observations.

First, the importance of partnerships as a precursor to M&A has only grown since the book was written. Companies partner, integrate products, share customers. Over time, the relationship gets embedded in each side’s roadmap. At that point the “big idea” isn’t hypothetical — the buyer doesn’t just believe in the opportunity, they depend on it. The magic box becomes a dependency.

Second, geography matters to some more than the book acknowledges. Snowflake, for example, drew a reasonably hard line for a long time: a deal couldn’t happen unless the technical team relocated to one of their engineering hubs. With return-to-office (RTO) continuing to gain momementum, I think this will continue to increase in importance.

Third, don’t underestimate the importance of team buy-in. Strategic acquirers aren’t just buying code, they’re buying the team that builds it and they can tell the difference between teams are cashing out (and who will work until exactly the day their handcuffs disappear) and teams who are genuinely excited about a combined future. As a reflection of this, buyers are increasingly splitting the payment, sending more money to the retention pool and less money to the cap table. This creates a tension between investors and employees, but it all gets negotiated in the process.

But What About Banker-led Processes?

At this point, you might reasonably ask: how does all this square with the standard advice to hire a banker and run a process? Aren’t these two ideas in tension?

Not really. They operate on different timelines.

The work Roizen is describing is long-term and strategic. It’s about shaping how a potential acquirer sees your company years before any process begins — helping the right person inside the right company build a strategy that depends on you. You’re not selling the company. You’re teaching someone else why they might need to buy it. The Magic Box Paradigm is about getting bought.

A banker-led process is something else entirely. It’s about getting sold. It’s a short-term mechanism to create urgency, surface alternatives, and establish price. It can accelerate a deal. It can’t create the underlying reason for one to exist.

If the strategic groundwork has been laid — if there are multiple potential acquirers who already “get it” — then a process can work extremely well. It forces those buyers to act, on a timeline, in competition with both PE sponsors and one another.

If the ground hasn’t been laid, then the process tends to default to financial evaluation. You get Corpdev questions, lukewarm interest, and a lot of “not a priority right now.” In other words, a scaled-up and more formal version of the sad path.

One nuance here, having lived it: the hardest part is aligning timelines.

A PE-led auction runs on a clock. You set dates, people prepare bids, and the banker’s job is to keep everyone moving in a tight, predictable cadence. That’s how you create urgency and price tension.

Strategics don’t work that way. They need time — to line up a champion, to socialize the idea internally, to get product, finance, and executive buy-in. Occasionally they can turn on a dime, but that’s the exception, not the rule.

The tension is obvious. Run the process too fast and you lose the strategics. Run it too slow and you lose the auction dynamics. This is why you need to have relationships in place with strategics well before your banker process begins. Otherwise they simply cannot keep up.

The banker’s real job, in this context, is to try to align those timelines. Because the worst outcome is hearing what I once heard: “We’re very interested, but we can’t possibly execute on that timeline, so we’re going to drop out.”

And once that happens, you’ve lost exactly the buyer who might have valued you the most. Utter process failure. Think: You had one job!

So the two ideas aren’t in conflict. They’re parallel. Do the strategic work early — years before you’d contemplate selling. Then, if and when the time comes, use a banker to run a disciplined process on top of it.

Read it Early

This is not a book about how to run an M&A process. It’s a book about how deals actually form — which is a different and more important topic. The sad path exists because most founders don’t think about this until they’re already in it, at which point it’s very hard to correct.

Read it at least four years before you think you need it. Let it shape how you build relationships with potential acquirers. Help the right person inside the right company build a strategy they can’t execute without you — and make sure they realize it before you ever hire a banker.

If you do that, you may not need a process at all. And if you don’t, you can’t count on a process to save you.

# # #

Notes

[1] With a second edition published in 2023

[2] This is actually a broader problem in business. The list of things people nod at in board meetings and then promptly ignore would fill several books.

[3] Roizen’s deadpan “Value added!” is one of the funnier lines in the book.

[4] To be clear, the taint is that they’re always for sale and nobody wants to buy them. Imagine the house on a street with a perennial for-sale sign in front of it.

[5] This is counterintuitive enough that it’s worth sitting with. The instinct is to show up with a complete, polished narrative — that’s what pitching trains you to do. But a complete narrative leaves nothing for the other side to build. Their investment in the idea comes from the act of building it.

The post The Odd Little Book All Founders Should Read On Selling Their Company appeared first on Kellblog.

Take SaaS, for example. Today, you can find scores of blogs — including mine — that talk about how to run SaaS businesses, how to plan SaaS businesses, and how to produce and interpret SaaS metrics. Twenty years ago virtually none of that existed. You needed to figure it out. And one of the best ways to figure it out was to spend time with other people who were doing the same figuring.

There’s a time for timeless wisdom and there’s a time for talking to other people who are doing the same thing as you are, right now. I think AI and the massive disruption it creates in marketing — from content to workflow to performance marketing to analytics — means it’s an awesome time to join a marketing peer-networking community.

I tell this to literally every CMO I work with:

• Come to me for timeless wisdom (if not fleeting opinions).
• Join Exit Five (or equivalent) to talk to peers who face the same challenges you do, every day.

That’s not to say that following thought leaders isn’t a great tactic, too. I keep an eye on Emily Kramer, Elena Verna, Carilu Dietrich, Alice de Courcy, and Jon Miller. And I can’t wait for Rand Fishkin’s rumored new book on Zero-Click Marketing. If there are other marketing thought leaders you think I should be following, please let me know.

All this is why I was thrilled when Dave Gerhardt invited me to speak at Exit Five’s Marketing Leadership Retreat on March 19-20 in Phoenix. Given the above, I knew I wasn’t going to be the person delivering fresh-from-the-trenches information on AI tools and methods. The audience is 100x more qualified than I am to do that.

So what did I want to offer up instead? Some timeless wisdom. Specifically, timeless wisdom not just on how to successfully do the CMO job, but on how to be the CMO everyone wants to work with.

Why that topic? And bear in mind it takes a lot for me to pick a title that ends with a preposition. Because I thought it captured the key to success in an important way.

• It’s not just about doing the job. Yes, that’s quite hard already but if you only focus on that you ironically increase your odds of becoming a statistic.
• It’s not just about keeping the job. And yes, there’s an art to that, a big part of that is simply remembering to market marketing.
• It’s about helping the boss do their job which not only increases your strategic value, but makes you more “sticky” in your role.
• It’s about doing all that while being the CMO that everyone wants to work with (EW2WW)

Why does that last point matter?

• Marketing is inherently a service organization, so a strong internal customer service orientation never hurts.
• Being the CMO EW2WW helps you keep your job. With a median tenure of 18-24 months, this should never be too far from a CMO’s mind. Belt and suspenders.
• If everyone wants to work with you, it helps you find your next job. Board members and recruiters — your top two job sources after peers — will seek you out when the time comes.
• It might well help you get promoted to COO or CEO. If everyone wants to work with you, they might well give you a shot at the next level.
• It frames things as a positioning problem. And we know a lot about solving positioning problems, working backwards from a desired result.

Ultimately, I’m saying that CMOs should want to position themselves as the CMO EW2WW.

Put differently: “Marketer, position thyself.”

(Adapted without permission from Luke 4:23.)

With that as background, here are the slides from the presentation, embedded below, and downloadable here. I had a fairly miserable time in Gamma building them so apologies for the upside-down funnel, some of the formatting, graphics, and mechanics (e.g., the absence of copyright notice and slide numbers). There is only so much time I’m willing to spend explaining to a chatbot what expression to put on the reflected image of a face in a mirror. And once you drop into PowerPoint to make changes, there seems to be no going back.

Thanks again to Dave for having me and to Allison Saxon for working with me to make it happen.

The post Be the CMO Everyone Wants to Work With appeared first on Kellblog.

Well, if Japan is better at making cars than we are, then they should make cars and we should do other things. It’ll be rough for the displaced auto workers but it’s merely a transition period and, like Ricardo says, it’ll be a win/win for both countries and we’ll all be better off in the end.

If you put aside the many concerns with basing policy solely on comparative advantage (e.g., national security, supply-chain resilience, support for fledgling industries, externalities), you could at least get a good night’s sleep.

After all, as I pointed out in my 2026 predictions post, while it’s painful in the short term, we typically don’t miss the jobs displaced by technology.

I don’t think anyone misses elevator operators, a job that peaked in the 1950s and is all but extinct today. The same is true of switchboard operators, pin setters, lamplighters, linotype setters, icemen, typists, shorthand secretaries, and slubber doffers. Yet all these jobs were automated away. While I’m sure the transition was rough for any given elevator operator, as a society we don’t miss them. Most coal miners work hard so their children won’t have to be coal miners.

It sucks for them — the displaced workers — of course, but collectively it’s good for us. Or, in econ-speak, trade creates net national gains, but the costs are often geographically concentrated and painful (e.g., the Rust Belt). If you want to know what it feels like to be on the other end of this displacement, listen to some Bruce Springsteen: Youngstown, My Hometown, Independence Day, and my favorite, The River. That’s what it feels like in a dying town. That’s the darkness on the edge of town.

The pain is real and while occupational transfers are a solution, some are easier than others. An autoworker could transition to another manufacturing job (though often at lower wages) but would be unlikely to become a mechanical engineer. For the economy in general, jobs transition from one sector to another. For any given person, one displacement could be the end of the line.

Another excerpt from the predictions post:

Let’s remember where the word Luddite comes from. Today, we use the word to describe people opposed to new technology. But the Luddites were part of a labor movement; they didn’t destroy machines because they were technophobes, they destroyed them to protect jobs. So the transitions can be rough — and come with social unrest — when jobs are automated away. But we still don’t miss the jobs they fought for.

So, with our safe emotional distance from a 1970s auto worker, what would we have wanted to tell them at the start of the disruption? I’d say:

• This isn’t a blip, it’s a trend. This is not a problem you wait out.
• You could try to fight your way through this, but the odds will not be forever in your favor.
• If you’re nearing retirement, ride this one to the beach and go retire.
• If you’re early or mid-career, you need to switch horses.
• Switching early is better than switching late. The goal is to get onto the next horse before everyone else realizes the race has moved.

The popular horse-switching fantasy answer is retraining. “Go back to school and become an engineer.” In theory, yes. In practice, rarely. The jump from an assembly-line worker to an engineer requires years of schooling and a different educational foundation.

The real move for the autoworker was sideways, not upward: industrial maintenance, tool and die work, welding, industrial electrical work, construction trades, trucking, or logistics.

None of these are glamorous answers, but they share an important property: they’re adjacent to the skill set the person already has. They’re realistic.

And now, without the emotional distance we have from a 1970s auto worker, let’s try applying the same thinking to ourselves. One way to do that is to imagine what someone from the year 2070 might write back to us.

They might start the same way.

This isn’t a blip, it’s a trend. Don’t assume this wave of AI capability will conveniently stall just below your job description. You can try to fight your way through it, but the odds will not be forever in your favor. The job you’re defending may still exist, but the number of people needed to do it may fall dramatically.

They might add something else we don’t particularly like hearing: if you’re late in your career, you may be able to ride this out. Large organizations change slowly and adoption curves usually take time. But I think less so in this case — it might prove to be a short ride. If you’re early or mid-career, you should be thinking about switching horses.

But here the analogy gets interesting.

The auto worker in 1975 often had a high-school education and a skill set tightly tied to the factory floor. The realistic moves were sideways into adjacent trades.

The knowledge worker today, by contrast, is already highly trained. Many have college or graduate degrees and have spent years building transferable skills: writing, analysis, design, coding, planning, advising.

In other words, knowledge workers should be much easier to redeploy than auto workers ever were.

So yes, this all hits different because this time it’s us. That’s where we lose the emotional distance, and why the people trying to scare us sound so convincing.

Citrini’s recent thought experiment imagines a negative feedback loop with no natural brake. As they describe it:

AI capabilities improved, companies needed fewer workers, white-collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved …

Repeat.

In that scenario, the problem isn’t sudden unemployment so much as a slow erosion of the income base that supports the modern consumer economy. Highly paid white-collar workers lose their jobs, move into lower-paying work, compress wages across the service sector, and reduce spending at the same time.

The one thing I find most missing from the AI future analyses I’ve read is a simple realization: in my experience, there is always, always more work yet to do.

This relates to how I’ve always thought about the total available market (TAM) for business intelligence (BI). Over the decades I’ve worked in and around BI, I’ve been asked countless times whether the market was saturated or saturating.

My answer was always the same.

One day I’ll go to a cocktail party and ask everyone in the room: “Does everybody have all the information they need to make data-informed management decisions?” If everyone says yes, then on that day the BI market will be saturated.

But that day strikes me as a long way off. It did 40 years ago and it still does today. We have accomplished so, so much in BI and analytics. But there is still so much more to do.

Since no AI post would be complete without a reference to Jevons Paradox, let’s invoke it here: When technology makes something cheaper or more efficient, we tend to use more of it, not less. When steam engines became more efficient, coal consumption didn’t fall — it exploded because steam power became economical in far more applications. The same dynamic has repeated itself with computing, storage, and bandwidth. Every time technology dramatically increases our ability to do something, we don’t run out of work. We discover many more things worth doing.

The vast majority of management and strategic consulting today is about focus. And the reason focus matters so much is simple: execution bandwidth is limited. Organizations can only get so many things right.

So managers spend an enormous amount of time cutting, cutting, and cutting. Prioritizing, prioritizing, and prioritizing. Literally speaking, if we had to list the one key to business success, it’s focus. Why? Because there is so much work that we could do. So many strategies that we can imagine. So many things that we could do to help our customers.

It’s not that there’s only a little more work to do. There is an unfathomable amount of work yet to be done. The job of management is pruning that list down to something the organization can actually execute.

Which is why the idea that we’re somehow going to run out of work strikes me as absurd. It feels like a theory written by people who haven’t actually spent much time doing the work in the first place — serving customers, building products, and running businesses. There is always more that could be done.

There’s also an irony here. Increasingly, the people who say SaaS will stand for “service as a software” are the same people saying AI will massively disrupt employment. But when you sell service-as-a-software you are definitionally in the solutions business, solving problems, not merely delivering data and algorithms. And when you look at your customers through the lens of “problems yet to be solved,” you quickly discover that the list is very, very long.

So no, I’m not particularly worried about us running out of work to do.

I do think the transition could be difficult, especially if you’re not ready for it. So, given everything we’ve said thus far, here’s the advice I’d give to a knowledge worker heading into this transition.

You want to be driving the tools, not driven by them. Aggressively learn AI. Be the person who knows the most about solving problems using AI tools — integrating them, automating workflows. Not just generating content.

You want to understand the business outcome of the work you do. For example, if you’re in public relations, your end goal is awareness. Learn how to use these tools not just to generate articles but to drive awareness through whatever channels and mechanisms will make that possible.

You want to know a little about how the tools work, but not too much. People like me are naturally drawn to understanding the internals, but given the complexity of modern AI systems the more practical skill will be knowing how to use them.

One profession today that looks closest to the autoworker analogy is copywriting. If you’re a copywriter, you should already be learning multimedia and learning how to use AI tools to generate and manage all forms of content. Five years from now you won’t be manually writing and editing. You might be checking work, guiding it, generating variations, and certainly personalizing it at scale.

I know it’s a cliché, but I’ll end with a famous line from Wayne Gretzky, who attributed his success “not to skating where the puck is, but to where the puck is going.”

The puck is going toward heavy AI automation. That will create a new class of jobs around using AI to automate work — and another layer above that focused on managing, directing, and orchestrating those systems to actually run and grow businesses.

Skate there.

The post Why I’m Not Worried About Running Out of Work in the Age of AI appeared first on Kellblog.

So I’m always happy when someone wants to talk about software history.

His specific request was interesting: he was looking for case studies or books that were popular at the time — something that would help him understand how people in the industry were thinking back then.

I decided to do him one better. In my view, the real canon of books that shaped enterprise software thinking was largely written before 2000. So I assembled the following reading list: a set of 1990s-era books on software, strategy, marketing, and the industry itself that many of us were reading while the enterprise software industry was taking shape.

Think of it as a reading-list stroll down software, and Silicon Valley, memory lane.

1990s Era Tech Marketing and Strategy Books

High-Tech Marketing

• Marketing High Technology — William Davidow (1986)
• Crossing the Chasm — Geoffrey Moore (1991)
• The Gorilla Game — Geoffrey Moore, Paul Johnson & Tom Kippola (1998)
• The Regis Touch — Regis McKenna (1985)

Positioning / Marketing Foundations

• Positioning: The Battle for Your Mind — Al Ries & Jack Trout (1981)
• The 22 Immutable Laws of Marketing — Al Ries & Jack Trout (1993)
• Ogilvy on Advertising — David Ogilvy (1983)

Technology Strategy / Innovation

• The Innovator’s Dilemma — Clayton Christensen (1997)
• Competing for the Future — Gary Hamel & C. K. Prahalad (1994)
• Only the Paranoid Survive — Andrew Grove (1996)
• The Discipline of Market Leaders — Michael Treacy & Fred Wiersema (1995)

Product Marketing Culture

• The Macintosh Way — Guy Kawasaki (1990)

Enterprise Sales / Go-to-Market

• SPIN Selling — Neil Rackham (1988)
• Solution Selling — Michael Bosworth (1995)

Economics of Software / Networks

• Information Rules — Carl Shapiro & Hal Varian (1998)

Enterprise Technology Industry Case Studies

• Softwar: An Intimate Portrait of Larry Ellison and Oracle — Matthew Symonds (2003)
• The Ingres Papers: Anatomy of a Relational Database System — Michael Stonebraker et al (1985). Technical background on Ingres that provides useful context prior to reading Softwar.
• The New New Thing — Michael Lewis (1999)
• Hard Drive: Bill Gates and the Making of the Microsoft Empire — James Wallace & Jim Erickson (1992)
• Accidental Empires — Robert X. Cringely (1992)

Software Engineering

• The Mythical Man-Month — Frederick P. Brooks Jr. (1975)

Classical Strategy

• The Art of War — Sun Tzu

The post The Silicon Valley Canon, Circa 1998: A Stroll Down Software Memory Lane appeared first on Kellblog.

The idea was always that, once at scale, software companies could print money.

With SaaS, revenue recurred. If you could buy a dollar of annual recurring revenue (ARR) for one, or even two, dollars, then why not buy a lot of them? You’d break even on customer acquisition costs in year one or two — and everything after that was gravy. Raise VC, invest in sales and marketing, and grow, grow, grow. 

All the better if your market was greenfield and switching costs were high. When the music stopped, the company with the most market share would win. And for a long time, the music wasn’t stopping.  So, for market share: grab, grab, grab. 

Throw in cheap money — courtesy of low interest rates — and you get the Growth at All Costs (GAAC) era of SaaS. During GAAC, a (growth, profit) profile of (100%, -100%) was more attractive than (60%, -40%), which in turn beat (40%, 0%). Growth dominated everything.

Then the wind shifted.

Investors asked, “Why wait forever to print money?” Even if 40%+ mature margins weren’t required, why not produce some profit now?

Private equity (PE) became the most common exit path. And PE wants fixer-uppers, not teardowns. Improving margins in a profitable business is far easier than turning around an unprofitable one.

Moreover, PE enhances returns with leverage. Borrowing 1–2X ARR to finance a deal generates interest expense equal to 10–20% of revenue. If you’re not producing 20%+ margins, you won’t have the cash to service the debt. Losing money becomes a non-starter.

This spirit of balance crystallized in the Rule of 40: growth rate plus profit margin should equal at least 40. Grow as fast as you want — just don’t lose too much money (110%, -70%). Or grow more slowly and produce enough profit to compensate (20%, 20%).

The Rule of R40 (R40) didn’t replace GAAC overnight. It existed during the GAAC era as a disciplining metric — but it became more binding when capital tightened. 

In the post-GAAC era, R40 “worked.” Companies that complied with R40 generally traded at higher revenue multiples than those that didn’t. Statistically, it outperformed growth or margin alone as a predictor of valuation. More nuanced metrics were proposed, like the Rule of X, to remind us that a point of growth carries more weight than a point of profit. Even so, R40 remained the headline metric.

Then the wind shifted again. 

Investors began looking past NRR to GRR, exposing soft SaaS underbellies where expansion masked churn. Cohort analysis replaced year-over-year snapshots, and — because of lot of expansion often happens in year one — they generally showed a less pretty picture. Interest rates rose. Leverage became more expensive. Multiples compressed.

On top of all this, AI fears went viral, amplifying uncertainty across markets.

In short:

Ladies and gentlemen, we interrupt coverage of the SaaSacre to bring you live footage of the SaaSpocalypse.  

PE is a demanding overlord, largely unsympathetic to the daily pressures of customers and markets. While VC sees itself as “partnering with founders” to build a business, PE sees itself as “underwriting a model” — that they fully expect to achieve.

When the prevailing price of SaaS companies falls from 30X to 15X EBITDA, the model doesn’t bend. It breaks. 

Indulge me in an example and some arithmetic to make this concrete. Assume PE bought a company in 2023 for 30X EBITDA — $180M total — financing half with debt. That’s $90M of equity targeting a 3X return over four to six years. Under R40, the equity grows nicely in Years One and Two to 1.4X and 1.9X. Then multiples are cut in half, and the equity collapses to 0.7X.

What saves the deal? The Rule of 60. Maintain 20% growth while doubling EBITDA margins to 40%. Instead of 0.7X, the equity rebounds to 2.5X. The path to 3X+ reappears.

For the PE partner, switching to the Rule of 60 (R60) turns what would have been a 1.1X infield single into a 3.1X stand-up double. The trick, of course, is that management needs to figure out how expand EBITDA margins to 40% while preserving 20% growth. And here, unfortunately, “management” means you.

To summarize:

• Why the change to R60? Because the model fails without it.
• How do you double EBITDA while holding growth at 20%? That’s the hard part.

Nor is this some passing obsession. R60 isn’t PLG. It isn’t ABM. It isn’t a fad the board will tire of next quarter. It’s the financial model. That thing is out there. You can’t bargain with it. You can’t reason with it. It doesn’t feel pity or remorse or fear. And it absolutely will not stop. Ever. Until you are —

Okay, I know financial models aren’t Cyberdyne Series T-800 Terminators, though they sure can feel like them sometimes. 

But enough warnings about the inevitability of this change. Let’s switch to what you can do about it.

Here are 12 ideas to help you drive increased productivity and remain sane while doing it:

1. Ignore macro whiplash.

Don’t get wound up by techno-optimists or doomers. Watching the narrative swing day to day is as unproductive as tracking your stock price tick by tick — it will drive you insane. You have a job to do. Focus on it.

2. Reframe your job around efficiency.

Don’t measure success by team size or budget — if you ever did. Your job is to hit the ARR target while increasing ARR per seller and ARR per S&M dollar. Growing faster than your costs is the mandate now — and the skill your next employer will pay for.

3. Allocate the efficiency burden intelligently.

Work with your CEO to distribute the margin expansion burden intelligently across R&D, G&A, COGS, and S&M. Pro rata allocation is easy but rarely optimal. Don’t default to cutting S&M simply because it benchmarks high — or because the product-oriented founder won’t touch R&D and the CF-No refuses to trim G&A. Get in a room and have a hard conversation.

4. Operate as one revenue team.

Sales, Marketing, and Success must build the plan together and share accountability. Align on pipeline quality, win rate, churn, and NRR. While most teams think they’re aligned, few actually are. If you’re not answering each other’s calls on the first ring — or reallocating budget across departmental lines when needed — it’s not tight enough.

5. Increase street prices.

Raise list prices or discount less. If your category is PE-backed, your competitors face the same margin pressure and are likely doing the same. No one should be racing to the bottom right now. Show pricing discipline — and expand margins in the process.

6. Try “heretical” moves in your sales model.

Let reps run their own demos. Charge for POCs. Push SDRs into real discovery — or eliminate inbound SDRs altogether. Disqualify aggressively and walk away from bad-fit segments. If PLG applies, feed sales only PQLs. Go back to the ideas you once dismissed as crazy in brainstorming meetings and reconsider them. Let new constraints force new behavior.

Growing faster than your costs is the mandate now — and the skill your next employer will pay for.

7. Build a partner channel.

Start with partners as a lead source, then develop real channel leverage. Hire channel managers with meaningful quotas — effectively running partners as a leveraged sales force. If you need to improve GTM productivity, the channel isn’t “extra.” It’s structural leverage.

8. Improve deal mechanics.

Go back to basics with the sales velocity formula: opportunities × ASP × win rate ÷ sales cycle. Improve any variable and revenue per day increases. The most overlooked levers are win rate — start with rigorous win/loss analysis — and sales cycle length. Identify where deals stall and systematically accelerate them from discovery to demo to POC to legal. Time kills all deals.

9. Lean into AI for real work.

Move beyond experiments. Embed AI into workflows — content, analytics, segmentation, attribution, automation. It may take longer at first, but production use is the goal. Charter your Ops leader to know the leading AI tools in the GTM stack, educate the GTM leadership team on them, and develop a clear adoption roadmap.

10. Automate — but protect trust while you’re doing it.

Many companies will successfully automate with AI but will quietly erode customer trust in the process. Keep humans accessible in your workflows; escalation out of a chatbot should be effortless. Automate content generation, but don’t flood customers with slop. Never forget: There may be a human on the other side of your AI — even if sometimes it’s just another agent.

11. Engage with peer groups.

The fastest way to learn what’s working is by talking to operators doing the same job elsewhere. Shared intelligence compounds, which is exactly why communities like Pavilion and Exit 5 matter. Sometimes you want timeless wisdom; sometimes you need to talk to someone doing the exact same job as you at another company. Do both.

12. Protect your job by evolving it.

AI will eliminate some roles and create others. Be on the right side of that shift. Redesign your workflows, raise the productivity bar, and position yourself as the person who knows how to get leverage from the new tools. Then bring your team with you.

Adjusting to the New Reality

In this article, we’ve traced the path from GAAC to the Rule of 40 — and why capital markets are now pointing us toward the Rule of 60. Unless multiples suddenly double, hope is not a strategy, and margin pressure isn’t temporary but structural.

The 12 ideas above are about regaining control. Tune out the noise, redefine the mission around productivity, distribute the burden intelligently, and try the things you once thought were off-limits.

Use the new constraints to change behavior. And behavior change is where real performance improvement begins.

You can wait for multiples to bail you out or you can build a business that works at today’s multiples. The companies that figure this out won’t just endure this cycle, they’ll outperform in it. And the market will reward them — and the people who built them — accordingly.

# # #

Notes

This article was originally published in the Topline newsletter, a spin-off from the Pavilion go-to-market community. Since then, I’ve applied a few edits and style changes, but it’s largely the same content. Because Topline is a GTM newsletter, I wrote actions for the GTM executive, notably omitting AI product strategy. Because I often write for founders, not just about GTM but the whole company, this omission generated some confusion.

The post Why The Rule of 40 is Becoming the Rule of 60 — and What You Can Do About It. appeared first on Kellblog.

1. What it really means to be a manager, director, VP (2015). Now at ten years old, this post is a perennial favorite. I wrote it because I got tired of answering the question and something about my answer clearly struck a note with a lot of people. (Hint: the answer’s not in your job leveling system.)
2. How to navigate the pipeline crisis (2025). In this post I wrote about what I saw as a general pipeline crisis in the industry, shared some interesting posts on it, and then tried to put myself back in the CMO chair and answer: what would I do about it?
3. The one key to dealing with senior executives: answer the question! (2012). If the manager vs. director post (above) gets the most traffic, this post gets the most in-person mentions. Think: “Dave, I forwarded your ATFQ post about a dozen times this year.” This issue bothered me 13 years ago when I wrote the post and evidently non-answered questions are still bothering people today. If someone, particularly a customer or an executive, asks you a question: answer it.
4. Kellblog predictions for 2025 (2025). I scored these an 8 out of 10. Go here to read my predictions for 2026, the 12th annual post in this series. These posts are more industry commentary and analysis than simply a list of things I think are going to happen. And they require Herculean effort. This year’s post was 7,644 words with 166 links and took 65 hours to write.
5. Your ICP starts as an aspiration and ends as a regression (2025). I love the pithy title of this one. This post discusses the evolution of your ideal customer profile (ICP) which starts out as a wink in the founder’s eye and should, over time, end up the result of a regression analysis. That is, you start out by deciding who you want to focus on and then, over time and as a function of your definition of “success,” the data should tell you.
6. De-mystifying the growth-adjusted enterprise value to revenue multiple: introducing the ERG ratio (2024). I first heard of the PEG ratio in Peter Lynch’s classic, One Up on Wall Street. This post takes the same idea — growth adjusting — and applies it to price/sales as opposed to price/earnings. Much as I love the metric, I was frankly surprised to see this one up here.
7. The SaaS Rule of 40 (2017). Another classic, from eight years back. See this year’s predictions to understand why I believe the Rule of 40 might well become the Rule of 60 in 2026.
8. A CEO’s high-level guide to GTM troubleshooting (2025). An integration and repackaging of a lot of my advice specifically written for the CEO and to help them troubleshoot their go-to-market (GTM) issues. I was happy to see this one up here.
9. The pipeline progression chart: why I like it better than tracking rolling-four-quarter pipeline (2022). Give the CRO rolling-four-quarter sales targets and I’ll be in favor of tracking rolling-four-quarter pipeline. Meantime, we need to track it by quarter and this chart shows you how. Don’t even get me started on people who want to track annual pipeline.
10. Six tips on presenting to the board of directors (2025). A post I wrote to help executive staff make a good impression on the board by losing any prior board PTSD, making a deck from scratch (not recycling slides), cutting to the chase, taking certain things offline, and of course ATFQ.

Technically, my Best of Kellogg post also made the list, so if you’ve not checked that out lately, perhaps you should. I’ve recently revised it as I do about once a year.

I was happy to see that five of the ten top posts were from 2025, which I think hits the right balance of healthy re-use of the classics along with some endorsement of my new material. Thanks for reading.

The post The Ten Most-Read Kellblog Posts in 2025 appeared first on Kellblog.

As always, I’ll remind you of some disclaimers: (a) don’t view this as investment advice or anything resembling it, (b) while I generally avoid politics on my blog, I make an exception in these posts because I like to start macro and then zoom in, (c) remember that I do this for fun and entertainment, so please take it as such, and (d) my self-scoring for the previous year is always generous [1].

This is my twelfth annual predictions post. Let’s begin.

Review of 2025 Predictions

1. America gets what we deserve. Hit. I predicted a more brazen, more interest conflicted, and less constrained Trump — and scored a hat trick. The more interesting question today, given that Trump has increasingly diverged from his official platform (if less so from his unofficial one), is whether people are getting what they think they voted for and if they are happy with the results? Some visible MAGA types, from MTG to the QAnon Shaman, are saying no. Either way, we voted for Trump and we got him. I believe that will one day serve as an enduring reminder that character matters in leader selection.

2. The broligarchs enjoy their 15 minutes of fame. Hit. I almost scored this a miss because some of the tech bros have enjoyed more than their 15 minutes. But I’ll credit myself with the hit because Elon Musk was the poster child for this group and, with the DOGE fiasco, he exemplified what I was predicting. 

Many Silicon Valley leaders appear to have aligned themselves around four priorities: (a) a more favorable exit environment, including a revival of big-tech M&A after an FTC posture that had largely stymied it; (b) crypto, which — despite substantial effort to convince myself otherwise — I continue to view as suspect [2]; (c) a light-touch regulatory framework for AI; and (d) pro-growth energy policy, given AI’s heavy dependence on power.

I foresaw much of this thanks to this December 2023 quote from Ben Horowitz [3]:

We are non-Partisan, one issue voters: If a candidate supports an optimistic technology-enabled future, we are for them. If they want to choke off important technologies, we are against them.

Thus far, the administration has largely delivered, so the digerati may well feel vindicated, though I could do without the sane-washing [4]. In the coming year, we will learn more about precisely what they traded in this Faustian bargain.

3. The startup ecosystem purge continues. Hit. I got lucky here because I meant to say “accelerates,” but wrote “continues” — which is more indicative of what happened. In 2023, 769 startups shut down; in 2024, that figure rose to 966; and Carta’s commentary suggests the 2025 total will fall between 1,000 and 1,100. Interestingly, per SimpleClosure, startups shut down later in their lifecycle compared to 2024 [5].

But an ecosystem purge is also about M&A, not just shutdowns. Per Berkery Noyes, 2025 M&A volume was up 66% from $253B to $420B, with deal count up 8% from 1,992 to 2,152.

The point was that many startups were going to hunker down in 2025, extending cash runways to buy time until the return of a healthier exit environment. That return did not happen in 2025, so many of them will need to keep hunkering. Exit multiples were flat to down in 2025, though I am hopeful they will bounce up in 2026.

4. Attention is the new oil. Hit. This one is an accidental double entendre because of the popularization of LLMs (enabled by the transformer architecture published in the famous 2017 paper Attention Is All You Need). But my intent here was not to talk about transformers but communications, inspired by Chris Hayes’ book, The Sirens’ Call.

Anyone who’s lived with addictive, dopamine-fueled doomscrolling (i.e., anyone who’s ever used social media) knows that this is true. It’s a clickbait world, often a “rage bait” one (Oxford’s 2025 word of the year), and we have enormous trouble resisting. Click here to learn the nine ways to use clickbait tactics in your marketing strategy [6].

The point of this prediction was to say that we’re leaving the age of information and entering the age of attention. Information used to be scarce. That drove content marketing strategies and the use of gated assets to drive leads. But a few things changed:

• The algorithms have taken over social media to the point where the word “follow” is meaningless. It’s no longer enough to have a large follower count; you need to write posts that the algorithm likes or no one will see them [7].
• Thanks to AI, content generation has become free. Whereas information was once scarce, information is now plentiful and cheap. Unfortunately, it’s often AI slop.
• Thus, the challenge is no longer producing and optimizing gated assets, but instead trying to break through the noise with your point of view.
• All while the gatekeepers are changing from search engines to generative AI engines, requiring new and different optimization strategies.

There are two strategies in this environment: (a) play the game by fighting for attention in the media and on social platforms [8] and (b) build your own audience via newsletters, podcasts, blogs, and educational content. Companies should do both, subject to the constraint of not playing the game so well that you damage your credibility. Trust matters. See prediction six for 2026.

5. The worldwide web, as we knew it, is dead. Hit. The Economist called this one for me.

Here’s what they had to say:

Artificial intelligence is transforming the way that people navigate the web. As users pose their queries to chatbots rather than conventional search engines, they are given answers, rather than links to follow. The result is that “content” publishers, from news providers and online forums to reference sites such as Wikipedia, are seeing alarming drops in their traffic.

As AI changes how people browse, it is altering the economic bargain at the heart of the internet. Human traffic has long been monetized using online advertising; now that traffic is drying up. Content producers are urgently trying to find new ways to make AI companies pay them for information. If they cannot, the open web may evolve into something very different.

There’s no better example of the effects of AI front-running on web traffic than Stack Overflow (chart posted by a top-ten, all-time contributor) as you can read about in this post by Gergely Orosz.

6. Working for the algo. Hit. This was a contrarian prediction inspired by an odd fact. The Turing test asked whether a computer could convince a human that it was human. Today we’re living through the inverse: humans increasingly have to convince computers that we are human. If that sounds strange, think about CAPTCHAs — every time you solve one, that’s exactly what you’re doing.

More generally, “working for the algo” means we are working to please an algorithm as opposed to it working to please you. Every time you optimize a social media post to drive amplification. Every time you jiggle your mouse to show you are still working from home. Every time an Amazon driver pees in a bottle. Every time you two-factor authenticate to prove that you are you. Every time you change a web page for SEO or AEO. Every time you modify your spending behavior to boost your credit score. Every time you do any of these things, you are working for the algo.

7. The death of SaaS is greatly exaggerated. Partial. This prediction was playing off two things: (a) a popular rant by Satya Nadella about how SaaS apps were just UIs atop a CRUD database, and (b) the widely-distributed September, 2024 story of Klarna’s plans to replace Salesforce with in-house AI applications.

Since then, the storm around the Satya rant quieted quickly and the Klarna CEO went on record saying he was terribly embarrassed about the whole situation.

While the reality is that enterprise software platforms are very hard to extract (“you inject them into your corporate veins” as my friend Paul Wiefels likes to say) and ergo at relatively low risk from generative AI replacement, there are nevertheless seeds of truth in the “death of SaaS” argument.

It doesn’t start with ripping out Salesforce or Workday. It starts with a huge variety of new applications focused either functionally (e.g., Vic.ai in accounts payable [9]) or vertically (e.g., EvenUp in personal injury law), continues into enterprise AI application platforms such as Writer [10] and Glean, and a new generation of low-code / no-code tools for building bespoke applications from the red-hot vibe coding companies (e.g., Lovable, Replit) to new offerings from established enterprise vendors like OutSytems [11].

SaaS is sick, but it isn’t dying. Though, if you’re not actively incorporating AI into your traditional SaaS product as your top product priority, then your company might be. My most-viewed article of the year was the newsletter I wrote for Topline broadly on this topic. 

8. An unlikely revival of branding. Hit. “Brand versus demand” was the marketing watch-phrase of 2025. Brand is not only gaining relative steam in this dichotomy, but marketers would like it to gain even more.

The idea is simple: as traditional demandgen and “performance marketing” channels become more expensive and less effective, marketers increasingly want to allocate budget to more brand-oriented programs. I think this is a good idea because (a) brand and demand spend feed each other (you’re more likely to register for a webinar from a company that you know) and (b) over the past five years we, as an industry, have over-rotated to highly measurable marketing into a myopic position where boards and CEOs are now reluctant to invest unless you can “show me the money” quickly and directly. But that’s not how marketing works. It’s not how people work. And while measuring brand program effectiveness requires stepping outside the world of traditional CRM reporting, it is by no means impossible. That said, brand measurement is something of a lost art that marketers will need to relearn in 2026. (For listeners of The Metrics Brothers, Ray and I are scheduling an episode on this topic for early in the year.)

9. PR is the new SEO. Miss. While I think people would acknowledge the underlying point – that great PR hits can have a strong positive effect on answer engine inclusion — I don’t think this realization sparked the PR renaissance that I was hoping for in 2025. First, because few companies can generate such a hit regardless of their PR investment – i.e., even a great PR flack needs an articulate and charismatic founder, a product with demonstrable customer impact and momentum, and a story that maps pretty directly to mainstream business. Hypergrowth and an enormous fundraising round don’t hurt either.

More importantly, it’s because PR has changed. It’s a lot more trench warfare across dozens of bloggers, podcasters, and influencers than it is swinging for home run stories. If any spending shot up in 2025 it was on lobbying, not public relations. Though the two fields are less far apart than one may think.

But no sane marketer is going to propose a big PR budget in order to boost their AEO program; there are so many other things they can do first. So, hopefully that was thought-provoking. But a miss.

10. LinkedIn enters the social media death cycle. Partial. Followers will know I’ve been trying to manifest LinkedIn improvement because I’ve had troubles remaining on X. And this prediction was kind of a caution, an anti-manifestation (as if anyone was listening) to say if LinkedIn doesn’t get better, it might get a lot worse. But neither happened. It didn’t get better, but it didn’t really get worse. It’s still the same vanilla, namby-pamby, excessively self-promoting content as it was in 2024. But I do see an increasing number of posts from second-hand recyclers of ideas. Think: “Steve Jobs said this” or “Revolut did that ” from people who were not first-hand involved in the company or its story. Which, when they are, is the ultimate cool thing about social media. So, while I think LinkedIn has thus far avoided enshittification and the social media death cycle, it’s still a site that I’m rarely excited to visit.

So, an 8 out of 10, overall. Let’s move on to the coming year.

Predictions 2026

1. Trump, but even more so. With checks and balances significantly weakened, expect an even less constrained Trump presidency. His leadership and communication style — modeled more off a 1970s mafia Don than a democratic statesman — will likely become even more pronounced. That means more indecency, corruption, lip service to the rule of law, revisionism, power plays, and “Donroe” doctrine foreign policy. Given his age and health, Trump may view 2026 as a culminating chapter, raising the odds of bolder, riskier moves aimed at both leaving his mark and cementing his legacy.

Speaking of legacy, prediction market Kalshi currently assigns a 16% chance that Trump leaves office in 2026 [12]. Thus, the possibility of a Vance presidency is becoming more plausible — if not in 2026, then perhaps in 2027. Maybe, among other things, we’ll learn to stop electing septuagenarians.

Meantime, if you’re interested in learning how people can perceive reality so differently, try reading Good Reasonable People by Keith Payne. It’s insightful, if not particularly optimistic.

2. It’s a bubble, but this time it’s different. Make no mistake, today’s AI market has many of the hallmarks of a classic bubble (e.g., technology catalyst, euphoria, high valuations, debt accumulation). And, of course, part of the euphoria is everyone saying it isn’t a bubble (i.e., “this time is different”).

But I think there are some key differences between today’s situation and the Internet bubble that will affect how this one unwinds. Usually bubbles pop; I think this one is going to leak – i.e., the deflation will run in slow motion relative to traditional bubbles. Why? Three things: (a) this is a private market bubble where most of the players are not public companies, (b) everything happens in slow motion in VC/PE land where funds last a decade and trading (i.e., valuation determination) is infrequent, and (c) there is opacity in the private markets that can make it hard or impossible to see when the bubble is deflating.

For example, consider a native-AI company growing at 200% that recently raised money at 50x $20M in ARR, so a $1B valuation. Now, let’s say growth slows well below 100%, but they grind it for 2-3 years to double revenues. For a flat round, they’d need a 25x valuation, but with things deteriorating that’s not going to happen. So, how can you obscure a down-round if you want to? Structure. You can put a 2x liquidation preference on the new money to preserve the headline $1B valuation, but ceteris aren’t paribus. This is a down-round in a wig and lipstick. But since terms are not disclosed, the world will basically never know. And multiple liquidation preferences are just one type of structure you can use in this situation.

This can’t happen when companies are public. Thus, I believe that private bubbles end differently than public ones. They deflate, as opposed to pop. The process happens more slowly and with less hysteria. But it happens. Some will argue it’s already happened to the last generation of SaaS unicorns. In 2026, it will start to happen to many of the AI ones, including a few of the big ones.

3. IPOs are back, baby. Many folks thought 2025 would be a banner year for tech IPOs but, while they did increase over 2024, the damn didn’t break open due to tariff shock in the spring and the government shutdown in the fall.

I think 2026 will see a big uptick relative to 2025, both in the number of transactions and the deal sizes. Why? First, because there is a backlog of amazing companies waiting in line. Second, because while companies can stay private longer – sometimes indefinitely – that’s becoming the luxury of the ultra-elite firms, so all the merely elite ones will still need to go public at some point to get proper liquidity. Third, because a better exit environment is one of the top priorities Trump-supporting VCs wanted, and I believe both Sand Hill Road and Wall Street will apply significant pressure to get what they bargained for. Finally, because there is a liquidity crisis in parts of VC/PE and if the public markets can successfully absorb the elite companies, then lower-tier companies will be in a hurry to go public while the IPO window remains open. 

And that’s not to mention the potential gale-force tailwind if the administration ends up setting interest rates — at least before the subsequent sugar high would likely lead to 1970s-level inflation.

4. AI takes jobs, but not as we think. There’s no doubt that AI will replace jobs and plenty of them. The questions are (a) will they be missed and (b) will it drive folks up or out?

I don’t think anyone misses elevator operators, a job that peaked in the 1950s and all but extinct today.

The same is true of switchboard operators, pin setters, lamplighters, linotype setters, icemen, typists, shorthand secretaries, and slubber doffers. Yet, all these jobs were automated away. While I’m sure transition was rough for any given elevator operator, as a society we don’t miss them. Most coal miners work hard so their children won’t have to be coal miners.

Let’s remember where the word luddite comes from. Today, we use the word to describe people opposed to new technology. But the Luddites were part of a labor movement; they didn’t destroy machines because they were technophobes, they destroyed them to protect jobs. So, the transitions can be rough – and come with social unrest — when jobs are automated away. But we still don’t miss the jobs they fought for.

And sometimes, when we think a job is going to be automated away, we get it wrong. In 2016, the godfather of AI, Geoffrey Hinton, said that we should stop training new radiologists because AI could read images better than people. And he was right. AI has outperformed people for nearly a decade at image reading. But today we have more radiologists than ever, with an average salary of $520K. It turns out that reading images isn’t all that radiologists do.

This is an example of AI driving a job up, not out. But this doesn’t always happen, either. I watched Jensen Huang trying to apply this exact argument to Uber drivers [13]. Let’s just say that it didn’t work [14]. For some jobs, such as drivers, they just drive. Those jobs are more driven out than up.

So, are we all condemned to become unthinking LLeMmings? I’m not as negative as the “we won’t be missed” crowd (thoughtful rebuttal here). Nor do I believe that the path to AGI is through LLMs. But, with admittedly little background in the space, I am nevertheless drawn to the idea of world models, which act as state prediction machines as opposed to word prediction machines.

In short, AI will displace a lot of jobs, but many will be pushed up, not out. While the transition costs will be painful for those affected, society overall will benefit. In the end, a lot of bygone jobs won’t be missed. And I’m quite optimistic about our ability to make interesting, new ones.

5. The polymaths aren’t polymathing. Since money isn’t the flex it used to be in Silicon Valley, something was bound to replace it. Now you can’t just be well educated, you can’t just be rich. You can’t just be smart about technology. You need something more. You need to be smart about everything. That is, you need to be a polymath.

So, the first part of this prediction is simple: I think being a polymath will be the ultimate flex in Silicon Valley in 2026 and thus we’ll see an increasing number of people working to attain, and demonstrating the flex of, polymath status. If you want to join the wave, start by reading these lists of books recommended by Marc Andreessen. There’s some great stuff in there.

Don’t get me wrong. I’m all for well-rounded individuals who don’t specialize too early and end up one-dimensional as a result. Many of my friends are super interesting because, in addition to high proficiency in tech, they’re students of business, languages, philosophy, or history. This is all cool. Great stuff.

What’s less cool is when Silicon Valley leaders decide that they are smarter than everybody about everything. Sure, in the spirit of the bear joke, you might be smarter about history than the other MIT engineer, but are you smarter than the person whose lifelong passion is history? Do you know more about international relations than the person who went to SAIS and then to the state department? Do you know more about improving government efficiency than people who’ve spent their entire careers working in government?

Moreover, were the people who built the data structure behind tweets the best people to predict the societal impact of Twitter? Are the people who build GPUs or foundation models really the best people to predict the societal impact of AI? We tend to confuse the ability to build something with being in the best position to understand its impact. Oppenheimer knew how to build the bomb, but later said, quoting the Bhagavad Gita, “now I am become death, the destroyer of worlds.” If you want to know how to build a bomb, ask a physicist. If you want to know its impact, ask a general.

Finally, there’s a difference between being well rounded and being a dilettante. When the new Silicon Valley overlords – whose primary competency is making money by creating technology businesses [15] [16] – start thinking they should be in charge of government and society, blind to their own ambitions and agendas, we’re going to get more messes (e.g., DOGE), more inane statements, and more crazy interviews. The reputation of Silicon Valley will continue to suffer and eventually – probably in 2027 or 2028 – we’ll see backlash-driven policies that actually hurt the ecosystem. As bizarre as the Thiel interview was (and as hilarious its parodies), if you wanted to drum up popular support for a billionaire tax, it’s hard to find a better tool.

If they were really dialed in, the new overlords would take a lesson from the robber barons, such as Andrew Carnegie — and build libraries and write essays like The Gospel of Wealth. They’d also take fewer dubious actions and obviously self-interested policy positions.

6. Trust is the antidote. Across a web of AI-generated, algorithm-juiced content, people don’t know who they can trust anymore. What news can they trust? What companies can they believe?

Can they trust the answers to seemingly basic questions, like “how much does this hotel cost?” Ah, the mandatory resort fee. Or “how much does this ticket cost?” Ah, convenience fees or seat-reservation fees. And, with generative AI, can they even trust what they see with their own eyes? Was that video real? How about this photo?

People can’t trust the algorithms to show them content they want to read. Slide into the wrong thread and X instantly turns into a bot-driven cesspool of ad hominem attacks. LinkedIn has become a tedious sea of corporate news, thinly-veiled pitches, humblebrags, and ghost-written content. Reviews sites, despite consumer protection rules, are often gamed.

Soon, if not already, we’ll be using AI both to generate and summarize our communications. And, as Scott Brinker says in his predictions, “AI inbox gatekeepers will turn email marketing into earned media.” Ponder that for a second.

So, what’s a marketer to do? One thing. Focus on trust. Only trust will get people to open your emails. Only trust will get them to sign up for your newsletters and subscribe to your podcasts. Only trust will allow them to believe the reviews and testimonials about your product. Only trust will get them to listen to what you have to say.

Trust that you produce great content. Trust that you won’t lie to them. Trust that you won’t waste their time. Trust that you won’t sell their contact information to someone else. Trust that you won’t speedbag them with SDR calls. Trust that if someone unsubscribes, you’ll stop. Trust that your product does what the marketing says it does. Trust that customers get the outcomes you promise.

I’ve always felt that branding was simply about trust. Trust that you’ll be you [17]. Trust that you’ll look like you (visual). Trust that you’ll sound like you (voice). Trust that you’ll act like you (values, operating principles). Trust in your mission (vision).

It’s funny. The further we got into the Internet era the more we heard that people didn’t have time anymore. And that long-form content was dead. So, everything needed to be bite-sized morsels. There’s no time. Make it shorter. Make it a short video. Make it an infographic.

I always believed David Ogilvy: “long copy sells.”

You know when people don’t have time for your content? When it’s bad. And there’s so much bad content out there that we started to believe that lazy people were the problem. No, they aren’t. Bad content is the problem. And now it’s easier than ever to generate it. So, do we win the war by out-slopping our competition? Or do we try something else?

You know when people do have time for your content? When it’s good and they’re interested in the subject (you know, like when people are betting a part of their career on your software package). Ask the guys at Acquired and observe evolution of their average episode length:

• Season 1: ~45 minutes.
• Seasons 2–4: ~1 hour 15 minutes.
• Seasons 5–8: ~2 hours 30 minutes.
• Seasons 9–11: ~3 hours 30 minutes.
• Seasons 12–14: ~4 hours+

There is literally no better example that when people are interested and you produce good content, that they will make the time for it. I liked the three-hour episode on Costco so much, I listened to it twice.

So, overall, what would I do? Make good content. Make long content, get rid of artificial and outdated best practices and constraints. Take the time to tell the story you need to tell. Build your own (“first party”) audience. Get access to, and respect the rules of, private communities (e.g., The SaaS CFO, Exit5) that are increasingly forming to provide trusted spaces.

And don’t be afraid to place marketing chips on brand over demand in 2026. 

7. Fee culture changes VC. As with several of my predictions, this one is already in progress, but I do see it accelerating in 2026. The trend in VC is towards increasingly larger funds, e.g., a16z’s recent announcement of over $15B across about six funds, representing 18% of all VC dollars raised in the USA in 2025. (Read The Metamorphosis of a16z for a fascinating, if somewhat conspiratorial, take.)

While many smart people are writing about how VC is changing in terms of expected outcomes, fund performance, concentration, kingmaking (and how founders can deal with it), risk, value creation, and scalability, there’s one thing that few people are talking about: fee culture, which is enabled by these ever-increasing fund sizes.

Back in the day, using several assumptions [18], a successful VC running four funds in parallel could make around $3M/year in fee income. With that, they could live a very comfortable Silicon Valley life: a house in Atherton, a place in Tahoe, tuition at the Menlo School, and all the rest. But to get really rich – fly your PJ to your Montana ranch rich – then you needed carry. 

What’s carry? The second half of the 2 and 20 fee structure common in VC. The 2% is the annual fee on committed capital. The 20% is the carry, or cut, that VCs take after the fund returns its initial capital to investors. For example, if a $250M fund returns 3x to investors, the VCs would make $100M in carry (20% of $500M) over the fund’s roughly ten-year lifecycle [19]. Split that $100M across five general partners, and after one fund you’ve got the Montana ranch and, after another, the PJ to fly to it [20].

But 2% of a huge number is a huge number. So today, with a $5B fund, a VC can make more money off fees alone than they previously made off fees and carry [21]. By my rough math, more than double. On top of this, the carry, split across 15 GPs, would be $133M each [22].

While I’ve spent my career in and around VC-backed startups, I don’t consider myself an expert on VC, per se. But I think this new mega-fund reality changes things. Remember the Charlie Munger quote: “Show me the incentive and I’ll show you the outcome.”

While today’s mega-fund VCs are certainly incented to earn the aforementioned “substantial” amount of carry, they can live better — on fees alone — than the VCs of yore did on total compensation.

So, what do I think this means?

• People managing large amounts of money need to deploy it. You can’t raise $5B, collect $100M/year in fees, and then not invest the money.
• They will generally prefer to deploy it in bigger chunks. It’s easier to deploy $5B with $250M to $500M checks than $25M to $50M. It’s the difference between 20 investments and 200 investments. There’s a fixed cost – and a relatively high one – in pre-investment diligence and investment commitment presentations.
• Kingmaking should increase. If I’m sure, for whatever reason, that founder X will be the winner, I can load them up with money to help ensure that outcome happens. While more VC gets deployed, it ends up being a have and have-not financing environment.
• There will be a temptation to “foie gras” startups. Unlike the geese, this requires founders as willing accomplices, but if you follow the simple rule of “raise money when you can on good terms,” most will say yes. Hopefully they then won’t piss it away, which is how you get in real trouble.
• This should create a damn-the-torpedoes attitude. If you need to deploy $5B in order to make $20M/year in fee income, then I’m pretty sure you’re going to find a way to do so. Moreover, if the game becomes “tails I win big, heads I win five times bigger,” then who doesn’t want to play? Put differently, it’s not hard to be a techno-optimist when your firm is collecting $300M/year in fee income off your last $15B round of funds. Heck, I’d be one, too.
• Mega-VCs will become more investors and less business partners. I remember when Sequoia’s tagline was “the entrepreneurs behind the entrepreneurs.” Around that time, I also remember being shocked – and this is going to sound incredibly stupid – when I heard someone refer to VC as part of “financial services.” “What? VCs aren’t Wall Street types,” I thought. “They’re tech people.” Of course, I was wrong at the time, but part of me nevertheless continues to see this as “the financialization of VC,” or more aptly perhaps, “the PE-ization of VC.”

8. A growth retention apocalypse. I’ll start this one with an excerpt from The Grapes of Wrath.

Then the grapes [ripen] – we can’t make good wine. People can’t buy good wine. Rip the grapes from the vines, good grapes, rotten grapes, wasp-stung grapes. Press stems, press dirt and rot.

But there’s mildew and formic acid in the vats.

Add Sulphur and tannic acid.

The smell from the ferment is not the rich odor of wine, but the smell of decay and chemicals.

That’s the way I feel about ARR today. Press trials. Press onboarding. Press professional services. Press overages and hardware. Take anything, multiply it by 12 and then press that. The smell from the ferment is not the oh-so-sweet smell of annual recurring revenue, but that of bygone customers, tire kickers, experiments, and decay.

In other words, I see what Cassie Young calls a growth retention apocalypse. I think we should measure it with a metric that I call retention spread.

Retention spread = NRR – GRR

That is, the gap, in percentage points, between NRR and GRR [23]. When the apocalypse hits, you might see it in NRR alone, but to the extent that spectacular growth can hide heinous retention, you might not. That’s why we need to look at retention spread [24]. 

9. Context graphs storm the market. Sing with me: “on the negative-third day of Christmas, my X feed sent to me: a billion posts on so-called context graphs.”

The first post I saw was this paper by Jaya Gupta and Ashu Garg of Foundation Capital. I’ve rarely seen any enterprise idea hit harder, take off faster, and get more engagement. Over the next few days, here are some of folks who joined the conversation: Dharmesh Shah (Hubspot) Aaron Levie (Box), Nick Mehta (Gainsight emeritus), Arvind Jain (Glean), Satyen Sangani (Alation), Anshu Sharma (Skyflow), Colin Treseler (Supernormal, Radiant), Animesh Koratana (PlayerZero), Diego Lomanto (Writer CMO, who made a particularly compelling case for marketing), Kirk Marple (Graphlit), Anshul Gupta (Actively, and whose sister was a co-author of the original article), and Tony Seale (The Knowledge Graph Guy).

This is not a new academic paper from a research lab, but an article from a VC firm on what they see as a trillion dollar opportunity. Why did it resonate?

• First, they either got profoundly lucky or did a very well-coordinated launch that leveraged their considerable social network [25]. Or perhaps a bit of both.
• It hits on a sensitive issue within the enterprise: the predicted imminent death of systems of record made by AI zealots who may understand AI well, but who lack an understanding of the enterprise. Even if you believe in an upcoming wave of AI agents – as I do – the question remains: where will “the truth” live in enterprise systems?
• It raises the critical, and often forgotten, question of context. Not just the current state (which operational systems capture) and not just historical states (which data warehouses and analytic infrastructure capture), but the context for why it happened, which they individually call decision traces, whose accumulation results in a context graph.

As someone who started in operating systems and then moved into databases, I had to be dragged into caring about context. That’s not what we do here. We store stuff. We index it. We back it up. We ensure ACID transactions. We let you query it in flexible ways. But: How did it get here? Where did it come from? What does it mean?

As the Tom Lehrer lyric goes: “Once the rockets are up, who cares where they come down? ‘That’s not my department!’ says Wernher von Braun.”

But data warehouses taught me to care about technical and operational metadata. Data catalogs taught me to care about governance, collaboration, and quality metadata. Over time, I became a true believer in context around data [26]. Thirty years in business taught me to care about business metadata. Stuff that, until recently, most of us only dreamed of keeping in systems.

Ultimately, what’s exciting is that this is much more than just replacing existing systems or building value-added layers atop them. It’s about capturing a category of information – knowledge – in enterprise systems that has, for the past 5 decades, generally eluded us in all but the most specialized systems. That’s big. In the 1980s, I got to watch the industry transform by capturing data.

Now, I’m going to watch the sequel by capturing knowledge [27], not just in the sense of documents and text, but process [28]. Remember the old Lew Platt quote: “if HP knew what HP knows, we’d be three times more productive.” 

I know that context graphs hit hard over the holidays. Will we still be talking about them next Christmas or will they set a new record for hype cycle traversal? I think the former.

10. The Rule of 60 replaces the Rule of 40 for traditional SaaS. As industries mature, so do their financial metrics. 

And their multiples change. Consider this tweet: “looks like we’re paying 5x sales for software. Mr. Valentine has set the price.”

Let’s observe that 5x sales with 33% EBITDA margins means 15x EBITDA, which by PE standards is a healthy multiple for a software company. Also remember that 5x is about twice the average price/sales ratio of the S&P 500. So, software companies are still well valued; just not as well valued as they were before.

The Rule of 40 is a SaaS metric that was created to be a better predictor of a company’s enterprise value to revenue (EV/R) multiple than growth or EBITDA alone. The intent, coming off a growth-crazed period, was that the best companies should balance growth and profit. Hence the Rule of 40 was generally calculated as [29]:

Rule of 40 score = YoY revenue growth rate + FCF margin

Year later, Bessemer came along and correctly argued that a point of growth should be worth more than a point of profit, and created the unfortunately-named “Rule of X” which put a slow-varying multiple on growth relative to profit, typically around 2.3x. Years before, and less visibly, the Software Equity Group had created a 2x growth-weighted Rule of 40 which did roughly the same thing.

But today’s debate is not about growth weighting. It’s about the proper score. Increasingly you hear people, particularly in PE, say that are now pushing towards a Rule of 40 score of 60, or in their parlance, a Rule of 60.

Let’s look at a spreadsheet that compares two companies. They both start out in Year 1 at the same size with the same Rule of 40 (R40) score of 40, composed of (20%, 20%) [30]. The first company remains at (20%, 20%) while the second evolves from (20%, 20%) to (15%, 45%) over the four-year period. Let’s see what happens to valuation.

Over the four years, the first company ends up valued at $518M on a 15x FCF basis and $864M on a 5x sales basis. Since there’s a question whether either of those companies would get valued on a revenue multiple — which is usually associated with higher growth — let’s look primarily at the FCF multiples. The second company ends up worth $1.03B based on 15x FCF, whereas the first one ends up worth only about half that at $518M. Since PE investors usually see profit as “more of sure thing” (i.e., “more within management’s control”) than growth, I think the vast majority would push to become the company on the right.

And most do. Hence, why I predict – particularly in the traditional SaaS, non-native-AI part of the market – that most companies will be shooting for R40 scores of between 40 and 60 in 2026. The Rule of 40 is dead! Long live the Rule of 60! 

(That is, unless you’re a native AI startup and growing like a weed.)

Conclusion

If you’ve read all the way through, let me offer a special thank you for sticking with me. I wish everyone a happy, healthy, and above-plan 2026. I’ll conclude with a hat tip to Bob Weir of the Grateful Dead who died twelve days ago. Here’s John Mayer playing a touching Ripple, on one of Bobby’s guitars, at his life celebration in San Francisco last weekend.

Notes

[1] Also see terms and conditions as well as other disclaimers in the Kellblog FAQ.

[2] In my view, the most consistently monetizable activity in the crypto ecosystem has been the promotion and issuance of new tokens. Given that such tokens usually lack intrinsic value, these dynamics can, in practice, start to resemble Ponzi structures. Moreover, crypto assets have also been associated with a range of questionable and/or illegal use-cases, including money laundering, ransomware, online fraud and investment scams, political influence, tax evasion, and payments for illegal goods (remember how Tim Draper came across his considerable bitcoin position). The most legitimate use cases, in my view, are speculation — which may be unwise but should not be unlawful — and stablecoins for use in countries lacking reliable local currencies.

[3] At the time I remember thinking two things: (a) how unusual, and arguably unwise, it was for VC firm to declare a political position, and (b) that as soon as you declare yourself a single-issue voter – on any issue — you absolve yourself from looking at the bigger picture and the very real complexities within it. In short, it’s a cop out.

[4] The start of this video talks at length about the president’s personality which I find uncompelling because virtually all con men are personable (it’s kind of a job requirement) and there is ample data suggesting his behavior speaks otherwise. Ergo a higher bar than pleasant conversation and good listening skills should be applied.

[5] That there exists a startup focused on helping founders close startups is itself indicative of something!

[6] Please tell me I didn’t get you with this one!

[7] On LinkedIn and X, I can write posts that reach 1% of my followers if I don’t pay attention to the rules.

[8] And playing the game works differently on different platforms. LinkedIn swims upstream on many of its choices, so what works elsewhere will tend not to work on LinkedIn, and conversely.

[9] Where I sit on the board.

[10] Where Balderton is an investor.

[11] With whom I also do some work.

[12] As of the day I wrote this (1/14/26). That includes only resignation or removal. Prediction markets don’t allow bets on deaths so this market will not resolve to yes in that event.

[13] The things I do for my readers! Heretofore, I’d never watched an episode of The Joe Rogan Experience.

[14] Saying roughly that drivers, other than driving, do lots of things like … uh, uh, uh … uh, uh, uh … security? Not exactly an example we can all relate to. While billionaires often use drivers for security, for most people – if they’re even using one at all — a driver just drives.

[15] Even if those businesses are not ultimately successful or profitable

[16] In some cases, it’s creating. In others, it’s more “walking in front of a parade.” For example, Benioff, Musk, Altman – and many others — all to a greater or lesser degree walked in front of a parade. This, of course, is also a skill, both in identifying the right parade and in finding a way to get in front of it. And then, of course, building the business once you’re there.

 [17] And deciding who you want to be is a key part of the exercise. 

[18] Assumptions: $250M funds, 5 GPs, 2+20 fees, 4 funds in parallel, $3M staff and admin expense.

[19] This ignores the carry that is distributed to staff within the firm. But all math here is approximate, just to demonstrate the higher-level point.

[20] Or, more realistically, a nice fraction of a G5.

[21] Assumptions: $5B fund, 15 GPs, 2+20 fees, 4 funds in parallel, $30M staff and admin expense.

[22] Assumptions: 20% of $10B returned in excess of the $5B fund divided across 15 GPs.

[23] The naming here gets tricky due to existing conventions. If I were to call it net expansion rate, it would be too easily confused with net revenue retention (NRR); they sound too similar. What I’m trying to get at is the mechanism through which you hit your NRR. You can hit any given NRR (e.g., 100%) in several ways. For example, expansion of 50 against shrinkage of 50, or expansion of 0 against shrinkage of 0. The first example has a retention spread of 50 points (100 minus 50), the second 0 points (100 minus 100).

[24] Kyle Poyar wrote a nice report, The AI Churn Wave, on this topic. While he doesn’t explicitly look at retention spreads and while his data doesn’t show what I’d expect (i.e., higher retention spreads in AI than B2B SaaS), we do both arrive at the same conclusion: there is a wave of churn in AI startups coming.

[25] But launching something on December 22? Was this the ultimate contrarian timing, or a launch that slipped a few weeks and they thought, “Oh, just do it anyway”?

[26] I no longer tell my erstwhile favorite joke: the only thing you can make with meta-data is meta-money.

[27] Much like how AI was “a thing” decades ago, but only became real and widespread recently, so had knowledge management been a thing for decades. But it’s also never been real and mainstream. Maybe now it finally will.

[28] Similarly, I know that process mining has been around for a while and Celonis has created a ~$13B (valuation) business doing it. I view this as validation for lighter, more automated, more robust process graphs. As the William Gibson quote goes, “the future is already here, it’s just unevenly distributed.”

[29] Due to its odd name there are some tongue-tying semantics around it – e.g., “my Rule of 40 is 30” or “we are a Rule of 30 company.” To avoid this, I insert the word “score” – i.e., our Rule of 40 score is 30.

[30] I’ll use (growth, profit) as my nomenclature for communicating the elements of the R40 score. 

The post Kellblog Predictions for 2026 appeared first on Kellblog.

Around the corner, written on the light brick wall in black, permanent ink was a line that the grafitti artist foresaw as iconic: “What a long strange trip it’s been.” The Dead permeated my high school like tie-dye through the fibers of a white, cotton t-shirt.

Such was high school in the 1970s. While I’m not in the photo below, it’s the only one could easily find online, just to give you a taste. While I don’t know what happened to most of the folks in this picture, one later became principal of the high school. Another was killed in 9/11. But this was us.

Many of us became deadheads. Second only to their home state of California (847), the Dead played more shows in New York (289) than any other. Often these were in small municipal or college venues like the Glens Falls Civic Center, the Onondaga War Memorial, the Broome County Arena, or Barton Hall at Cornell, later famous for hosting what was generally deemed their single best concert.

I was first drawn to Phil because the tone of his voice was unique and I loved the high harmonies that he often sang. Then I was drawn to Jerry because of his souful voice, wistful ballads, stunning guitar leads, and well, who couldn’t be drawn to Jerry?

I didn’t know what a rhythm guitarist was. But that was Bob Weir, often simply Bobby, cofounding member but nevertheless kid brother of the band. He was only 17 when he joined, compared to Garcia at 21, and Lesh at the ripe old age of 25.

But I liked Weir’s high energy songs like Truckin’ and Sugar Magnolia, which often served as a gateway into the Dead’s music. And Sugar Magnolia contains one of my favorite Dead lyrics (“she’s a summer love in the spring, fall, and winter”) although it was penned by Robert Hunter.

A dispute with Hunter over the previous line — “she can jump like a Willys in four-wheel drive,” written by and classic Weir — permanently ended their songwriting partnership, driving Weir to collaborate with John Barlow going forward. Weir and Barlow would write some masterpieces together such as Weather Report Suite, a song so challenging to play that the band eventually stopped playing it.

But that was the Dead. A band that wrote songs that were too hard to play. The band that routinely played the most difficult thing that John Mayer ever learned. The band who built a public address system that was too expensive to transport. The band that wrote songs in 11/8 and 7/4 time signatures (The Eleven, Estimated Prophet). The band that encouraged bootlegged recordings with a dedicated “tapers” section. The band that provided medical and retirement benefits for its road crew. The band that pioneered a new category (“jam bands”), blazing a path in the 90s for Phish and today Goose.

These were people who pushed the envelope. And Bob was a big part of that. Perhaps the biggest thing Bob did in recent years was recruit talent to keep the band going. John Mayer, Oteil Burbridge, Jeff Chimenti, and eventually Jay Lane. Without Bobby attracting this talent, the whole scene might have died with Garcia in 1995.

In other posts, I’ve written about business lessons from the Dead and there are plenty. Innovation, disruption, open source, community, alternative business models, category creation, customer centricity. I won’t rehash them here. If I had to net it all out, I’d say read Brian Halligan’s book. He knows the Dead, he knows marketing, and — as a cofounder of HubSpot — he knows tech.

Those of us in community knew that, as great as it was, it couldn’t last forever. The Sphere shows were epic.

So were what turned out to be the final shows in Golden Gate Park. Particularly moving were the nightly guest appearances: Graheme Lesh (Phil’s son), Trey Anastasio, and Sturgil Simpson who knocked Morning Dew literally out of the park.

After Garcia’s death, Weir was once asked how often he thought about him. “Quite often,” Weir responded. “You know, he lives and breathes in me.”

And so it will be, Bobby, between you and us. Thanks for everything. Thanks for the long strange trip. Fare thee well.

The post Fare Thee Well, Bobby appeared first on Kellblog.

Today, I’ll do a review of Courageous Marketing by Udi Ledergor, Chief Evangelist and former CMO for over 6 years at Gong.  Let me preface this by saying I have always been a huge fan of Gong. From the first second I saw Gong, I thought, “this connects the C-suite to ground reality” and used the product at the companies I ran and recommended it to the other startups I worked with.

I always told CEOs this: “Buy Gong, get together as an e-staff, and listen to 3-5 sales calls. When you’re done listening, crawl back out from under the table, and then you can decide what you want to do about it.”  That’s what happens when you get connected to ground truth.  That’s how “cringe” your reality often is compared to your management team’s expectations.

Everyone had onboarding programs, everyone had quarterly update training, everyone had certification, but nobody knew what was actually being said on sales calls. Gong eliminated that problem.  I was fascinated to see more emergent use cases later arise like forecasting based on activity.  I was unsurprised to see the space eventually consolidate around a broader sales platform with Zoominfo buying Chorus, Clari acquiring Wingman, Gong acquiring RightBound, and Outreach acquiring Canopy, among other examples.

Throughout its history I always felt that Gong was one of a very few enterprise software companies that was not only a clear leader in its market, but also had a distinct brand and personality.  Others might include Salesforce and Splunk.

In Courageous Marketing, Udi tells you where that personality came from and how they fought to define and maintain it.  The book is organized as a series of twelve short chapters, each containing a series of related lessons.

• A Super Bowl Commercial describes the process for getting board approval, executing, and then socially promoting a 2021 Super Bowl commercial they aired regionally.  The commercial was quite good in my opinion — unlikely to win any awards for creativity from advertising groups — but clear, simple, and benefit-oriented messaging told in an interesting way.  It was a gutsy move, and it worked, but it led to a second, not-good commercial in 2022 that Udi later discusses.  Don’t let starting with a chapter on Super Bowl ads turn you off (as it initially did me).  There’s plenty of great, less rarefied stuff coming.

• The Riskiest Strategy of All, which according to Udi, is playing it safe.  He describes how Gong didn’t play it safe with either its visual identity or with its messaging.  He describes the focus and consensus problems that often result in mediocre, least-common-denominator marketing and punches it home with one of my favorite quotes:  “I’ve searched all the parks in all the cities and found no statues of committees” from GK Chesterton.  One great way to not play it safe is to speak your buyer’s language.  A lot of the corporate veiling drops off when you do that.  And you’ll sound different.

• Punch Above Your Weight.  I’ve often heard it said that marketing’s job is to “make us look bigger than we are” or, in my case, additionally to “make us not look French” (chez Business Objects).  I think every CMO needs to make their company look bigger (and, if applicable, less French) as well as somewhat further along with its vision.  As Larry Ellison once said, “sometimes I get my verb tenses mixed up,” which is fine on the about-us page, if not the product one.  Udi describes a  technique straight out of pre-stoic Ryan Holiday where you “advertise offline, amplify online,” for example, by buying a half-hour’s worth of the NASDAQ billboard in Times Square and then amplifying it via social media.  He then importantly shares some thoughts on measuring brand investments, including using Gong to do so (e.g., counting references to a podcast appearance in sales calls).

• You Can’t Own Brand, which echoes one of my favorite David Packard quotes (“marketing is too important to be left to the marketing department”) and one of my favorite Henry Ford quotes (“quality is doing it right when no one is looking”) – or its marketing equivalent from Jeff Bezos, “your brand is what people say about you when you’re not in the room.”  To the extent branding is determined not just by what you say, but by what you do, he outline Gong’s operating principles – not corporate values, mind you – but actionable principles people could follow in their day-to-day work (e.g., create raving fans).  In short, as Udi says, “the takeaway is clear:  marketing can’t succeed if brand-building is a disjointed exercise, separate from the rest of the company.”  He ends the chapter with advice straight out of Seth Godin:  don’t be boring.

• Should You Build a Category?  This chapter alone is worth the price of the book because Udi provides reasoned pushback on the Play Bigger argument that to win in Silicon Valley you must to create and dominate a category — which itself is arguably a reskinned version of Geoffrey Moore who said to create a tornado and then emerge from it as the gorilla.  (Moore mixed metaphors, but we love him nevertheless.)  In addition to the category creation challenges Udi mentions, my problem with this is that as Silicon Valley matures, more and more categories have already been created — so life is not as simple as homesteading an unoccupied piece of the market as it was in the 1990s to 2000s. Today, I tell people: if you want to create a category, go sell some software. (Which means we need to talk about how you’re going to do that, which quickly takes us back to marketing strategy.) Udi’s viewpoint is not miles away.  Though he does observe that in certain situations, classical category creation remains relevant, and Gong’s situation was one of them with Revenue Intelligence.  He outlines who they hired to do this, how long it took (3 years), the approach they used (market the category, not the product), and how they measured it.

• Would You Pay For Your Content?  This is a delightful essay on content marketing.  It introduces the 95/5 rule of B2B marketing (95% of buyers are not in-market) and ergo the need to find those few in market while nurturing the rest, and producing content that works for both audiences to avoid “pitch slapping” the vast majority who are not currently in-market.  He provides a nice differentiation between product marketing and content marketing.  He wraps up with a case study on Gong Labs, which I always thought of as a great, data-driven content factory, much in the same way I think of Peter Walker’s content today at Carta.  The difference is that Gong sells to sales and can express a totally different personality in presentation.  One early headline was, “Secret #1 – Shut The F*ck Up” in a piece that analyzed talk/listen ratios on successful sales calls.

• Creating Events Magic is a topic about which I need no convincing.  I am a huge fan of well-executed events, both large and small.  Especially now, in the post-Covid but still somewhat WFH-heavy world, people like to get out and talk to each other.  This chapter is an excerpt/rewrite of a book Udi published in 2015, The 50 Secrets of Trade Show Success.  It’s quite tactical, but it’s good.  Tradeshows are all about tactics.

• When Things Go Wrong discusses how to handle things when some of your bold experiments backfire, like the example he presents where – and this is somewhat unbelievable – they tried to leverage the murder of George Floyd by making donations to the NAACP in return for G2 reviews.  While there may be no statues of committees in parks, no committee in a zillion years would have approved this campaign.  He discusses the fast, direct approach he took to dig out from this mistake.  Then he discusses the second, unsuccessful Super Bowl commercial.  There are a few good lessons here, but IMHO he misses the biggest one:  make sure your CEO understands that you’re taking risks and once in a while they’re going to blow up on you.  Put differently:  if you want fewer mistakes, I can take smaller risks, but that might also reduce sales.  Get some buy-in on your chosen risk profile before the shit hits the fan.  You might need it. 

• Chart Your Own Path is a chapter on career that encourages you to carve out roles that fit your strengths, work at startups that have already achieved product-market fit (PMF), and to pick the right company at which to work.  The right company not only has established PMF, but has a CEO whose vision for marketing aligns with yours and your styles work well together.  We all know a perfectly good marketer who suffered because they joined a company that didn’t pass one or more of these tests.

• Lay The Foundation For Greatness emphasizes the importance of having a high-level marketing strategy that is aligned with company goals so people can understand not only the details of your plan but the underlying logic behind it.  Understanding both is key to driving commitment. He also emphasizes an idea that I heard almost verbatim from one of my bosses when I was a CMO:  wear two hats.  Or, as it was put to me:  “you have two jobs – one is to run the marketing department and the other is to help me run the company.”  The natural consequence is that you must build a strong team beneath you, so that you have time for your second job.  Too many CMOs fail because they never get beyond the day job, and that is usually a result of a weak team or insufficient resources.  If your CEO tells you, “you have two jobs,” then make sure they’ve given you the resources to do them both.  One of my rare disagreements with Udi is at the end of this chapter where he advocates for executives taking positions on social and global issues.  I think that’s a slippery slope and a mistake and, as Udi foretells, I’ll be someone who respectfully disagrees with him on that viewpoint.  My quip on the general issue of enterprise software companies taking official positions on social and global issues is: “Sir, this is an Arby’s.”

• Building a Courageous Team shares Udi’s views on teamwork, including his take on when to hire for potential over experience, sequencing how you build a marketing team as a company scales, and the culture that drives great teamwork.  He shares three of their operating principles:  foster of a culture of healthy risk-taking (a central thesis of the book), stay involved without micromanaging (easier said than done), and keep it simple.  I’ll take his third principle one step further:  I think it’s marketing’s job to impose simplicity on a complex and chaotic world.

• You’re Half of a Two-Headed Dragon recognizes that reality that sales and marketing are partners in revenue generation.  My favorite metaphors are “we’re running a three-legged race” and, more colorfully, “the CRO and CMO are lashed together as a human battering ram.”  If Udi likes dragons, so be it. He repeats his belief that marketing exists to make sales easier (amen) and shares five principles of sales and marketing alignment.

The book ends, fittingly, with a list of tips from CMOs on how to do more courageous marketing.

While you shouldn’t judge a marketing book by its cover, you can judge it by its marketing. And Udi has done an impressive job here. The back cover quotes come from a high-firepower list including Daniel Pink, Robert Cialdini, Nir Eyal, Neil Patel, Carilu Deitrich, and Kyle Lacy.  The forward is written by Sam Jacobs of Pavilion.  The interior quotes include Trisha Gellman CMO at Box, Dave Gerhardt from CMO at Drift and founder of Exit 5, Dave Kellogg (I served as an advance reviewer and provided a quote), Jon Miller cofounder of Marketo and Engagio, Andrew Davies CMO of Paddle, and Anthony Kennada former Gainsight CMO and founder of Goldenhour. 

The book was published in April to some great coverage. I’ve recently noticed Udi doing some double-dip marketing on social media. Those posts provided me with enough energy to complete my long-overdue review.

Courageous Marketing is a quick and uplifting read.  I’d knock it off on an upcoming airplane trip to get your marketing juices flowing. It could also be the perfect stocking stuffer for the marketer in your life.

The post A Review of Courageous Marketing by Udi Ledergor appeared first on Kellblog.

Musical theater offers a surprisingly useful lesson for B2B marketers: the “I want”song. Nearly every musical features an “I want” song third or fourth in the score, after we’ve been introduced to the characters but before we understand their motivations. That’s when the protagonist steps forward and articulates what they want, often in the language of longing. It is a structural requirement: the audience cannot understand the stakes of the story unless they first understand the desire that animates its hero.

Hercules wants to find where he belongs. Hamilton wants his shot. Ariel wants to be part of another world. Mulan (like Rumi) wants to be accepted as her true self. Elder Price wants to do something incredible. The details vary, but the mechanism does not: the story cannot begin until the protagonist tells us what they want.

This turns out to be a useful metaphor for B2B marketing. Every buyer has an “I want” song: usually unspoken, often half-formed, but always present. Yet most messaging fails to reveal it. Instead, we default to talking about our own technology, our architecture, our features, our AI, our category. We sing our song. When we should be singing theirs.

I like devices that force a frame switch, and thinking about “I want” songs does exactly that. They push us out of vendor-centric thinking and into a more empathic posture: what does the buyer wish were true, not only organizationally but personally? To make the idea concrete, I went so far as to draft a sample FP&A “I want” song to the tune of Go the Distance, which worked quite well as a template. The exercise also forced me into short, simple phrasing — an unexpected but useful reminder to use plain language.

That detour was fun, but let’s return to the main argument.

The reason the “I want” structure works as a messaging framework is that its underlying components — the ache, the aspiration, and the bridge — map surprisingly well onto the way buyers perceive their own situation.

First comes the ache: the protagonist’s sense that something is missing. Ariel feels trapped; Moana feels pulled toward something beyond the reef; Hercules feels out of place. In a business context, the ache is rarely “we need AI-driven orchestration.” It is more grounded and more personal:

• I want to stop feeling constantly behind.
• I want the board to stop grilling me every month.
• I want managers to believe in the plan rather than treat it as something imposed on them.
• I want my team to get home at a reasonable hour.
• I want to actually do analysis instead of wrestling with systems.

These are the underlying motivations that drive buyers. As I’ve said before, marketers tend to remember the business benefits but forget the personal ones –i.e., the “kiss” in the benefits stack. Writing an “I want” song forces us to reinsert the personal dimension.

Next is the aspiration — the imagined better world. Hercules imagines belonging; Mulan imagines authenticity without alienation; Elder Price imagines extraordinary accomplishment. In FP&A terms, aspirations could include:

• A plan that people believe in and are committed to
• A model that can be updated without triggering a cascade of breakage.
• A planning cycle that doesn’t consume nights and weekends.
• A finance team that is viewed as a strategic partner rather than a reporting function.

These aspirations, importantly, should be described in terms the buyer actually uses, not in vendor jargon.

Finally comes the bridge — the mechanism that makes the aspiration feel reachable. In a musical, this is the moment when the hero decides to act. In marketing, this is where the product finally enters, not as the hero but as the tool that enables the hero’s journey. If the ache is “I’m stuck in Excel hell” and the aspiration is “I want a planning process that people trust and that lets me get home for dinner,” then the bridge might be: This system will take me from the chaos I live with today to a world in which the plan is credible, the board is confident, and I’m not working every weekend.

In this framing, the buyer is the protagonist and you are the mentor, guide, or map. The best narratives work this way. The worst invert the roles.

Unfortunately, much of modern B2B messaging still sings the wrong song. “We’re an AI-enabled platform delivering real-time insights at scale” is an “I am” song. The buyer does not care who you are until they understand why it matters. A better start would acknowledge the ache, gesture toward the aspiration, and only then offer the bridge: “FP&A teams spend 30% of their week pulling data instead of analyzing the business. Our platform gives them that time back.” That’s the beginning of an “I want” song: I want to put the A back in FP&A.

Companies that successfully reframe markets often do this instinctively. Snowflake didn’t lead with “cloud data warehousing”; they led with, “I want my data to be usable.” Figma didn’t lead with “multiplayer design”; they led with, “I want design to move at product speed.” Datadog didn’t lead with “observability”; they led with, “I want to see everything before it breaks.” These are buyer “I want” statements, whether or not the companies described them that way.

Narrative, messaging, and positioning are distinct disciplines, but they share a foundational principle: the buyer is the protagonist. Your first task is to understand the buyer’s “I want,” and your second is to articulate it more clearly than they can themselves.

Try this simple test: read your homepage aloud and ask whether the buyer can hear themselves as the one singing it. If the answer is no, you are singing your song, not theirs.

You know you’re on the right track when buyers start reacting not just to your product, but to your understanding of their situation. They nod before you ever show a screenshot. They finish your sentences. They repeat your messaging back to you in their own words, often with a slight sense of relief that someone has articulated the problem. They circulate your deck internally not because it describes your offering, but because it describes their goals. They say things like, “This is exactly what we’ve been trying to do,” or — my personal favorite thing to hear — “It sounds like you’ve been in our meetings for the last three years.”

Let’s net this out: if this were a musical and your buyer was the protagonist, what “I want” song would they sing?

Figure that out and you’ll build some powerful messaging. But don’t be like me and actually try to pair those lyrics to a song — though I have to admit it is fun.

The post Your Buyer Has an “I Want” Song — Does Your Messaging? appeared first on Kellblog.

Since this was written for the launch of a new publication, I made it somewhat more sensationalist than usual. It’s also shorter and tighter, without the usual deep-drives and asides.

The reception was not without controversy, in part because I touched the third rail by mentioning 996. Some hastily took that to mean, “some VC is telling portfolio companies to grind 996.” That I’m not a VC and never told everyone to blindly grind 996 seemed beside the point.

What I said was: if you’re in a winner-take-all market, then you need to win. Grinding 996 might be a part of that. But the point isn’t to work hard, it’s to win. You can grind yourself to death at a strategically doomed company and it won’t change much. (I’ve tried that. AMA.)

Here’s the article. Thanks to Sam Jacobs and Asad Zaman for inviting me to write it:

AI has created an era of haves and have-nots.  AI-native companies with spectacular growth rates are grabbing all the attention, talent and money.  Is this insanity?  How long will it last?  If you’re not among the ranks of the AI-native high flyers, how do you avoid becoming seen as a zombie, a living-dead SaaS company with uninteresting growth, little profit, and no future?  

First, it’s important to understand the external environment.  While the world may seem insane, it’s not.  We are at the start of a major disruptive cycle on the order of client/server computing or the Internet.  Such cycles come maybe every 20 years in my experience, just long enough for us to have forgotten what they feel like.  

These technology disruptions create opportunities to build enormously valuable companies that will lead their markets for a generation.  This is the system at work.  It’s chaotic.  It’s inefficient.  It feels crazy when you’re in it.  But always remember that from the wreckage of Webvan, Pets.com, and a hundred other dot-coms, sprung Amazon, Google, and Salesforce.  Nobody said creative destruction came without casualties.  

These cycles reflect the nature of venture capital.  While fixer-upper private equity (PE) has always been about driving modest growth with ever-increasing EBITDA margins, venture capital (VC) has always been a hits business.  I remember nearly a decade ago reading the prospectus of a top-tier fund which said that the internal rate of return (IRR) of their previous fund was 36%, but that dropped to 12% with the top two investments omitted.  Most of what makes VC a great investment, worth the 10-12 year illiquidity, comes from a handful of fund-returning companies.  While consistent base hits are the PE business model, the VC model is not just about home runs, but grand slams.

Viewed in this light, today’s ARR multiples seem much less insane.  After all, if a company is going to be worth $20B at exit, it doesn’t matter much if you bought at a valuation of $50M or $80M.  This is what drives the valuation insensitivity and fear-of-missing-out (FOMO) that we see today in AI.  Moreover, if you remember that in greenfield platform markets, first place ends up worth 10-100x second place, and second 10-100x third, you should be willing to pay almost anything to get into the leader.  And if you’re currently in second place, you should be willing to spend almost anything to get into first.  Second prize really is a set of steak knives.

While some will question the durability of high-growth AI revenue, to many investors it’s surprisingly unimportant.  Yes, a lot of the $100M in revenues (that a company hit in 18 months) may not recur, but 70% of something is worth a lot more than 100% of nothing.  Thus, we are seeing a surprising lack of interest in traditional SaaS metrics and the very notion of ARR — particularly the recurring part — is starting to lose meaning.  Increasingly, companies are just talking about revenue or product revenue because today’s pricing models (e.g., consumption, outcomes) no longer align to subscriptions and traditional SaaS metrics.  

While we can’t help wondering how long this will last, that’s the wrong question. It will last until it doesn’t.  Shorting bubbles is a dangerous business because the market can stay irrational longer than you can stay liquid.  Eventually, some trigger will start an unwind cycle. And once again, we will learn that this time wasn’t different from all the times before.

If you’re an AI-native growth company, the strategy is simple:  win.  Take no prisoners.  Grind 996.  Grow faster than your competitors, blunt all attempts to overtake you.  In the words of Larry Ellison, it’s not enough that you win, all others must lose.  Hire people who are so aggressive they make you uncomfortable.  Think:  “you want me on the wall, you need me on that wall.”

But what if you’re not?  Per Jason Lemkin et al., you probably can’t raise new money.  Even T2D3 (triple, triple, double, double, double) — a growth trajectory that takes you from $0 to $100M in seven to nine years — is no longer interesting to 80% of VCs.  Instead of T2D3, we hear of Q2T3 (quadruple, quadruple, triple, triple, triple).  We now measure time to $100M in ARR in months, not years.  And, by the way, do it with a tiny team, driving ARR/head of $1M+.

That the bar has been raised so high is a mixed blessing because now there’s no kidding yourself.  There’s no pitching a cloud story while still selling on-premises.  The bluff factor has been eliminated.  If you want to raise money at an AI valuation, then you don’t just need an AI story, you need an AI growth rate to match it.  Clear and simple, but far from easy.

If 80% of VCs aren’t interested in talking to you, how might you win over the other 20%?

Hunkering down is not good enough.  Particularly if hunker means something like 10% growth and 5% EBITDA at $30M in ARR.  Financially, that business might be worth 10-20x FCF, so $15M to $30M.  That’s not bad if you’re bootstrapped and you’re a founder who owns 100% of the company.  But, even then, that works only if there is confidence that the $1.5M in annual EBITDA will continue.  That is, that you won’t be disrupted by AI natives who vibe code your replacement app over the weekend.  However, if the same business raised $50M in VC then it’s effectively worthless, because the entire business is worth less than preference stack.

So how do you create value?  One word:  growth.  Growth is what takes you from an EBITDA-based multiple to a revenue-based multiple.  Mathematically, a point of growth is worth about 2.3x a point of profit.  One way or another you have to figure out growth. 

But how?

1. Make growth at positive FCF the top financial goal.  Note that this is not a strategy, but a constraint.
2. Build an AI story. Do an inside round, raise debt, or even cut traditional R&D if you need to, but you have to find money to build an AI product and story.  If you get it right, it will not only enable current sales but increase your value at exit.
3. Be relentless in sales model optimization.  You are fighting for your corporate life.  This isn’t about arguing with the board about how much to invest in growth.  You are highly constrained, but let those constraints drive creativity.  Do market research.  Do win/loss analysis.  Get good at listening. Figure out what you can do to improve sales productivity.  Often, that will be doubling down on a key segment.  Or stopping in an unproductive segment.  Or changing key assumptions in your sales model (e.g., SC to AE ratio, AE hiring/cost profile) that might have been heretical to consider in the past.
4. Consolidate the space.  Investors who have “no money” for operational experiments often do have money for new strategies.  If you’re competing with the usual suspects in every deal and everyone is struggling, then consolidate the space.  It should increase both win rates and prices.  
5. Fresh eyes.  You might think you’ve tried everything already over the past few years.  But have you?  And if you tried something and it didn’t work, was that because it was a bad idea or because you didn’t execute it well?  Beware false knowledge that blinds you to solutions.  Or bring in fresh eyes to challenge your assumptions.  Yes, it’s not going to be easy, and yes you’ve tried a lot already, but you need to look at things with fresh eyes to find fresh solutions.

In a world of haves and have-nots, you want to be a have. And the key to doing that, no matter how many times you’ve tried before, is to figure out growth.

The post The Era of Haves and Have-Nots appeared first on Kellblog.

You don’t need to look far to find signs of a problem:

• SaaS stocks, as measured by Bessemer’s Emerging Cloud Index are down 3.4% year to date.
• Customer acquisition efficiency is down.  Earlier this year, median CAC payback periods hit 57 months, implying a staggering almost five years to recoup the cost of acquiring a dollar of net-new ARR.
• Pipeline coverage ratios are running below their required targets.  The top reason for missing sales targets is insufficient pipeline coverage and Cloud Ratings shows stated coverage of 3.6x vs. target coverage of 4.1x.  (I can hear the cries of CROs everywhere saying, “please, just give me more at-bats!”)
• Articles about the web traffic crisis are ubiquitous, from Rand Fishkin’s must-read posts on zero-click marketing to CJ Gustafson swimming outside his normal lane with a post entitled Google Zero.  The web is transitioning into a series of walled gardens and what’s left over is increasingly front-run both by Google search and, of course, answer engines such as ChatGPT, Perplexity, Claude, and Gemini.
• Earlier this year, Andrew Chen put it bluntly:  Every Marketing Channel Sucks Right Now.

Add it all up and you can summarize this rather grim picture — as the Exit Five newsletter recently did — with Nothing Works Anymore.

I see this every day in my work with dozens of SaaS companies.  Because many companies are missing bookings targets by roughly the same percentage as they are missing pipeline coverage targets, I believe this is a pipeline crisis, and not a conversion rate crisis.

The struggle is real.  If you’re facing it, you are not alone.

Against this cacophony we hear a lot of talk about “brand vs. demand.”  The argument being that since demand generation programs are working less effectively, marketers should increasingly allocate dollars to brand programs.  It’s not a bad argument — in part because I believe that marketers over-rotated to highly measurable marketing during the go-go days — and thus a swing back to less directly measurable marketing is a good idea. 

(Aside:  I’d argue that marketers didn’t over-rotate on their own.  They got an assist from CEOs and CFOs who were only too eager to invest exclusively in marketing programs that delivered a clear short-term return and ignore the underlying complexity in B2B sales, effectively living-the-lie that is marketing attribution.  We don’t sell toothbrushes here, people.  Nobody goes to a tradeshow and buys a $250K enterprise solution — or even a $25K one — based on one interaction with one person.  But I digress.)

The question, of course, is what to do about it?

What Others Are Saying

A lot of smart people are weighing in, so I thought I’d provide a few links before sharing my own take.

• Kyle Poyar wrote a great post called The 2025 State of B2B GTM Report.  (Subtitled “What’s Working in GTM?  Anything!?”)  My favorite part is the GTM Scorecard, a quadrant that maps channels by popularity and likely impact.  The underlying report is full of good ideas, GTM tool recommendations, and survey data.
• The aforementioned Exit Five post, despite its title, is actually about what is working with answers derived from an informal poll of community members.
• Scale recently published a State of GTM AI report which provides survey data on AI within GTM, focused largely on high-level use-cases and a two-phase adoption model.  (Jadedly, if we’re going to do less effective work, then let’s at least do it more efficiently.)
• If your issues are more strategic, such as identifying and targeting sub-verticals, then you should read my friend Ian Howell’s book, Smart Conversations.

What Would Dave Do?

I’m going to build upon a popular comment I made on Kyle’s CAC payback period post.  Consider this a sister post to What To Do When You Need Pipeline in a Hurry, but this time not focused on the hurry, but on today’s environment.

Here’s what I would do:

• Think holistically.  You might only be the CMO, but you need to look across all pipeline sources.  The job is to start quarters with sufficient coverage and notably not just to hit marketing pipegen goals.  If outbound is working, reallocate money to it.  If AEs can generate more pipeline (e.g., formal targets, more direct routing of inbound), then do it.
• ABM.  Substitute across-the-board campaigns with targeted outreach on key accounts, leveraging both marketing and human channels (e.g., SDRs), both digital and dimensional assets (i.e., physical things like branded Moleskines), and intimate live events.  As an old CRO friend says, “if by ABM you mean us picking our customers as opposed to them picking us, then I am in favor.”
• Events.  People are tired of working from home all day and champing at the bit to get out and press the flesh.  This includes major tradeshows, annual user conferences,  and roadshows all the way down to field-marketing dinners and sporting event boxes.
• Get good at AEO.  It’s quickly replacing and more effective than search.  It’s also more winner-take-all.  There is plenty of content out there on how to do it and agencies eager to help.  Read these two articles for starters.
• Leverage the CEO via social media (e.g., LinkedIn), podcast appearances, and speeches.  And LinkedIn doesn’t just mean a few posts, it means an overall strategy.
• Use your AI message to put butts in seats.  We’re still in the stage where people are confused about AI and nothing puts butts in seats like confusion.  Do educational webinars, videos, and content.  Educate people but be sure to do it en masse.
• Leverage AI tools and workflows.  Review Kyle’s report, particularly the part on the GTM tech stack.  Read Paul Stansik’s practical posts on AI, including how to avoid slop.
• Build first-party audiences.  If you can no longer pay a reasonable amount to reach other people’s audiences, then you’re going to need to build your own.  While this is a slow burn, over time you’ll be happy you did it.  Build a Substack, a YouTube channel, a quality newsletter, or a podcast.
• Leverage partners.  They can account for 20-30% of your pipeline and usually bring opportunities that close faster and with a higher conversion rate.  If you have a partner program, leverage it.  If you don’t, start building one.  It’s another slow burn, but you’ll be happy you did it.
• Check your nurture tracks.  Long-term nurture is easily forgotten.  Measure recycled leads.  Report on your tracks.  Ensure you’ve built specific tracks for competitive loss and bad timing.  A/B test them, the flows, and the content.
• Understand why you lose.  While I believe most companies have a coverage problem, not a conversion problem, I like to win anyway and if your conversion rates are below 20-25% you need to understand why.  Do quantitative win/loss via CRM reporting, listen to call recordings, and do win/loss interviews to understand what’s really going on.
• Invest in customer success.  While I know this doesn’t help with pipeline coverage (except for expansion), always remember that the cost to backfill churn is CAC-ratio * lost-ARR.  Thus, if your CAC ratio is 2.0 and you lose $2M in ARR, it’s going to cost $4M to backfill it. The easiest – and most cost-effective — way to keep the ARR bucket rising is to limit leakage.
• Join a community.  In times of change it’s important to have colleagues you can talk to, so I’d not only keep in close touch with existing peers, but join a marketing community like Exit Five to engage in shop talk.

The post How To Navigate the Pipeline Crisis appeared first on Kellblog.

Unfortunately, I think “do no harm” actually sets too high a bar.

To help startups succeed, boards need to challenge leadership teams, ask hard questions, and get them to consider new ideas and approaches. While I think boards should refrain from giving directive feedback, there is always the chance that a hard question leads the company down a path that ultimately proves unproductive. For example, if a board member asks if a company to consider a PLG motion for a new product, that could lead to the company launching a new sales motion that ultimately fails.

This example, by the way, shows both why boards should not give directive feedback (i.e., “do a PLG motion”) and why founders should not listen to them when they do. Think: yes, we’ll consider that, but only try it if we think it’s a good idea. Throwing a bone to board members by agreeing to try ideas you don’t believe in is a losing strategy. If they fail, you are more likely to get scorn for poor execution than credit for the openness in having tried. When results are the only thing that matter, only place your bets on things you think will deliver results. (And yes, the possibility that you threw good execution at a bad idea seems conveniently never to be in consideration.)

If “do no harm” sets too high a bar, then what oath might we use? After talking to my friend, I think I found a great alternative: do no demotivation. “I don’t want executive teams leaving board meetings feeling demotivated,” he said. And he was absolutely correct.

How do we want people to feel at the end of a board meeting?

• We want the board to feel like they attended a well-run meeting, had a chance to help the company, and understand the plan to address current challenges going forward
• We want the management team to feel like the board is knowledgeable, helpful, and supportive
• And we want the management team to feel energized to go execute the plan

That’s it. If you get those three things, you had a successful board meeting. And demotivation is nowhere on that list. Demotivation doesn’t help anyone.

• It doesn’t improve the odds of executing the plan successfully
• It definitionally doesn’t make anyone feel good
• It does make the e-staff start to question the CEO and each other
• It does make people wonder why they’re grinding so hard
• It does make the team feel unappreciated and potentially vulnerable

So I’d propose Do No Demotivation as the Hippocratic Oath for startup boards.

I’ll finish this post by listing some common ways that boards demotivate executive teams (and feel free to put more examples of your own in the comments):

• Expressing surprise over things they should have known.
• Asking trap questions: “do you think our sales productivity is substandard or very substandard?”
• Placing blame: “clearly, since our CAC payback is so long, we have an inefficient sales organization.” (Maybe we do. Or maybe we have a hard-to-sell product. Or weak gross margins. Or something else. The high CPP is a fact. The reason for it is not always a bad sales team.)
• Cherry-picking: taking top decile benchmarks, or public comps, or even just top quartile numbers but across 4 different metrics. It’s like comparing your child to the best mathematician, athlete, musician, and writer in the school. (It’s quite rare when one person is all those things.) Or, my favorite: benchmarking without regard for situation. Yes, our CAC ratio is high, but 75% of our deals are dogfights against a price-slashing competitor. And yes, I know what “sell value” means, thanks.
• Expressing anger in pretty much any form. While I’ve seen some howlers, fights in board meetings are not OK. They demotivate everyone. And they take focus off the top busness priorities.
• Ratholing, failing to take things to an offline meeting or working group. OK, I do this one from time to time. (“But I promise it will be quick.”)
• Making easy things hard. When in doubt, if a topic is not strategic, just do things the standard way at the good-enough level.
• Expressing negative or hopeless sentiments: “at this course and speed, I’m not sure we’re creating any value.” As opposed to: “we need a new plan that creates value and that means we need to find a way to accelerate growth.”

So before you attend your next board meeting remind yourself to do no demotivation. It’s the new Hippocratic Oath of startup boards.

The post The Startup Board’s Hippocratic Oath appeared first on Kellblog.

Topic covered include:

• Defining outcomes
• Contrasting outcomes vs. usage
• The outcomes stack and intermediate vs. end outcomes
• How a dating site would price based on outcomes vs. subscriptions
• The basic trade-offs in selling subscriptions vs. outcomes
• How to capture value created and share it between the vendor and customer
• How selling outcomes can (radically) expand the total available market (TAM)
• Jevon’s Paradox and what happens when we make things radically cheaper
• Selling virtual humans vs. jobs-to-be-done
• A long list of links to references for additional reading

You can download a PDF of the slides here. You should be able to see a recording of the session here. (Frankly, I’m not 100% sure that link will work, but you can try.) And I’ve embedded the slides below.

The post Slides From My SaaS Metrics Palooza 2025 Session on Selling Work vs. Selling Software appeared first on Kellblog.

The session was based on an article Dan and I wrote, by the same title, which was published on the Balderton site last month and about which I wrote here. The purpose of this post is to share the slides from that webinar which are available here and embedded below.

Thank you to everyone who attended the session and who asked questions in advance or in the chat. I’m sorry that we didn’t have the time to answer each question, but if you drop one into the comments below, I’ll do my best to answer it here and/or ask Dan or Thor to weigh in as well. I’m not aware if Balderton is going to make a video of the session available, but if they do I’ll revise this post and put a link here.

The post Slides from Balderton Webinar on Aligning Product and GTM Using Customer Value Metrics appeared first on Kellblog.

It’s not news that this is a trend across all entry-level work, though I just found a new paper on the topic by three people at Stanford who examined ADP payroll data as the basis for their analysis: Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. And another one that analyzes resume and job posting data: Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data.

But in today’s post, we’re not going to look globally at the topic — no matter how interesting it is — but instead look specifically at just one question: if all the SDRs are AI agents, then where are we going to get sellers from?

I should also explain that I have a dog in the fight. My son Brian just graduated from NYU and started this summer as an SDR at Ramp. (If you’re a US-based company with 150+ employees and interested in spend management, please let me know and I can connect you.) I recommended that he take the job because it’s an amazing company, they have built an excellent sales machine (and the early-career learning on how to do things right is invaluable), and he definitely has both the raw material and the mettle to be successful in technology sales. But as I made the recommendation, I couldn’t help but wonder if he’d be in the final cohort of human SDRs.

My question actually has two parts, so let’s take them one at a time: (i) an assumption that SDRs will be replaced with AI agents, and (ii) the realization that doing so would seriously interrupt the sales career development pipeline.

Will All SDRs Be Agents?

I think the answer here is no, though I do think a good number of them will be. One easy division is inbound vs. outbound. Inbound SDRs primarily qualify and route people with intent (“hand raisers”) to sellers for a discovery and qualification meeting. Input: MQLs. Output: stage-1 opportunities. Outbound SDRs focus on some set of target accounts and work them via outreach sequences in order to get them to take a meeting. Input: contacts. Output: stage-1 opportunities. While they might also receive MQLs from their target accounts, they start higher in the funnel and are more responsible for developing interest in a meeting than someone who downloaded an asset, like it, and wants to speak to a seller.

I believe inbound SDRs provide less value than outbound SDRs and their job is more automatable. Ergo, I think inbound SDRs will be quickly replaced by AI or superannuated by targeted, hybrid inbound/outbound models (i.e., my job is to get into Citibank and I’ll take all the names, leads, and MQLs we have and leverage them to get meetings within the account).

I think outbound SDRs are here to stay. And Ramp, for what it’s worth, seems to agree. I know they’ve onboarded another cohort since Brian’s and they seem to believe that their SDR model works quite well for them. So if the old career path was inbound-SDR into outbound-SDR, I think the new one will start with a hybrid. You’re just an SDR and your job is to get meetings within some target. Sometimes you’ll have a lot of inbound interest to work with, sometimes you won’t.

The first-principles argument here is simple. When automated outreach sequences are table-stakes that every firm can easily do, the only way to break through the AI-generated and AI-automated noise will be via some combination of people/execution, message, and air support [2]. That’s why we’ll still need SDRs — and good ones — in the future.

Where Will We Find Tomorrow’s Sellers?

Since I believe there will be SDRs in the future, I think we’ll find our future sellers there. But in case that’s wrong, let’s examine where we might find them additionally or instead. I’m old enough to remember life before SDRs. So where did we find salesreps back then and where might we find them in the future?

• Junior sales roles. You’d work your up from smaller companies to bigger ones and from managing smaller accounts to bigger ones. This should still work.
• Sales training programs. Some companies were famous for their sales training programs, like Xerox or IBM. I’d differentiate those who emphasized entry-level sales training from those who hired sellers with some experience and who emphasized sales onboarding in a particular message or methodology (e.g., Salesforce, PTC). In the future, large companies who find themselves with a talent gap may need to create such programs, substituting Darwinian survival in the SDR ranks for a formal, and presumably demanding, training program. Once established, these companies will be targets for everyone else’s recruiting.
• Sales consultants. A difficult path but those who survive the transition are often your best sellers. Everytime I hear an SC complain about salesrep compensation, I say the same thing: “quotas are available.” Go grab one and see how you do. (Or don’t and stop complaining,)
• Customer success. I think this is an under-developed career path and hopefully, as CS gets more business-oriented and account-management-focused, that customer success will be more of a stepping stone into sales. Think: I developed my prospecting muscle as an SDR, I developed my closing and account management muscles as a CSM, and now I’m ready to be a salesrep.

As the SDR ranks shrink due to the pressure brought by AI, companies will have to be more creative about where they find their salespeople. Some will certainly walk up the SDR path. Others, the junior sales path. Some, the top sales training path. But I don’t believe there will be a shortage of sellers in the future. Just a shortage of good ones, as there is today.

# # #

Notes

[1] Turns out that while both “whence” and “from whence” can be considered correct, technically standalone whence is still better in my humble opinion because whence means “from where” so “from whence” is, well, redundant.

[2] In the form of marketing, awareness, reputation, brand, etc.

The post Whence Will Come Tomorrow’s Sellers? appeared first on Kellblog.

There are four common questions around ICPs. Here they are, along with my answers:

• Is the ICP about a person or a firm? Both. It should include firmographic as well as role (or persona) information. Example: VPs of sales at technology companies between $500M and $2B in revenues. Here, we included the size and industry of the company along with the target buyer’s title.
• Should an ICP include a problem to be solved? Yes. VPs of sales have lots of different problems from recruiting to training to pipeline management to forecasting, just to name a few. Thus, your ICP should include the ideal person at the ideal company and the problem you’re looking to solve for them.
• Should the ICP include adjacent systems? Yes. Deciding at the outset if you want to focus on customers using specific, adjacent systems is often critical (e.g., NetSuite vs. Oracle vs. Xero for core financials, Salesforce vs. HubSpot for CRM). The alternative is drowning in integration work while never having the time to support the idiosyncrasies of a given package which, when you do it, is usually adored by customers.
• Should an ICP include sales qualification criteria? No. The ICP is about the buyer: this is the person we’re looking for. They have this job at this kind of company. Whether they’re out shopping right now, whether they have budget, whether they have a buying timeframe and purchasing authority are all important qualification questions, but they are not part of the ICP itself. People differ on this, I know [1].

Because the world is imperfect and it’s difficult to find “Mr. or Ms. Right” every time, it’s useful to think of the ICP as a bullseye. The absolute perfect customer is in ring 0, the next level off in ring 1, after that ring 2, et cetera. Note that I have no religion about the things you vary across the rings, but the usual candidates are: job title, industry, size, adjacent systems, and problem (aka use-case). And you might do them in unusual combinations. For example, if you think a director of finance with a budgeting problem is about as good as a manager of finance at a bigger company with an operational reporting problem, then you can put them both in ring 2.

The idea is to give you a simple and flexible model to agree on who to target and who to prioritize across sales, marketing, and product.

For a zero-to-one startup, you might focus exclusively on ring 0. As you grow you will typically get more use-cases, more industries, more adjacent systems, and thus more rings. That’s fine as long as you’re defining the rings clearly and triaging them into: hot pursue, pursue, and slow-roll or some similar encoding system.

With a few clearly established tiers you are now ready to report on ARR and pipeline by ICP tier to see if “you’re walking the talk” when it comes to your ICP. At many companies, you will find the majority of the ARR and pipeline [2] outside ring 2 or 3. In these cases, you simply aren’t living your ICP and instead suffering from a faux focus. The usual cause is an inability to control the sales force and prevent their default “chase anything” behavior [3].

The ICP is typically born in the founder’s head as an hypothesis. Think: I bet if we can build something like this, it will solve a problem like that. By the time a company has been founded and a product built, it becomes an aspiration. Think: I want to sell to people like this to solve a problem like that. So you sharpen your definitions of this and that, and add some additional targeting criteria like company size, industry, or adjacent systems. And then you go off and sell.

Let’s say it works. One day you look up and you’re now $50M or $100M in ARR. Congratulations. Should your ICP still be an intuition-driven aspiration? No. It should be a regression. Reality happened. Let’s find out what reality is telling us.

Are the people in our ICP ring 0 really our best customers?

Well, what do we mean by best? Do they have higher ASPs? Do they have shorter sales cycles? Do they renew at higher rates? Do they expand at higher rates (e.g., NRR)? Do we win new deals at higher rates? Do they give us higher CSAT scores?

At the first order, these are all just simple segmented metrics calculations that you can and should do. Your QBR and board decks should show these key metrics segmented by ICP tier [4]. And — since not all these metrics can be important — your e-team also needs to have the conversation about “what do we mean by best” so you can have a common, precise definition of the “best” customers that you are trying to target [5].

But the best answers to these questions are not performed using segment analysis [6]. Segment analysis is great for finding anomalies — e.g., why do we have a higher win rate in ICP tier 3 than tier 1? But it’s not a great technique for actually finding the impact of different variables on the success criteria.

For that, we need regression analysis. Regression analysis will tell us which variables most strongly correlate with the outcomes we want (e.g., that the strongest predictor of renewal is company size, not CSAT) [7]. A good regression analysis will tell you not only which factors most correlate with the outcome, but it can also be the best way to bucket those variables (e.g., the real breakpoint is at 250 employees, but your initial segment went from 0 to 500).

Odds are, when we do this kind of analysis we’ll find lots of surprises. Some of your intuition will be proven correct, but some won’t. And you’ll likely find entirely new variables (e.g., number of data scientists) that you didn’t even consider in your initial ICP exercises.

So this is why I like to say that your ICP starts as an aspiration — about who you want to sell to — and over time becomes a regression. Because one day you will have lots of data to analyze to determine who your best customers are — subject to your definition of best, of course — as opposed to who you thought they would be.

# # #

Notes

[1] Regardless of where you land at least be aware there are two types of criteria: those that change slowly or not at all (e.g., company size, adjacent systems, industry) and those that can change overnight (e.g., out shopping, budget, authority). My analogy here is dating: you can meet the right person at the wrong time. It doesn’t change the fact that they’re the right person. (And that’s why God made nurture tracks.)

[2] Think of pipeline as a potential leading indicator of ARR. Well, it should be, at least.

[3] Using the ICP in territory and compensation plan definitions can help with that. Think: you only earn commissions on customers in ICP rings 1 through 3 within your geographic territory. That will get your sellers’ attention.

[4] Note that I’m kind of using ICP tier and ring synonomously here and that’s generally OK. However, in cases where you have lots of rings, I would then sort those rings into tiers, so ring is the more specific and tier the more general term. For me, because I like simplicity, I want to see ICP segmentation in at most 3-4 buckets, so if there are N rings underlying those, I’d prefer to hide those by using 3-4 tiers.

[5] You probably don’t want marketing targeting high LTV prospects when sales wants to target high win rate ones. We should all be on the same targeting page.

[6] One of the key problems being that the segments themselves were somewhat arbitrarily chosen. Sure, we did our best to guess who’d be our best customers. But who are they actually? We may have used not only the wrong bucket boundaries (e.g., 100 emps vs. 500 emps) but even the wrong dimensions (e.g., maybe company size is a poor predictor and industry or use-case a powerful one).

[7] I cheated here on purpose to see if you were paying attention. Thus far, we’ve largely said the ICP is about a company (firmographics) and a role/persona. But here I’ve said that company size is a better predictor or renewal than CSAT — and CSAT isn’t a ICP-style criteria. The reality is these tools can do precisely that, looking across a wide range of input variables to see which most influence the output. Obviously, for marketing targeting purposes we don’t want CSAT to be an input variable to the model, but for renewals analysis we sure would.

The post Your ICP Starts as an Aspiration and Becomes a Regression appeared first on Kellblog.

I kept in loose touch with Ian over the years as he went to Documentum (content management) [3], SeeBeyond (supply chain), Alfresco (content management), and eventually to Intacct (accounting), later called Sage/Intacct after their subsequent acquisition by Sage.

So when I heard Ian wrote a book on how to use generative AI to improve marketing, I was intrigued. When I learned he was so excited about generative AI’s potential that he took a year off from work to dedicate all his time to the task, I was hooked. Whatever he produced, I was going to read it.

What he produced was a book called Smart Conversations, Revolutionizing B2B Marketing with the Generative AI Playbook. And in this post, I’ll share my conclusions based on a pretty in-depth reading of his book.

Here they are:

• Anyone in B2B marketing with an interest in strategy should read this book.
• This book isn’t what I expected. I feared the book might be full of prompts for generating content marketing (aka, AI slop), copy for marketing campaigns, presentations, or infographics.
• Instead, Ian has produced an elegant work that teaches B2B marketing strategy while showing how to use generative AI to define and implement it. I’m not 100% sure what I was expecting, but this sure wasn’t it. It’s way, way better.
• The book is both theoretical and applied. One page he’s explaining why you should target what I’d call sub-segments (that he calls micro-verticals). Five pages later he’s walking you through the prompts he uses to to build lists of them, right down to their NAICS codes.
• On one page he’s talking about the definitions of ideal customer profiles (ICPs) and improved Geoffrey Moore positioning templates. A few pages later he’s got you in the prompts for getting ChatGPT to generate them. One minute he’s talking theoretically about the opportunities created by market discontinuities and, boom, several pages later, he’s back in the prompts showing you how to use ChatGPT to discover them.
• What’s even more fun is how he shows what it used to take to do some of these exercises. Like building messaging by doing deep customer interviews, transcribing your notes, printing them, and then spreading them over a conference room for days so you can spot patterns. And then contrasting that to just how fast you can do it today.
• This wonderful pattern repeats, through competitive analysis, all-in-one positioning, power messaging, and “wall of sound” campaigns [4]. Each time, the theory and then the ChatGPT practice.
• Ian concludes with measurement. That section comes complete with a lesson on the benefits of becoming a market leader (that we both learned from the sting of Oracle’s lash), with lessons quite similar to what I describe in The Market Leader Play.

Congratulations to Ian on writing such a great book and sharing it with us. I’m glad you took the year off to write it! Now, every B2B marketing leader should go read it. Kindle version here.

Notes

[1] It’s not every day you find one of your company’s anti-competitor documents in the Computer History Museum!

[2] The company, by the way, was called Ingres. But since few have heard of Ingres today, I remind people they’ve almost certainly heard of its offspring: Postgres, which stood for Post-Ingres, an open source and extensible version of the system that achieved enormous popularity. I often say that “Postgres is corn” in the sense of The Omnivore’s Dilemma (i.e., it’s in everything) or quip that Postgres is Stonebraker’s revenge. While Larry Ellison made all the money, Stonebraker did win a Turing Award, create several new classes of database systems (e.g., column-oriented), and build Postgres which while ranking fourth on db-engines is generally acknowledged to have a higher market share than Oracle, in part due its open source heritage.

[3] And one of the original case studies in Geoffrey Moore’s classic, Crossing The Chasm.

[4] I’m not capable of typing the words “wall of sound” without referencing the Grateful Dead’s amazing and utterly impractical public address system. What Ian’s describing is what I call a backfire or surround-sound campaign, the goal being the economic buyer at your target can’t stop hearing about you from all sides. Regardless of the name, it’s a great idea, and a much more realistic goal on a limited budget than making “everyone” hear about you (e.g., super bowl ads).

The post Smart Conversations by Ian Howells: A Must-Read Book on Where B2B Marketing Strategy Meets Generative AI appeared first on Kellblog.

First, a bit on the authors. The definition of EIR (here meaning executive-in-residence) varies widely — as does the job itself. At Balderton, it means that we are on-staff resources available to help portfolio companies, on an opt-in basis, with the issues that founders and executives face in building a startup. Dan focuses on technology and engineering while I focus on sales and marketing. Dan’s founded two startups as well as having technology leadership roles at Criteo, Google, and Microsoft, and I’ve been CEO of two startups in addition to having served as CMO of three. That means we are both able to see the bigger picture in addition to our purely functional views. Not to be immodest, but I’d have trouble finding two better people to write an article on how to align product/technology and go-to-market. Heck, we even had the expected us vs. them disputes!

I write a lot about aligning sales and marketing (always remember the CRO is the #1 cause of death for the CMO), but I’ve not written before about aligning product and GTM. So this was a new, fun challenge that necessarily led to strategy, organizational behavior, and leadership. Yes, often, the CEO is the cause of the problem. I can’t tell you the number of times I’ve said: “You want to know whose fault this is? Grab a mirror!” But knowing that doesn’t necessarily help the particpants in a mess unless they know how to get out of it. Usually that starts by asking one simple question: why would anyone want to buy this again?

Does any of this sound familiar?

It’s a 2,750-word paper, which should take around 10 minutes to read, and I’d encourage everyone to check it out. We’ve got some nice, juicy historical examples in there where good companies, even great companies, lost the plot, forgot about customer value and wasted tons of resources as a result. Spare yourselves that pain. Or, if you’re in the thick of it already, step up and start asking the one big question: why would anyone want to buy this again?

The post Aligning Product and Go-To-Market with Metrics appeared first on Kellblog.

The dialog goes something like this:

• There are so many products out there,
• They are getting more similar,
• Customers are more confused than ever,
• Unable to see the differences between them.
• Thus, we are lost in sea of sameness.

I believe the first four statements are largely true, though I might challenge statement two.  But the conclusion — that we are inevitably flotsam in a sea of sameness — is where I beg to differ most.

Somewhere along the way, we got lost.  We’ve turned what should have been the problem statement into an invitation to a pity party.  The correct response to differentiation challenges isn’t “woe is me,” but “that’s why we get paid the big bucks.” 

That’s our job.  That’s what we do here:  differentiate similar products in the minds of customers.  See Positioning.  No, it’s not easy.  But the day you think differentiation is impossible is the day you should turn in your marketing gun and badge.  Differentiation is always possible.  If consumer packaged goods (CPG) marketers can differentiate rice or yogurt, then we can darn well differentiate enterprise software.

While we’re at it, none of these arguments are new.  Thirty years ago, when we were building Business Objects, most customers couldn’t tell you the differences between Actuate, Brio, BusinessObjects, Cognos, Crystal, Discoverer, Essbase, Forest & Trees, MicroStrategy, OLAP@Work, Panorama, ReportSmith, Spotfire, TM1, and a dozen other business intelligence tools.  (Yes, markets were crowded back in the day, too.)

It was not because those differences didn’t exist.  It was because you had to be a connoisseur to see most of them.  But most customers aren’t connoisseurs and don’t want to be.  They’re just businesspeople with problems that they’re hoping to solve.

Differentiation is a key duty of product marketing [1]. You do it in three ways:

• Essence distillation.  First, you need to find the essence of what makes the product different.  Sometimes those differences are general, sometimes they’re specific to given use-cases. The key questions are:   What’s actually different?  What’s not, but maybe the founders wished it were?  What used to be different, but isn’t any more [2]?  What’s different, but only in shades-of-gray and not in black-and-white [3]?  You need to get to the heart of what’s both actually different and differentiate-able, in the sense that you can explain why pretty easily.

• Emphasis of differentiated features.  Once you understand what’s different, you need to build a message that emphasizes your differentiation.  One standard approach is to “set the agenda” by turning your differentiation list into your buyer’s selection criteria.  One way to do that is to write an Evaluation Guide that explains the key features buyers should be looking for and which prominently includes your key differentiators and why they matter.

• Selling benefits and consequences.  Every feature has benefits (the good things that happen when you have it) and consequences (the bad things that happen when you don’t).  Great marketers market both.  Think:  alerting is critical to the successful deployment of your conversational intelligence system [4].  Or:  God help you if your data governance platform can’t manage data assets from the modern data stack [5].  

In short:  if you’re shopping for a product in [category], then be sure to find one that includes features ABC.  If you do, you’ll succeed and reap benefits DEF.  If you don’t, you’ll fail and face consequences PDQ.  See my post on how to build a marketing message for more.

Navigating the Sea of Sameness

So how do you navigate the sea of sameness?  Good old-fashioned product marketing.  But if the answer is so simple, one must wonder, why are people talking so much about the sea of sameness today?  Why is the volume so high on this message?

Are products really getting so similar that customers can’t see differences among them?  Or is it something else?

I think the sea-of-sameness conversation is less about changes in markets, and more about changes in marketers.  That is, the staffing profile of today’s software CMOs.

Back in the day, nearly 100% of CMOs came from product marketing backgrounds.  Today, that’s no longer true.  Because pipeline generation is now the sine qua non of marketing, the vast majority of today’s CMOs come from demand generation backgrounds. 

So, when faced with a challenging differentiation problem, it’s a little too easy for them to blame the market and tell the CEO that we’re lost in a sea of sameness. 

When you’re only tool’s a hammer, problems that don’t look like nails are for someone else to solve.  Many CMOs are, in effect, saying that the problem isn’t marketing’s lack of skills in finding and emphasizing product differentiation, but that such differentiation does not exist.

Hogwash.  The fault lies not within our stars but within ourselves.                   

What’s a founder/CEO to do about all this?

• Beware knee-jerk brand spending.  If you follow this line of reasoning, you then say “well, since we can’t differentiate our product, we’re going to need to differentiate our company.  Ergo, we need to spend a ton on branding.”  While brand spend might be a good thing for your company, it might not be.  But God help you if you think differentiating your company is going to be easier than differentiating your product [6].
• Hire a strong product marketer.  In most cases they should report directly to you — and not a CMO more interested in pipegen or a product leader more interested in roadmap.  While board members might question this as unusual, you’ll find a better product marketer if they work directly for you and remove a potentially uninterested middleman.
• Work closely with them.  Great product marketers need to interact with (even, interview) the CEO, founders, and product leaders repeatedly, searching for nuggets, and structuring what they hear.  The process is highly iterative and somewhat subjective.  Hopefully with each cycle you improve both quality and consensus. 
• Support them.  While the product marketer should be able to hold their own in debates with sales, product, and the e-team, there is no substitute for founder/CEO support when trying to standardize a company on a message.  Think:  “I know this may not be perfect, but it’s very, very good, and we’ve iterated ten times with Sandy.  This is what we’ve decided to go with.”

As a founder/CEO you’re likely to already be hearing about the sea of sameness from your sales and marketing teams.  The question is:  what are you going to do about it? 

Blame the product, and set off on a endless quest for potentially irrelevant differentiation – all while investing more and more of your marketing dollars in branding?  Or hire some product marketers who can distill the essence of what you’ve got today and build on it?

There is no sea of sameness.  Only marketers who don’t know how to differentiate.

Notes

[1] Some product marketers think demonstrating value is the job.  And in some situations (e.g., an early-stage startup selling an entirely new thing) you do certainly need to sell value.  But in more developed markets, the game quickly changes from why buy one to why buy mine?   The reward for successfully selling the concept is typically N competitors all selling something similar.

[2] Companies often cling to lost differentiators, well past their neutralization date.  This is likely due to positive reinforcement from past success and, surprisingly, the fact that it usually still works for a while even though the feature is no longer differentiated.  (A mind is a difficult thing to change.) But eventually customers learn that the differentiation is no more, and you lose both product differentiation and credibility with the customer.

[3] And is harder to demonstrate.  So hard, perhaps, that it’s not worth trying.  This is why I often refer to “graying-out” competitive differentiators.  You don’t need to match them functionally; you just need enough to take their formerly black-and-white difference and turn it gray, changing their claim from “only” to “better.”

[4] And you should be able to explain why.

[5] And yes, you should explain in detail the exact problems that God will need to help you with.  The rhetoric is fine, but only if you can back it up.

[6] If you think tech products all look the same to customers, try tech companies.  They’ve all got hip founders who went to Stanford to MIT, tons of venture capital (and no, they can’t tell Redpoint from Sequoia), modern offices, ping pong tables, youthful energy, a “we’re going to change the world” vision, customer focus and integrity as core values (regardless of whether they actually practice either), and a strong conviction that their people is really what differentiates them.  Think hard about really differentiating your company from a dozen others, in your space or not, and then you might find yourself in a real hurry to go back and differentiate your product.

The post Navigating the Mythical Sea of Sameness appeared first on Kellblog.

I can’t tell you the number of times I applied this rule when I ran marketing from $30M to $1B at Business Objects back in the day. It’s 1:00 AM, I’m doing final edits on a press release or launch deck. Sure, I have some crazy creative idea that I’d like to try. But it’s 1:00 AM and the materials need to be ready tomorrow.

When in doubt, be clear.

I’ll be creative another day. Or, I’ll get my shit together earlier next time and leave myself more time to be creative, try something, get feedback, sleep on it, and make a final decision. Creativity needs feedback and time. It’s inherently riskier. But time’s the one thing I don’t have in this scenario.

Or, I’ll be reading something where I’m lost in the minutiae of differentiation. Christ, I can barely understand it. Should I stick with the writer who’s trying to differentiate or should I kick it up a level and just ensure people understand what it is and why they might buy one?

When in doubt, be clear.

I guess I’ll need to leave differentiation to another day. And put more work into writing the shorter letter that crisply explains why my feature is different from the other product’s. But, once again, I don’t have time.

So, when in doubt, be clear.

So many marketers today get axle-wrapped in jargon, buzzwords, and attempts at differentiation that when people hear your schtick, they glaze over. They have no idea what it is or why they’d buy one. But they know it’s AI-native, next-generation, cloud-native, low-code/no-code, a copilot, and now agentic.

But what was it again? I have no idea.

When in doubt, be clear.

</rant>

Note

As part of my evolving social media strategy I now write short, ranty posts on LinkedIn and usually reserve Kellblog for longer, more well thought out — and hopefully better written — posts. But today I wrote a quick rant that I liked enough that I wanted to post it here for posterity (and indexability). If you want to read all my stuff, please also follow me on LinkedIn because some material only goes there. And if you want to hear all my stuff, please listen to SaaS Talk with the Metrics Brothers, my podcast with Ray Rike.

The post The First Rule of Messaging: When in Doubt, Be Clear appeared first on Kellblog.

Is a dyed-in-the-wool seller with sales blood coursing through their veins. Their first sales job was in high school and they’ve sold anything from dictionaries to shoes to cars early in their career. When asked, “what’s your favorite part about sales?” they should say “winning.” Not “the process,” or “enabling the team,” or even “money,” but winning. You want to hire the person who will eventually rise to be a CRO managing a large team but who, when they’re approaching retirement, steps down to carry a bag for the last phase of their career — because selling, not managing, was their first love. Such people exist. Go find one.

Has sold to the target buyer before. While many sales skills are generic, you can save six to twelve months of onboarding time by hiring someone who has already sold to your target buyer. CFOs are different from CROs are different from CHROs are different from CCOs are different from CIOs are different from CDOs. Ditto for industries if you’re a vertical SaaS company. Glossing over these differences will do nothing to enhance your sales cycle. As they say in France, vive la difference. Embrace the idiosyncrasies of your buyer because to do so is to truly know and understand them. Certainly, it’s tempting to find “a sales athlete” and assume they can learn about your buyer — and they can — but you’ll lose valuable time teaching them and they’re certain to arrive with precisely zero existing customer and partner relationships for you to leverage. One day, when you’re huge, you may have to hire sales talent unfamiliar with your buyer and build an onboarding program to teach them. But that day is not today.

Has sold deals in your size range before. Size is an excellent proxy for complexity. Simply put, a $20K deal is a lot simpler than a $200K deal which is a lot simpler than a $2M deal. Find someone who has sold deals in your current size range and with some headroom above that. If 30 years in enterprise software taught me one thing, it’s this: deal size is in the eye of the beholder. The candidate must have prior experience selling deals in your current size range, but also be able to “see” how you could do deals that are significantly larger. Never hire someone who’s only done $20K deals before when they’ll be selling your $200K system.. They are going to lose a lot of deals learning the intracies of complex, multi-constituent sales cycles in large organizations. Let someone else pay for that education.

Has the executive presence to work a level above your target buyer. If you’re selling to the VP of FP&A, you want to hire someone with enough polish to meet with the CFO. Or, if you are selling to the VP of Support, you want a seller who can converse with the CCO. This characteristic is hard to quantify, but it goes by words like presence, polish, savvy, and gravitas. Simply put, if you can’t imagine your candidate in a serious conversation with your target buyer’s boss, then don’t hire them. Your seller needs to be looking across at the target buyer’s boss, not up.

Has significant first-line sales management experience. This is my most controversial criteria because with one bullet I wipe out all candidates who are currently individual contributor (IC) sellers. “But the job doesn’t require sales management! For at least a year, all they’ll be doing is selling. Why can’t I just hire an aggressive seller early in their career?” I hear you cry. First, ICs will generally do worse than experienced managers on the polish and presence front. Second, because you are about to put this seller, at a great investment in time and money, through the world’s best onboarding program: 6-12 months of being glued directly to the founder, doing almost every sales call together, doing every customer proposal together, and closing every deal together. There is literally no better sales onboarding program and you don’t want to waste that investment in someone who, at the end of the onboarding, is only equipped to go sell on their own. You want to invest in someone who can say: “Thank you for the amazing training, I am now ready to be unleashed to do what I already know how to do, which is to build, manage, and scale a sales team.” If you get this wrong, you’re stuck with two bad options: (1) promote the IC to a manager and hope they have the aptitude to succeed, or (2) hire them a boss, do a poor job repeating the onboarding you just did, and hope that they succeed and that they don’t drive out the IC you’ve successfully trained.

Is willing to join your company way “too early.” But why would any sane sales manager want to step back into an IC sales job to join your company? Because they see the potential. Because they believe in your vision. Because they believe in you. And because they understand that if they want to be the first sales VP at your company, there’s only one way to do it: join as an IC, get glued to the founder for 6-12 months, and earn it. There is no shortcut. There is no other path. There’s no, “call me back when you’re looking for a sales director in 12 months,” because you won’t be. This also serves as an important test on their commitment to your company’s vision and on their true love of selling. “Carry a bag for 6-12 months, glued to the founder, in order to earn the sales head job? Sure, why not. I love selling.” And, “after that training program, combined with 6-12 months of selling this system myself, I will literally be the best person on earth to build the sales team for this product.”

This is why I say that your first seller should be willing to join the company too early relative to their resume, and be willing to run in a three-legged race with the founder for 6-12 months. That’s the job.

Now, I’m not arguing that it’s going to be easy to find this person. But I have done it and I know others who have as well. I can’t take solo credit for the idea, either, as it crystallized in my mind many years ago in a conversation with Moveworks founder Bhavin Shah. I had some of the pieces in place, but it all came together in that chat.

The best argument for this approach, as alluded to above, is what happens if you don’t do this. Hard as this approach sounds, I have worked with many, many companies who ended up either riding a IC salesrep into management well beyond their abilities or one day realizing that they’ve spent two years giving the world’s best onboarding person to the wrong person — and now have to start over.

So while my approach may sound difficult and unpleasant, well, consider the alternatives.

The post Who To Hire As The First Salesperson In Your Startup appeared first on Kellblog.

“I have a map of the United States … actual size. It says, Scale: 1 mile = 1 mile. I spent last summer folding it. I also have a full-size map of the world. I hardly ever unroll it.” — Stephen Wright (comedian)

Much as we build maps as models of the physical world, we build mathematical models all the time in the business world. For example:

• A sales booking capacity model
• A marketing inverted funnel model
• A marketing attribution model

These models can be incredibly useful for planning and forecasting. They are, however, of course, wrong. They’re imperfect at prediction. They ignore important real-world factors in their desire for simplification, often relying on faith in offsetting errors. Reality rarely lands precisely where the model predicted. Which brings to mind this famous quote from the British statistician George Box.

“All models are wrong. Some are useful.” — George Box

It’s one of those quotes that, if you get it, you get it. (And then you fall in love with it.) Today, I’m hoping to bring more people into the enlightened fold by discussing Box’s quote as it pertains to three everyday go-to-market (GTM) models.

First, it’s why we don’t want models to be too precise and/or too complex. They’re not supposed to be exact. They’re not supposed to model everything, they’re supposed to be simplified. They’re just models. They’re supposed to be more useful than exact.

For example, in finance, if we need to make a precise budget that handles full GAAP accounting treatment then we do that. We map every line to a general ledger (GL) account, do GAAP treatment of revenue and expense, model depreciation and allocations, et cetera. It’s a backbreaking exercise. And when you’re done, you can’t really play with it to learn and to understand. It’s precise, but it’s unwieldy — a bit like Stephen Wright’s full-scale map of the US. It’s useful if you need to bring a full-blown budget to the board for approval, but not so useful if you’re trying to understand the interplay between sales productivity, sales ramping, and sales turnover. You’d be far better off looking at a sales bookings capacity model.

To take a different example, it’s why business school teaches you discounted cashflow (DCF) analysis for capital budgeting. DCF basically throws out GAAP and asks, what are the cashflow impacts of this project? The assumption being that if the DCFs work out, then it’s a good investment and that will eventually show up in improved GAAP results. Notably — and I was really confused by this when I first learned capital budgeting — they don’t teach you to build a 20-year detailed GAAP budget with different capital project assumptions and then do scenario analysis. Instead, they strip everything else away and ask, what are the cashflow impacts of this project versus that one?

In the rest of this post, I’ll explore Box’s quote as it relates to the three SaaS GTM models I discussed in the introduction. We’ll see that it applies quite differently to each.

Sales Bookings Capacity Models

These models calculate sales bookings based on sales hiring and staffing (including attrition), sales productivity, and sales ramping (i.e., the productivity curve new sellers follow as they spend their first few quarters at the company). Given those variables and assuming some support resources and ratios (e.g., AE/SDR), they pop out a series of quarterly bookings numbers.

While simple, these models are usually pretty precise and thus can be used for both planning and forecasting (e.g., predicting the bookings number based on actual sales bookings capacity). Thus, these are a lot useful and usually only a little wrong. In fact, some CEOs, including some big name ones I know, walk around with an even simpler version of this model in their heads: new bookings = k * (the number of sellers) where that number might be counted at the start of the year or the end of Q1. (This is what can lead to the sometimes pathological CEO belief that hiring more sellers directly leads to bookings, but hiring anything else does not, or at least only indirectly.)

Marketing Inverted Funnel Models

These models calculate the quarterly demand generation (demandgen) budget given sales booking targets, a series of conversion rates (e.g., MQL to SAL, SAL to SQL, SQL to won), and assumed phase lags between conversion points. They effectively run the sales funnel backwards, saying if we need this many deals, then we need this many SQLs, this many SALs, this many MQLs, and this many leads at various preceding time intervals.

If you’re selling anything other than toothbrushes, these models are wrong. Why? Because SaaS applications, particularly in enterprise, are high-consideration purchases that involve multiple people over sometimes prolonged periods of time. (At Salesforce, we won a massive deal on my product where the overlay rep had been chasing the deal for years, including time at his prior employer.)

These models are wrong because they treat non-linear, over-time behavior as a linear funnel. I liken the reality of the high funnel more to a popcorn machine: you’re never sure which kernel is going to pop, when, but if you add this many kernels and this much heat, then some percentage of them normally pops within N quarters. These models are a lot wrong — from first principles, by not just a little bit — but they are also a lot useful.

I think they work because of offsetting errors theory, which requires the company to be on a relatively steady growth trajectory. Sure, we’re modeling that last quarter’s MQLs are this quarter’s opportunities, and that’s not right (because many are from the quarter before that), but — as long as we’re not growing too fast or, more importantly, changing growth trajectory — that will tend to come out in the wash.

Note that if you wanted to, you could always build a more sophisticated model that took into account MQL aging — or today use an AI tool that does that for you — but you’ll still always be faced with two facts: (1) the trade-offs between model complexity and usefulness and (2) that even the more sophisticated model will still break when the growth trajectory changes or reality otherwise changes out from underneath the model. Thus, I always try to build pretty simple models and then be pretty careful in interpretation of them. Think: what’s going to break this model if it changes?

Marketing Attribution Models

I try not to write much about marketing attribution because it’s quicksand, but I’ll reluctantly dip my toe today. Before proceeding, I encourage you to take a moment to buy a Marketing Attribution is Fake News mug which is a practical, if passive-aggressive, vessel from which to drink your coffee during the next QBR or board meeting.

Marketing attribution is the attempt to assign credit for marketing-generated opportunities (itself another layer of attribution problem) to the marketing channels that generated them. In English, let’s assume we all agree that marketing generated an opportunity. But that opportunity was created at a company where 15 people over the prior 6 quarters had engaged in some marketing program in some way — e.g., clicking an ad, attending a webinar, downloading a white paper, talking to us at a conference, etc.

There are typically two levels of reduction: first, we identify one primary contact from the pool of 15 and second, we identify one marketing program that we decide gets the credit for the opportunity. Typically, people use last-touch attribution, assigning credit to the last program the primary contact engaged with before the opportunity was created. This will overcredit lower-funnel programs (e.g., executive dinners) and undercredit higher-funnel programs (e.g., clicking on an ad). Some people use first-touch attribution, reversing the problem to over-credit higher-funnel programs and under-credit lower-funnel ones. Knowing that both of those problems aren’t great, some send complexity to the rescue, using points-based attribution where each touch by each person scores one or more points, and you add up those points and then allocate credit across channels or programs on a pro rata basis. This is notionally more accurate, but the relative point assignments can be arbitrary and the veil of calculation confusion generally erodes trust in the system.

The correct way, in my humble opinion, to do attribution analysis is to approach it with humility, view it as a triangulation problem, and to make sure people absolutely understand what you’re showing them before you show it (e.g., “we’ll be looking at marketing channel performance using last-touch based attribution on the next slide and before I show it, I want to ensure that everyone understands the limits of interpretation of this approach.”) Then follow any attribution-based performance analysis with some reverse-touch analysis where you show all the touches over the prior two years, deal by deal, for a small set of deals chosen by the CRO in order to demonstrate the messy, ground-level reality of prospect interactions over time. Simply put, it’s the CMO’s job to decide how to allocate resources in this very squishy world, to make those decisions (e.g., do we do tradeshow X and do we spend $Y) in active discussion with the CRO as their partner and with a full understanding of the available data and the limitations on its interpretability. The board or the e-staff simply can’t effectively back-seat drive this process by looking at one table and saying, “OMG, tradeshow oppties cost $25K each, let’s not do any more tradeshows!” If only the optimization problem were that simple.

But, back to the Box quote. How does it apply to attribution? These models are a lot wrong, at best a little useful, and even potentially dangerous. Hence my recommendations about disclaiming the data before showing it, using triangulation to take different bearings on reality, and doing reverse-touch analysis to immediately re-ground anyone floating in a cloud of last-touch-based over-simplification.

Note that the existence of next-generation, full-funnel attribution tools such as Revsure, doesn’t radically change my viewpoint here because we are talking about the fundamental principles of models. They’re always wrong — especially when trying to model something as complex as the interactions of 20 over people at a customer with 5 people and 15 marketing programs at a company, all while those people are talking to their friends and reading blogs and seeing billboards from a vendor. I believe tools like Revsure can take the models from a lot wrong to a little wrong, and ergo improve them from potentially dangerous to useful. But you should still show the reverse-touch analysis to keep people grounded.

And Box’s quote still applies: “All models are wrong. Some are useful.” And what a lovely quote it is.

The post “All Models Are Wrong, Some Are Useful.” appeared first on Kellblog.

Here are those three things:

• “I’m getting tired of running the company.”
• “I’m running out of ideas for how to fix our core problems.”
• “I think we need to sell the company.” [1]

First, let’s note that it is much harder, sometimes actually impossible, for a founder/CEO to get fired than a hired (aka, “professional”) CEO. The former have a powerful combination of moral authority, share ownership, and/or contractual protections. The latter — even if they joined very early and built much of the company themselves — will never be seen as founders, but simply employees who, in the end, are replaceable much as anyone else.

While it’d be stretch to call hired CEOs “goldfish” — as one of my old CFOs used to refer to SDRs [2] — in the end, you’re either a founder or you’re not. So this post is largely for hired CEOs, but it should nevertheless be of interest to founders as well.

While it’s probably somewhat self-evident, what’s so scary about the three above statements?

• They each say the CEO is effectively giving up on solving the company’s challenges
• They are not easily fixable by the board — a stock grant, a pat on the back, or a bonus program isn’t likely to fix anything
• To the extent you define the CEO’s job as “to get what matters right,” they each signal that the CEO is no longer interested in doing it

And scariest of all, each statement is a bell that is impossible to unring. Think: Oh, just kidding, I have tons of ideas. Or, oh, I was just messing around, I don’t think we should sell the company. It was just a modest proposal, in the Jonathan Swift sense. Sure.

It’s like saying to your spouse, “hey honey, I think we should start dating other people.” It’s very difficult to roll that back.

This means the CEOs should think very carefully before making statements like these. Because once they’re said, they may be stuck somewhere in the board’s mind forever:

• We keep missing quarters because Mary’s tired and not pushing the company.
• We’re only shooting for moderate growth because Bob’s out of ideas for how to grow more quickly.
• James isn’t investing for growth because he wants to sell soon and is trying to juice up profit.

Why? Because the CEO told us so. If I were a bell, I’d go ding, dong, ding, dong, ding.

Now, it’s certainly possible to try and walk these statements back: “oh, I was just tired that day because I had some personal stuff going on and I was sick.” But they’ll always be there in the back of the board’s mind. Think: Maybe Joe said that because Joe meant it.

So the best thing to do is never say these things in the first place. Not unless you’re very sure how they’ll land and ideally have socialized them 1-1 in advance with key board members. Or, if you decide to say them anyway, at least understand the potential downstream effects. Otherwise, you may find that a simple, off-the-cuff comment may haunt you for some time.

# # #

Notes

[1] The notable exception here is a PE-backed firm where the company has achieved its target financial profile and ergo hopefully its target valuation, and it really is time to sell. In VC-backed firms, where the general goal (and belief that underlies the VC’s investment thesis) is to “shoot the moon,” saying you want to sell can be seen as betraying the mission — especially if the company is performing well — and/or if the VCs still believe in the company’s bright future. Saying you want to sell before there is consensus that hope is dead can be seen as a premature admission of defeat.

[2] On the theory that they often perish, and if you find one floating in the bowl, you just get a new one.

The post Three Ways To Get Fired as CEO appeared first on Kellblog.

Scene: you’re consistently having trouble hitting plan. Finance is blaming sales. Sales is blaming marketing. Marketing is blaming the macro environment. Everyone is blaming SDRs. Alliances is hiding in a foxhole hoping no one remembers to blame them. E-staff meetings resemble a cage fight from Beyond Thunderdome, but it’s a tag-team match with each C-level tapping in their heads of operations when they need a break. Numbers are flying everywhere. The shit is hitting the proverbial fan.

The question for CEOs: what do I do about this mess? Here’s my answer.

First:

• Avoid the blame game. That sounds much easier than it is because blame can vary from explicit to subtle and everyone’s blame sensitivity ears are set to eleven. Speak slowly, carefully, and factually when discussing the situation. You might wonder why everyone is pointing fingers, and the reason might well be you.
• Solve the problem. Keep everyone focused on solving the problem going forward. Use blameless statements of fact when discussing historical data. For example, say “when we start with less than 2.5x pipeline coverage, we almost always miss plan” as opposed to “when marketing fails on pipeline generation, we miss plan unless sales does their usual heroic job in pipeline conversion.”)

Then reset the pipeline discussion by constantly reminding everyone of these three facts:

• How do you make 16 quarters in a row? One at a time.
• How do you make one quarter? Start with sufficient pipeline coverage.
• And then convert it at your target conversion rate.

This reframes the problem into making one quarter — the right focus if you’ve missed three in a row.

• This will force a discussion of what “sufficient” means
• That is generally determined by inverting your historical week 3 pipeline conversion rates
• And adjusting them as required, for example, to account for the impacts of big deals or other one-time events
• This may in turn reveal a conversion rate problem, where actual conversion rates are either below targets and/or simply not viable to produce a sales model that hits the board’s target customer acquisition cost (CAC) ratio. For example, you generally can’t achieve a decent CAC ratio with a 20% conversion rate and 5x pipeline coverage requirement. In this case, you will need to balance your energy on improving both conversion rates and starting coverage. While conversion rates are largely a sales team issue, there is nevertheless plenty that marketing and alliances can do to help: marketing through targeting, tools, enablement, and training; alliances through delivering higher-quality opportunities that often convert at higher rates than either inbound or SDR outbound.

It also says you need to think about each and every quarter. This leads to three critical realizations:

• That you must also focus on future pipeline, but segmented into quarters, and not on some rolling basis
• That you need to forecast pipeline (e.g., for next quarter, if not also the one after that)
• That you need some mechanism for taking action when that forecast is below target

The last point should cause you to create some meeting or committee where the pipeline forecast is reviewed and the owners of each of the four to six pipeline sources (i.e., marketing, AE outbound, SDR outbound, alliances, community, PLG) can discuss and then take remedial measures.

• That body should be a team of senior people focused on a single goal: starting every quarter with sufficient pipeline coverage.
• It should be chaired by one person who must be seen as wearing two hats: one as their functional role (e.g., CMO) and the other as head of the pipeline task force. That person must be empowered to solve problems when they arise, even when they cross functions.
• Think: “OK, we’re forecasting 2.2x starting coverage for next quarter instead of 2.5x, which is a $2M gap. Who can do what to get us that $2M?”
• If that means shifting resources, they shift them (e.g., “I’ll defer hiring one SDR to free up $25K to spend on demandgen”).
• If that means asking for new resources, they ask (e.g., I’ll tell the CEO and CFO that if we can’t find $50K, then we think we’ve got no chance of hitting next quarter’s starting coverage goals).
• If that means rebalancing the go-to-market team, they do it. For example, “we’ve only got enough pipeline to support 8 AEs and we’ve got 12. If we cut two AEs, we can use that money to invest in marketing and SDRs to support the remaining 10.”
• Finally, if you need to focus on both pipeline coverage and conversion rates, then this same body, in part two of the meeting, can review progress on actions design to improve conversion.

Teamwork and alignment is not about behaving well in meetings or only politely backstabbing each other outside them. It’s about sitting down together to say, “well, we’re off plan, and what are we going to do about it?” And doing so without any sacred cows in the conversation. Just as no battle plan survives first contact with the enemy, no pipeline plan survives first contact with the market. That’s why you need this group and that’s what it means to align sales, marketing, alliances, and SDRs on pipeline goals. It’s the translation of the popular saying, “pipeline generation is a team sport.”

Notice that I never said to heavily focus on individual pipeline generation (“pipegen”) targets. Yes, you need them and you should set and track them, but we must remember the purpose of pipegen is to hit starting pipeline coverage goals. So just as we shouldn’t overly focus on other upstream metrics — from dials to alliances-meetings to MQLs — we shouldn’t overly focus on pipegen targets to the point where they become the end, not the means. While pipegen is certainly closer to starting coverage than MQLs or dials, it is nevertheless an enabler, in this case, one step removed.

Yes, tracking upstream metrics is important and for marketing I’d track both MQLs and pipegen (via oppty count, not dollars), but I’d neither pop champagne nor tie the CMO to the whipping post based on either MQLs or pipegen alone.

Don’t get me wrong — if your model’s correct, it should be impossible to consistently hit starting pipeline coverage targets while consistently failing on pipegen goals. But in any given quarter, maybe the AEs are short and marketing covers or marketing’s short and alliances covers. The point is that if the company hits the starting coverage goal, we’re happy with the pipeline machine and if we don’t, we’re not. Regardless of whether individual pipeline source X or Y hit their pipegen goals in a quarter. Ultimately, this point of view drives better teamwork because there’s no shame in forecasting a light result against target or shame in asking for help to cover it.

Finally, I’d note an odd situation I sometimes see that looks like this:

• Sales consistently achieves bookings targets, but just by a hair
• Marketing consistently underachieves pipeline targets

For example, sales consistently converts pipeline at 25% off 4x coverage and that 25% conversion rate is just enough to hit plan. But, because the CRO likes cushion, he forces the CMO to sign up for 5x coverage. Marketing then consistently fails to deliver that 5x coverage, delivering 4x coverage instead.

This is an unhealthy situation because sales is consistently succeeding while marketing is consistently failing. If you believe, as I do, that if sales is consistently hitting plan then, definitionally marketing has provided everything it needs to (from pipeline to messaging to enablement), then you can see how pathological this situation is. Sales is simply looking out for itself at the expense of marketing. That’s good for the company in the short term because you’re consistently hitting plan, but bad in the long term because there will be high turnover in the marketing department that should impede their ability to deliver sufficient pipeline in the future.

For more on this topic, please listen to our podcast episode of SaaS Talk with the Metrics Brothers entitled: Top-Down GTM Troubleshooting, Dave’s Method.

The post A CEO’s High-Level Guide to GTM Troubleshooting appeared first on Kellblog.

If so, you’ve got a problem. Not only because his younger brother Big is a controversial employee over in DOGE but, more importantly, because old Harvey is not defining his job correctly.

Many competitive analysts effectively define their job as product comparison. In short, to make Harvey Balls that compare products on different features, usually on a 1-5 scale using a circular ideogram. You know, something like this:

Harvey Balls are a useful communication tool. And you can use them not only for product features, but for non-functional product attributes (e.g., useability, performance) and even company attributes (e.g., support, viability).

I’ve got no problem with Harvey Balls in theory. In practice, however, such charts often quickly fall apart because they are entirely subjective and lack any rigorous foundation for the underlying 1-5 scoring. If you’re going to make comparisons using Harvey Balls, you must strive to maintain credibility by documenting and footnoting your scoring system, so a reader can verify the basis on which you’re assigning scores.

Otherwise, Harvey Balls are simply opinion thinly disguised as fact.

So what’s the real problem if your competitive analyst thinks his name is Harvey Balls?

Well, old Harvey has missed the point.

Marketing teams shouldn’t pay competitive analysts to make product comparisons. They should pay them to win deals. To quote one of my favorite movie scenes:

“I know how you feel. You don’t believe me, but I do know. I’m going to tell you something that I learned when I was your age. I’d prepared a case and old man White said to me, “How did you do?” And, uh, I said, “Did my best.” And he said, “You’re not paid to do your best. You’re paid to win.” And that’s what pays for this office … pays for the pro bono work that we do for the poor … pays for the type of law that you want to practice … pays for my whiskey … pays for your clothes … pays for the leisure we have to sit back and discuss philosophy as we’re doing tonight. We’re paid to win the case. You finished your marriage. You wanted to come back and practice the law. You wanted to come back to the world. Welcome back.”

To reiterate for Harvey’s sake: you’re not paid to make product comparisons, you’re paid to win.

What does that mean?

• The goal of competitive is not to produce research for the sake of knowing, to support product management, or to tell sales so they can figure out how to use it.
• The goal is to win deals, which does require in-depth product and competitive knowledge.
• Competitive intelligence is an applied function — they must apply the knowledge gained from research into creating sales plays to win deals.

And note that the research need not be limited to product — it can and should include the competitions’ sales plays (i.e., what they plan to do to us and how to defeat it). And it can and should include company research (e.g., executive biographies to anticipate strategies).

Let’s elucidate this via an example. Let’s say your competitor sells a data analysis product that demos really well. Yours looks like a clunker by comparison, especially in quick reactions to end-user demos. Let’s also say that competitor has poor governance, administration, and security controls. Yours look great by comparison. Furthermore, let’s say you know your competitor is going to run a sales play called “the end run,” where they want to leverage the end-users’ love for the product to effectively ram it down the throat of a resistant central data team.

Let’s contrast three approaches:

• Product comparison: create Harvey Balls that show the relative strengths and weaknesses in useability vs. administration.
• Holistic research: include warning your sales team to expect the end-run as the competition’s standard sales play.
• Win-deals: use all that information to create a sales play called “the Heisman” where you leverage the central data team to anticipate and block the end users to avoid purchasing a system with insufficient security and administration. That includes reframing user sentiment from a selection criteria to a hygiene criteria (i.e., it needs to be “good enough”).

Don’t get me wrong. Good product knowledge is critical. But it’s simply the foundation of the win-deals approach which also factors in company- and sales-level intelligence and then applies everything to creating sales plays that win deals.

If you had to put a metric on all this, it would be win rate. Competitive’s job is not to produce reports. It’s to increase head-to-head win rate vs. chosen competitors. If they sign up for that, then the rest should follow.

The post Your Competitive Analyst Should Not Be Named Harvey Balls appeared first on Kellblog.

• Analyzed existing success in enterprise accounts. (Most people don’t start purely from scratch, but instead have picked up a few “accidental” customers that they hope to replicate.)
• Hired a team to pursue enterprise, ideally with dedicated AE, SCs, SDRs and even more ideally with a dedicated CSM and marketer. Concretely, that initial team might look like 1 sales manager, 4 AEs, 2 SCs, 3 SDRs, 1 CSM, and 1 field marketer. A focused team of dedicated resources is an infinitely better approach to opening a new market segment than making it everyone’s pastime.
• Done market research to understand the competitors you’ll encounter and the buyers to whom you’ll be selling. You’ve then made an initial strategy for beating those competitors and built messaging that maps to those buyers and their business priorities.
• Identified product gaps. Your product team has done a study to identify missing product features that customers in the new segment will demand. You’ve made a plan that combines workarounds and the product roadmap to sell successfully despite these gaps while you work to close them. (Beware that product gaps, often involving non-functional requirements, doom more new market migration initiatives than any other.)
• Sold the initiative to both the board and the company as a valid and important priority. This will get you buy-in at both the board and team level, hopefully evoking real commitment to the new and difficult undertaking. As part of this, you may well have set initial end-state goals — e.g., getting 25% of new ARR from enterprise in six quarters, increasing your average sales price (ASP) by 50% relative to mid-market, and increasing product penetration per account from 1.5 to 3.0 products.
  

That’s awesome. You’re doing it by the book. So what did you forget to do? Particularly when getting to the initial end-state might take six quarters and another year or two beyond that to look at renewal and expansion rates?

What did you forget? To sell hope along the way.

I don’t mean selling hope in the metaphorical Charles Revson way. I mean selling hope in a literal way to the team and the board. Twelve to eighteen months is too long for people to wait for results. But if it takes 3 months to build the team, 3 months to ramp them, and sales cycles are 9-12 months, then your earliest possible results are 15-18 months from when you hit the “go” button with the board. Coincidentally, that approximates the MTBF for CMOs.

How do you sell hope along the way?

• You lay out expectations, up front. You describe how you expect things to progress over time (e.g., enterprise team staffed up, first enterprise marketing event, hit enterprise pipeline of $3M, first closed new customer). And like any expectations, you set them carefully and remind people of them often.
• You tell stories along the way. This is the part most people don’t forget. Hey, 125 people showed up at the enterprise event. Hey, we had a great meeting at BigCo. Hey, an analyst positioned us in an enterprise report. Hey, we hosted a great enterprise dinner in Philly. This is good, but the stories start to ring hollow pretty quickly in the absence of up-front expectations setting and downstream data.
• You use leading indicators. Sure, we all know the end-state indicators that we want. A big chunk of total ARR. Faster growth. Bigger deals. Higher win rates. Increased GRR and NRR. Those are real indicators — and they’re important — but they’re lagging and, in cases, badly. You need to find some leading indicators to track and measure along the way. For example, in the enterprise segment: pipeline size, pipeline coverage, average size of oppties, high- and mid-funnel conversion rates, number of customers. Note that when moving up market, some metrics are going to get worse (e.g., stage progression velocity) so you may as well track and set expectations for those as well.

The main point here is to not make the rookie mistake of screaming: “Mid-market is dead! Long live enterprise!” Thus setting impossible expectations for enterprise all while undermining your mid-market efforts. Instead, you should carefully think through how the enterprise initiative is going to unfold, lay that out both qualitatively and quantitatively with leading indicators, and then report back to the company and board with progress reports, war stories, and metrics.

You’ve already convinced people that the enterprise initiative is a good idea. Now you need to make and execute a plan to keep them excited along the way.

The post Change Management and Selling Hope Along The Way appeared first on Kellblog.

I had to kick about three executives under the table to prevent them from bursting into uproarious laughter. The last thing we needed, the team knew, was more Schmedleys.

Joe Schmedley wasn’t a bad guy. A competent, if somewhat bumbling, enterprise rep. Affable. Could talk about local sports teams. Not a bad guy to have a beer with. Not a particularly good guy at driving paperwork through procurement. Average to a skooch above in most respects. The last thing we needed to do was define our hiring profile by his background and tell a recruiter to go find ten more of them.

So what was driving this false signal in the data? The numbers don’t lie, right? And by the numbers, Schmedley was crushing it. Consistently our top rep.

What’s going on here?

What if I told you:

• Schmedley was hired to manage the company’s largest account.
• That account was originally sold by the founders.
• That account was effectively managed by the execs, specifically, the CRO, CTO and VP of professional services
• That account was quite successful with our technology and expanding their use of it every year
• Schmedley had little to no success outside that major account

Like some successful salesreps, Schmedley had found himself in the right place at the right time. We knew it. Heck, he wasn’t arrogant, I think he knew it. The only people who didn’t know it were the board, who was actively telling us to hire ten more Schmedleys, making him the archetype for all future sales hires.

The problem was, of course, that the big customer was effectively a “house account,” and Schmedley merely a steward. We were in the midst of the transition from founder-led sales to sales-led sales and the board was confusing the account’s success with Schmedley’s.

What can you do to avoid this problem?

• Don’t hire affable stewards. No early-stage startup should. If you somehow convince yourself that all you want is an account manager, then hire a CSM or TAM. Not an salesrep.
• Hire smart, aggressive salespeople who want to learn about success in order to replicate it. They shouldn’t be there to milk the house account. They should be there to learn deeply about it, so they can find ten more.
• Don’t use a highly leveraged compensation plan. If you’re just running a house account, there is a serious question as to whether you should earn typical enterprise sales compensation — e.g., $300K on-target earnings (OTE). Personally, I’d take the $150K variable, put $50K on OKRs for managing the house account, and the remaining $100K on a leveraged new business plan.
• Be consistent. You can’t tell the board in the morning that Schmedley’s success proves the industrialization of our sales model and then, three hours later, say that he’s just a steward. Most CEOs and CROs walk themselves into this problem by trying to have it both ways. Pick one.
• Get ahead of the problem with metrics. Don’t make slides that lead the board to the incorrect conclusion. Pull out the house account from analytics. Split Schmedley’s new business performance from his house account performance. While we all made fun of the board for saying, “we need more Schmedleys,” we did create the slides that lead them to that conclusion.

The post Transitioning from Founder-Led Sales: The “We Need More Schmedleys” Problem appeared first on Kellblog.

So, how do you calculate the cost/opportunity? Well, it depends! On what? On the specific question you’re trying to answer. When people ask about cost/opportunity, they usually have one of two things in mind:

• An efficiency question — e.g., how efficiently does marketing spend convert into sales opportunities (oppties)?
• A cost question — e.g., how much it would cost to get 50 more oppties if we needed them

Knowing which question you’re being asked has a big impact on how to calculate the answer. Let’s illustrate this by looking at this typical marketing budget, which is allocated roughly 45/45/10 across people, programs, and technology:

If this marketing team generated 1,000 oppties, then the average total marketing cost/oppty is $9,000 = $9M/1K oppties. You might argue that’s a good overall marketing efficiency metric and try to benchmark it. But those benchmarks will be hard to find.

Why?

Because there’s a better overall marketing efficiency metric: the marketing customer acquisition cost (CAC) ratio = (last-quarter marketing expense)/(this-quarter new ARR). Why is the marketing CAC a better marketing efficiency metric than average total marketing cost/oppty?

• It’s more standard. While relatively few startups break their CAC ratio in two parts, virtually every startup already calculates CAC ratio or CAC payback period (CPP). People are familiar with the concept and the math mostly already done — just back out the sales expense.
• There is less room for calculation debates. While neither total cost/oppty or marketing CAC is hard to calculate, because marketing CAC is a derivative of CAC, some nagging questions are already answered for you – e.g., Is it all marketing or just a part? Is it GAAP expense or cash expense? Answers: look at how you calculate your CAC ratio for guidance.
• The phase shift. The CAC ratio compares last quarter’s expense to this quarter’s new ARR in an attempt to better match expenses and results.
• There are more benchmark data sets. I can think of about ten sources for CAC ratio data (not all of which make the sales/marketing split). I can think of approximately zero for average total marketing cost/oppty. You can’t benchmark a metric without good data sets to compare against.

So if someone’s asking you about marketing efficiency by looking at average total marketing cost/oppty, I’d politely redirect them to the marketing CAC ratio.

But say they’re looking at cost. Specifically, that the company is forecasting a pipeline generation shortfall of about 50 oppties and the CEO asks marketing: How much money will it take for you to generate 50 more?

Is $9,000 * 50 = $450,000 even correct?

The answer is no. To get 50 more oppties, you don’t need to hire 5% more marketers, boost the CMO’s salary by 5%, up the PR agency retainer by 5%, increase the userconf budget by 5%, spend 5% more on billboards, or increase tech infra spending by 5%. Thus, you should not multiply the average total marketing cost of an oppty by the number of oppties. You should multiply the incremental cost of an oppty by 50.

And the best answer we have here, at our fingertips, for the incremental cost of an oppty is the average demandgen programs cost/oppty. In our example, that’s $3,250. So, to generate 50 more oppties would cost $162,500. That’s good news because it’s a whole lot less than $450,000 and because it’s correct.

In short, cost/oppty = total demandgen cost / number of oppties.

This begs a potential rathole question which I call the low-hanging fruit problem. Most demandgen marketers argue that picking oppties out of the market is like picking apples out of a tree. First, you pick the easy ones, which doesn’t cost much. But the more apples you need, the higher up the tree you have to go. That is, the cost of picking the 1,000th apple is a lot higher than the cost of picking the first one. That is, the average cost of picking 1,000 apples is less than the incremental cost of getting one more.

While I think there’s some truth to this argument — and a lot of truth when it comes to paid search — you can’t let yourself slide into an analytical rathole. As CMO, a key part of your job is to always know the incremental cost of generating 50 more opportunities. Because — as veteran CMOs know well — either or both of these things happen with some frequency:

• There is an oppty shortfall and someone asks how much money you need to fill it. You should answer instantly.
• There is a money surplus and on day 62 of the quarter the CFO approaches you, asking if you can productively spend $100K this quarter. The answer should always be, “yes” and you should start deploying the money the next day.

That’s what you might call “agile marketing.” And you get agile by doing the math in advance and having the incremental spending plan in your pocket, waiting for the day when someone asks.

To make things easy, unless and until you have a spending plan that answers the cost of getting 50 more oppties, just use your average demandgen cost/oppty and uplift it by 25% to adjust for the low-hanging fruit problem. That way you can answer the boss quickly and you’ve left yourself some room.

Let’s close this out by raising a common objection to using demandgen costs only. It sounds something like this:

If I use demandgen cost only, someone might say that I’m understating the true cost of a marketing-generated opportunity and I’m going to get in trouble.

Well, that certainly can happen. People can accuse you of anything. There are two ways to avoid this.

• Speak precisely. If asked, say “the average demandgen cost of an oppty is $3,250.” And, “the incremental cost of getting 50 more will be around $4,050.” (An approximately 25% uplift.)
• Use footnotes. If making slides, always put definitions in the footer. So, if a row is labeled “cost/oppty” then make a footnote that explains that it’s demandgen cost only. Better yet, label the row “demandgen cost/oppty” and use the footnote to explain why that’s a better proxy for an incremental cost — which is the thing most people are worried about.

And finally, remind them if they want to discuss overall marketing efficiency, they should change slides and look at the marketing CAC ratio, which does proudly include every penny of marketing expense. And if you’re really, really good, ask them to skip to the slide that shows the sales/marketing expense ratio and discuss that.

The post How to Calculate Cost Per Opportunity appeared first on Kellblog.

Dan, the founder/CEO of PeopleReign, has been doing AI since long before it was cool and, to give you an idea of how long he’s been podcasting about AI and the future of work, my appearance marks episode 324 of his podcast. He’s no Johnny-come-lately to the fascinating intersection of people and technology and his material is always worth a good listen.

In what’s become a tradition, I’m back on the show to talk about my 2025 predictions blog post. In the 43-minute episode we bounce around a lot, but cover these topics:

• The evolution of search: answers, not links
• LLM optimization, how to show up in LLM-generated answers
• Why it’s dangerous to think you’re lost in a “sea of sameness” when it comes to product differentiation
• Why branding isn’t the last bastion of differentiation
• Why to track Rand Fishkin when it comes to the evolution of SEO to LLMO
• Why general-purpose databases are generally good at absorbing special-purpose databases — but not always
• Does Europe’s tendency to greater regulate have any hidden benefits?
• The Robin Williams quote about Canada: “it’s like living in the apartment above a meth lab.”
• How America-first VCs will likely shoot their feet off with European companies and entrepreneurs
• Why I predicted that LinkedIn will likely follow the path to enshittification by following engagement as their north star

I’ll conclude by saying that the Future of Work has become one of my favorite topics. It started with my Clubhouse room (remember Clubhouse?) with Thomas Otter. That led to some ongoing collaboration with Thomas when he moved to become a partner at Acadian Ventures (which, by the way, is an investor in PeopleReign). That also eventually led, through introductions to the founders, to my joining the board of TechWolf, where I’m now learning about redesigning work, people-centric data platforms, and the skills-based organization. It’s a fascinating area, particularly here at the dawn of mainstream AI, and one that affects all of us.

Thanks again Dan for having me on the show and for a great conversation about the predictions and a whole lot more.

The post My Fourth Appearance on AI and The Future of Work appeared first on Kellblog.

• 2/3rds of an sales consultant (SC)
• 1/3rd of a sales development rep (SDR)
• 1/6th of a sales manager

With the chosen ratios varying as a function of your sales model. If we’re good, we might even include:

• 1/8th of an alliances manager
• 1/10th of a salesops person
• 1/12th of a sales enablement person

If we’re really good, and we have a large organization, we’ll also get the next layers of sales, SC, and SDR management.

But what’s the one thing that almost never comes up in these support ratio discussions? Demand generation (aka demandgen). Money for marketing to build pipeline for the incremental AE.

When we don’t treat demandgen as a ratio-driven, support resource, we get what I call the baby robin problem.

We throw our model out of whack by hiring more sellers than planned and thus everyone ends up with insufficient pipeline. The sellers turn into baby robins, mouths extended upwards, waiting for someone — e.g., SDRs, marketing, alliances — to drop opportunities in.

How can we avoid the baby robin problem? By treating demandgen budget as you would any other sales support resource. We instinctively think about SDRs and SCs (even if we don’t always go hire them). But we don’t do the same for demandgen. So part of this is self-discipline. The other part is math.

Let’s assume steady state, so we can ignore timing and ramping:

• If our AE has a quota of $300K/quarter
• And we want 3x pipeline coverage
• Then we need to generate $900K of pipeline each quarter
• If our pipe/spend ratio is a healthy 15:1
• Then we need $60K/quarter in demandgen spend per AE

That’s $240K/year. A lot more than 1/3rd of an SDR and 1/6th of a manager. Yet, we routinely model these lesser costs and forget the demandgen.

Why? Silos.

It’s a different budget. Oh, that’s marketing. But it’s sales that’s asking for incremental money to hire the seller. The marketing budget is someone else’s problem. Until you repeat this 5-10 times and now every seller is starving for pipeline. Then it’s everyone’s problem.

So how can you avoid this? I’ll say it a few different ways, so you can take your pick:

• Work together. Hiring incremental sellers is a go-to-market (GTM) problem, not a sales problem.
• Plan holistically.
• Treat demandgen like any other sales support resource.

The post Think of Demandgen Like Any Other Sales Support Resource appeared first on Kellblog.

I’d never heard it before. It sounded catchy. But, honestly, I didn’t know what it meant. At first, I thought it was VC-ism. But then I learned the phrase was coined by the venerable Howard Marks of Oaktree in one of his famous investor memos. So it’s really a finance-ism, not a VC-ism.

What does it mean? While Marks’ fourteen-page memo provides numerous clear, in-depth examples, I’ll try to capture the spirit of the question with one of my own. Consider two investments, A and B:

And let’s pretend they’re both made by a VC firm on your behalf. Firm A puts your $100 into a startup and one year later they sell the company and distribute $120 back to you. The internal rate of return (IRR) is 20%. That’s pretty good (e.g., compared to average stock market returns), but you were hoping for a long-term investment and now you need to find somewhere else to put your money. Firm B invests in a startup and sells it in year eight, getting you $350 back. The IRR is also 20%. But the total value to paid in (TVPI) is 3.5 compared to 1.2 with Firm A.

In essence, because IRR is based on time, if you can produce a nice return very quickly, you can get an amazing IRR. In my example, both investments produced a solid IRR of 20%, but in one case I had only $20 of profit to eat with, whereas with the other I had $250. (And this is why most investors prefer TVPI over IRR as their top metric. Why? Because you can’t eat IRR.)

I feel the same way about pipegen (aka, pipeline generation). Why? Let’s look at a simple example:

This company’s pipegen targets, presumably created using some funnel model, are in the first row. That model is typically some kind of waterfall where you take starting pipeline, add pipegen plans from the various pipegen sources (e.g., marketing, AEs, SDR, alliances), subtract closed/won opportunities, subtract lost and slipped opportunities, and make some adjustments for opportunity sizes varying around over time.

Actual pipegen, expressed as a percent of plan, is in second row. This is usually broken out by source, but I’ve aggregated them here to keep things simple. Note that if you stopped reading after row two, you’d pop the champagne. Go us! 105 to 109% of pipegen targets all year!! We’re the best!

And you’d be surprised how many companies do this. Sometimes they do it unknowingly. Once you break out pipegen by source, look at plan performance, and compare to prior quarters and years, you have a lot of numbers on a slide. So you tend to then look at the bottom and see total pipgen as a percent of target. If those figures are 100%+, then awesome. Pipegen’s not the problem. Next slide.

But that’s not good enough. What if, for whatever reason, that despite our strong pipegen performance relative to plan, that we’re not starting each quarter with sufficient pipeline coverage. Remember, there are only two high-level questions about sales:

• Did we give them the chance to make the number?
• Did they make the number?

Look at row 3. Starting coverage is short for starting pipeline coverage. That’s total current-quarter pipeline at the start of the quarter divided by the new ARR target for the quarter. Most CROs want this ratio to be 3.0x. Here, you can see we started every quarter with between 2.5x to 2.8x coverage. That’s not good enough. In short, it means that we’re not giving sales a fair shot at making the number each quarter. That might partially be sales’ fault. Normally sales is responsible for generating 20-40% of the pipeline (including SDR outbound). But it’s also the fault of marketing and alliances. Nobody should be drinking champagne.

Sure, we may have beaten the pipegen targets in our model, but something’s clearly wrong with our model if we can consistently miss starting coverage while exceeding pipegen targets. What might that be? Perhaps:

• We’re losing more deals so less pipeline is slipping
• Deals are shrinking, e.g., in response to price pressure from a competitor
• Sales cycles are stalled by a megavendor entering the space
• Deals are slipping more frequently and/or by more quarters than in the past
• Sales cycles are lengthening, so we’re generating enough pipeline but it’s all too far in the future
• There’s a math mistake in our model

There are a lot of different reasons this could happen. But the main point is don’t forget to look at row three in the example. That’s where the pipegen team can celebrate. They should need two triggers to open the champagne: first that we beat pipegen targets and second that we start with sufficient pipeline coverage.

And we only break out the caviar on the third trigger: when sales beats the new ARR number.

This post is similar in spirit to one I did last fall entitled, Why Great Marketers Look at Pipeline Coverage, Not Just Pipeline Generation. If you want more on this topic, then take a look at that post as well.

Finally, you now know why I say, “you can’t eat pipegen.” But you can eat starting coverage.

The post You Can’t Eat Pipegen appeared first on Kellblog.

The board can be a tailwind accelerating your career or a headwind slowing it down. Let’s talk today about how to make a good impression in board meetings and how to start building good relationships with the members of your board.

Here are six tips:

1. Lose the baggage. If you have authority issues or PTSD from prior board experiences, you need to lose the baggage. That may not be easy — and you may need a therapist to do it — but boards can easily sense passive aggression and inauthentic interactions. I worked with one CRO who viewed board meetings as a necessary evil, something to survive so we can all get back to work. If you feel this way, the odds are the board can tell. (Our board certainly could, and it limited his tenure as a result.)
2. Make your presentation from scratch. Bad board sessions start with bad slides. Usually, they’re too long and detailed. This typically positions the exec as either “in the weeds” (ergo too junior and in need of an upgrade) or “stonewalling” (i.e., deliberately making an overwhelming deck to stifle conversation). The path to hell begins in the slide sorter, so do not start there. Start with a blank outline to avoid the number one mistake — starting with what you have instead of what the audience needs. Doing so is a false economy and, for chrissake, it’s your board: if anyone deserves a custom presentation, it’s them. So make a custom slides, from scratch.
3. Cut to the chase. Boards are notoriously impatient. Individual sessions are usually pretty short. There’s no time to warm up with “How ’bout those Yankees?” or several introductory slides, including the tired highlights / lowlights slide. (That slide is appropriate once in a board deck, in the CEO’s update, and if there’s something particularly good or bad in a functional area, it should have already been raised there.) If we’ve had insufficient pipeline for the past two quarters, go immediately to the reasons why and the remediation plan. If you’re not sure what the hot issues are — itself a yellow flag — ask the CEO. Nothing will infuriate a board more than endless warm-up slides that don’t cover the important issues. Beware saying: “Great question, but we’ll get to that on slide 27.” With some boards, you may not still be employed by slide 27.
4. Make slides that facilitate discussion. Board members like to talk, so let them. Build slides that facilitate a discussion. That usually means first baselining the board with key facts and metrics. (Remember, they might sit on 5 to 10 other boards, so they’re not going to remember everything you talked about last meeting.) Then tee-up a discussion using techniques such as: (i) making a proposal and asking for feedback, (ii) outlining three options (if you really can’t decide) and requesting input on them, or (iii) asking three questions that will help you make the decision. Be authentic. Don’t propose three options if two are patently absurd. This wastes the board’s time and they’ll see through it.
5. ATFQ (answer the effing question). If, at any time during the meeting, the board asks you a question: answer it. Read this — among the top five Kellblog posts of all time — for advice on how to do so. If asked for your opinion, offer it. Don’t stare at the CEO and then toe the company line. The board is fully aware of the disagree-and-commit principle. They assume you’re committed. They’re asking if you agree.
6. Ask for relevant follow-up meetings. If you’re the CRO and one of your directors is a former-CRO, ask them for an offline meeting to discuss a hot sales-related topic. While you should spend most of that meeting discussing the advertised topic, you should take a bit of time to get to know each other and start building a relationship. Invite them to a coffee if you can, as opposed to a Zoom. Drive to their office if they invite you.

If you follow this advice, you’ll make a better impression on your board than most and you’ll start to leverage board meetings to set up offline conversations that will hopefully lead to a few career-long and career-changing relationships.

As CJ Gustafson replied when we discussed the idea of building relationships, “oh, you could find a relationship like Scorsese and DiCaprio.” Yes, exactly. That worked out pretty well for both of them.

The post Six Tips on Presenting to the Board of Directors appeared first on Kellblog.

I’m always humbled by the act of making predictions.  A few months ago, I was in London, sipping champagne at the BAFTA, improbably discussing the mast on Mike Lynch’s superyacht.  As I talked and sipped, with my mind already in 2025 predictions mode, I couldn’t help but think:  I’ve made a few predictions about Lynch in the past, but how could anyone have predicted this?

Life is indeed stranger than fiction.

On that note, let’s begin our eleventh annual Kellblog predictions post.  As always, I’ll review my 2024 predictions (with my generous self-scoring) and then make ten predictions for 2025.  This is neither business nor investment advice and this content is provided for information and entertainment purposes only.  See my FAQ and T&Cs for disclaimers.  See note [1] for my policy on political content.

2024 Predictions Review

1. Election dejection.  Hit.  The election certainly was distracting.  The media generally did emphasize “odds, not stakes” in their coverage.  My comment about “testing the once-veiled political neutrality of Silicon Valley,” was the understatement of the year with Elon Musk, A16Z, Sam Altman, the All-In Bros, and several others coming out in direct, vocal and fiscal support of Trump.  Andreessen’s take was the most interesting, effectively saying they made me do it, accompanied by explanations (some might say rationalizations) to justify their position as self-declared, single-issue voters.  In effect, to modernize an old argument, “what’s good for VC is good for America.”  In the end, I suppose it shouldn’t be surprising that when the president puts a For Sale sign on access, that some come forward as interested buyers of power.

2. A slow bounce back in startup land.  Hit.  I correctly called 2024 as a transition year where the Silicon Valley system would purge itself of recent excesses.  ARR growth rates continued to get hammered.  ARR multiples hovered around historical means, around half of what they were during ZIRP.  See these slides from Aventis.

While I was correct that 2024 would be a tough year, I was over optimistic in thinking we’d turn the corner.  I now think the bloodletting will continue in 2025.  AI will be a huge driver of the rebound, because of both the large VC investment it attracts and its ability to convert headcount budget into software budget (e.g., AI SDRs).

3. The year of efficient growth.  Hit.  Efficient growth was the watchword in 2024, with companies delivering increases in both profitability and Rule of 40 scores.  Again from Aventis:

Rule of 40 scores increased more moderately than profitability, a reflection of companies’ struggles with cracking the code on efficient growth. 

Investors did increasingly look at ARR/head as an overall efficiency measure.  Bessemer’s new Rule of X gathered momentum as a key SaaS metric because it better accounts for the ~2.2x greater importance of growth over profit in explaining valuation.   

4. AI climbs the hype cycle.  Hit.  While I’m not sure this requires explanation, I’ll share two observations.  First, per Pitchbook, AI grabbed 36% of VC deal value in 2024 on its relentless upward march.  Frankly, I’m surprised that figure wasn’t more than 50%.

Second, in the enterprise at least, I think Salesforce made the launch of the year by doing what they do best – neatly packaging industry evolution into a simple three-part message and broadcasting it to the world.  I’m not talking about technology innovation; I’m talking about the service they perform for the market by widely broadcasting key positioning messages including (a) we are in the agentic era of AI (the previous two being predictive and generative) and (b) it’s safe for enterprises to get into the AI water. 

Great marketers remove fear from the equation in new technology adoption.  While profit-motivated in the macro, enterprises are risk-averse in the micro because executives literally bet their hard-earned careers on the success or failure of new technology projects.  Credible announcements from enterprise leaders do far more to grease the skids of enterprise adoption than the endless, ever-inflating prognostications from Sam Altman, whose views were summarized by one critic as, “we are now confident that we can spin bullshit at unprecedented levels, and get away with it.” 

5. AI-driven GTM efficiency.  Hit.  There has certainly been an explosion in AI-powered, go-to-market tools from startups.  Mega-vendors, keenly aware of the deadly potential of disruptive technology, have not been caught flatfooted, either.  (That’s the often-ignored, second-order effect of everyone now having read The Innovator’s Dilemma.)  While I don’t think we have yet captured Battery’s 30% increased efficiency target, I believe we will in 2025, particularly for more mature SaaS businesses.  New and AI-driven SaaS businesses will likely be investing so much in growth that it will be hard to see those same-store sales productivity increases when they are mixed with the large investments in new capacity (which is a fundamental limitation of the CAC ratio as a growth efficiency metric).  But, overall, I think we are well on the way to achieving the GTM productivity improvements promised by AI GTM tools.  Keep investing in them and experimenting with them.  What’s a competitive advantage one day is often table stakes the next.

6. Beyond search.  Hit. To paraphrase REM, it’s the end of the (Internet search) world as we know it.  This MIT Technology Review article does a great job of explaining the evolution of search and how conversational interfaces are replacing the search box and generated answers replacing lists of links.  This will have a profound  impact on businesses that rely on Internet search for traffic, leads, and customers, from publishers to e-commerce providers.  And it will impact any business that relies on digital marketing, such as paid search, SEO, or content marketing.  So, basically everyone. 

Marketers should understand these impacts and get ready for a future of zero-click marketing.  While HubSpot’s SEO crash recently made headlines, I agree with Kyle Poyar that they’ve adequately hedged themselves against this.  The question is, of course, have you?  Lest all this sound too scary, I offer this excerpt from Rand Fishkin.

I believe that this is not an apocalypse for digital marketers.

These are important things that we need to consider, and we need to, as a result, invest in zero click kinds of marketing and change our entire thought process around what we’re doing online with digital marketing. But influence has always been better than traffic. Traffic was always a vanity metric. I love my friend Wil Reynolds who posted this video about showing how their traffic, Seer’s traffic, his company’s traffic had dropped 40%, and it seemed like the end of the world, but sales were up 20% because traffic is not the same as conversions. Traffic is not the same as customers. Traffic isn’t even the same as fans.

So, like REM, I feel fine.

7. From RAGs to riches.  Hit.  I like RAG because it’s a practical approach that solves or mitigates key problems with LLMs (e.g., hallucinations, explainability, sourcing), all while leveraging their tremendous power.  In 2024, I think RAG established itself as a cornerstone technology for enterprise AI.  These two posts provide a detailed review of RAG’s progress in 2024.  Menlo’s The State of Generative AI in the Enterprise report shows RAG as the dominant and fastest-growing design pattern in enterprise AI.

8. Outbound finds its proper place.  Hit.  I think that companies, assisted by the rapid adoption of AI SDRs (e.g., Piper), are increasingly figuring out some key truths about SDRs. 

• Inbound SDRs are an extension of marketing and, due to their fairly rote work, are increasingly being replaced by AI SDRs.
• Outbound SDRs are an extension of sales and, due to their relatively complex work, are not easily replaceable by AI SDRs.
• Unfocused outbound is generally an unproductive activity.  You are better off investing in inbound and partners if you don’t have defined, high-value targets.
• Outbound SDRs are best used as part of targeted account programs, such as ABM, aimed at high-value customers.  Think: is the juice worth the squeeze?

9. The reprise of repricing.  Partial hit. The best data I’ve found here is in a report from Carta, which suggests that I was a year late:  option repricing appears to have peaked in 2023.  That said, this chart contains only one quarter of 2024 data.  The 2H24 version of this report should be out soon, so we’ll know more in a few weeks.  Either way, if your company is still digging out from valuation overhang, it’s never too late to consider repricing.  Look at last year’s predictions post for more.

10. Peak podcasting.  Miss.  Podcasts continued their upward march in 2024.  While I’d argued that podcasts would peak in 2024, both market forecasts and industry trends suggest that podcasts will continue to grow in the years to come.  The demise of Internet search and the associated need for companies to build their own first-party audiences will drive podcasts to grow in importance.  While I’d written that 2024 might be the last good year to start a business podcast, I think 2025 will be a good one as well.  So, if you don’t have a business podcast yet, think about starting one.  Just make sure you produce good content.

Kellblog Predictions for 2025

1. America gets what we deserve.  We voted for it, both via the electoral college and the popular vote, so we’re going to deserve what we get.  That will include:

• A more brazen, more conflicted, and less constrained Trump. In short, we’ll see “Trump, Unbridled.”  The unlikely bedfellows that elected him will discover exactly what they ordered and exactly who the administration is going to serve.  Trump will face less resistance on both the internal front (i.e., intra-party, intra-staff) and external front (i.e. Democratic).  Decreased internal resistance will result from fealty-based screening and fear-based leadership, making quick examples of those who step out of line.  Decreased external resistance will come from a mix of advance obeying, a sense of futility, and continuous (if incorrect) mandate rhetoric.  If the Democrats brought knives to a gunfight last term, this time they’re bringing cupcakes. 
• A more divided country.  I’d initially thought the more brazen approach would result in buyer’s remorse, but I now think it’s more likely to result in increased division, with supporters doubling down in response to each fresh outrage.  Aided by a more fearful and less hostile media, Trump’s apologists may need to contort to new degrees, but they will invariably support virtually anything he says or does.  Thus, the country’s divide will widen, with one side believing that we’re making the tough decisions needed to restore America’s greatness and the other thinking we’re destroying many of the things that made America great in the first place. 
• A more distracted country.  I think of the government like plumbers.  I have little interest in what they do and how they do it.  I don’t view plumbing as a spectator sport.  I just want things to work.  But we have now signed up for four more years of stunts, boasts, bluffs, brags, parade jumping (e.g., Stargate), hyperbole, constitutional crises, and trial balloons.  Trump is a master at centering attention on himself, has turned shamelessness into a superpower, and paralyzed the traditional media in the process.  I’ve always been surprised that we haven’t seen clear opportunity costs associated with all this distraction.  In 2025, I think we will.     

Get used to hearing “unprecedented” a lot.  It, as was once said, will be wild.

2. The broligarchs enjoy their 15 minutes of fame.  For some, the agenda was preemptive defense.  For others, a desire to deregulate AI, crypto, or big tech M&A.  For a few, a chance to grab power and live in the spotlight.  For many, the ideological pursuit of sci-fi-inspired visions. 

We know who the broligarchs are and why they’re here.  A surprising number hail from the PayPal mafia. We know that they’ll all get their 15 minutes of fame.  The big question is how long will they last? 

Given Trump’s mercurial personality, the revolving door of “best people” in Trump’s inner circle, the sizes of the various egos, and the fact that the broligarchs are all much smarter than Trump, I think the general answer will be:  not long.

For every person who hangs on, I think we’ll generate several Rex Tillersons who don’t.  As a reminder, while his “fucking moron” quote was never publicly confirmed, here’s what Tillerson did say about his experience:

“It was challenging for me,” he said, “coming from the disciplined, highly process-oriented Exxon Mobil corporation, to go to work for a man who is pretty undisciplined, doesn’t like to read, doesn’t read briefing reports, doesn’t like to get into the details of a lot of things, but rather just kind of says, ‘This is what I believe.’ ”

It won’t be easy for the data-driven tech bros to handle such arbitrary decision-making.  But to steal a line from Airplane:  “they bought their tickets, they knew what they were getting into.  I say let ‘em crash.”

I think the odds of any given individual hanging on will be an inverse function of their desire for power.  The more they’re conducting business as usual and simply looking out for their firm’s or industry’s interests (e.g., Cook, Bezos), the longer they should be around.  The more they’re trying to work in the inner circle (e.g., Musk, Sacks, Ramaswamy), the shorter. 

Heck, Ramaswamy couldn’t even last one Scaramucci before getting blown out. While Musk’s $270M may have bought himself a longer tenure, featuring multiple lives, we’ll see how many times he gets to embarrass Trump before being blown out himself.

3. The startup ecosystem purge continues.  As mentioned above, I think the cleanse that started in 2024 will continue into 2025.  In many ways, startup investing is like playing craps.  You play for a long time, accumulate bets on the table, and either win slowly as different bets pay off at different times – or lose a lot all at once when the shooter rolls a seven. Personally, I’ve never had more angel investments sell, cease operations, or return money than I have had in the past 12 months.  There are two opposing forces in play: cash reserves and exit multiples.  I think that many startups have strategically decided to sell, but don’t want to start a process in what’s clearly a buyer’s market.  Look at these feeble M&A-specific exit multiples from Aventis:

Thus, many startups are tightly managing their cash reserves to buy time and hopefully sell into a better market.  I believe that as multiples start to bounce back many of those in waiting will be able to achieve their exits.

4. Attention is the new oil.  In 2006, Clive Humby, coined the phrase “data is the new oil,” to suggest that data would power the information economy in much the same way as petroleum powered the industrial economy.  Today, I think we can replace “data” with “attention.”  In his upcoming book, The Sirens’ Call, Chris Hayes argues that every single aspect of human life is being reoriented around the pursuit of attention.  Attention is a kind of resource, he argues, it has value, and if you can seize it, you seize that value.   

This harkens back to Jeff Hammerbacher’s 2011 quote, “the best minds of my generation are thinking about how to make people click ads.”  Today, you might update that with “click anything,” as best demonstrated by the bizarre game Stimulation Clicker, which ends up part game, part real-life reenactment, and part parable.

We are moving, Hayes argues, from the information age to the attention age.  The masters of attention, such as Trump and Musk, already understand this and are leveraging it to their advantage.  The rest of us need to learn how to play the game, both on offense and defense.  I think that will accelerate in 2025.

5. The world wide web, as we knew it, is dead.  Born: 1989.  Died: 2024.  The original web vision was for an open, world wide network of hyperlinked content, freely accessible to all. 

That worked until the information wants to be free crowd got (rightfully) squashed by paywalls to protect creators.  Then Web 2.0 came along, creating a read/write web, with user-generated content, so that individuals could not just read, but publish and share content without requiring any technical skills.  The mobile explosion extended connectivity but undermined the vision as applications and app stores (with their heavy platform fees) replaced web browsing and websites – resulting in oddities such as the inability to buy an e-book in the iPhone Kindle application.

In recent years, platform providers (e.g., Twitter, LinkedIn) declared war on the hyperlink, unapologetically downranking content that included links beyond their walled gardens. Google’s ever more ambitious front-running (e.g., featured snippets, AI-generated answers) provided the final nail in the coffin, decimating search traffic, and replacing it with the zero-click search.

When was the last time you saw an in-line hyperlink, particularly on a corporate website?  Why are newsletters and Substack replacing blogs and WordPress?  Why do people bury links in comments and replies?  Why can’t WordPress auto-post to Twitter?  Why did Seatguru stop updating its content years ago?  Why are sites like SlideShare so ad-laden as to become unusable?  Why, when I have 20K+ followers, do I have posts that get only 500 views?  In a world with algorithm-driven feeds, what does “follow” even mean anymore?

These are the death throes of the world wide web.  Why must platforms invariably undergo enshittification?  It’s the tragedy of the commons all over again.

While web 3.0 and a Read/Write/Own paradigm is theoretically coming to save us, I’m not holding out much hope.  As interesting as some of those ideas are, Web 3.0 strikes me as too much of a hodgepodge of agendas and ideas.  I think the current web 3.0 (which is actually web 3.0 v2) has roughly the same odds of success as its predecessor, web 3.0 v1, aka, the semantic web.

So, for now, I think we’ll remain stuck in the Hotel California era of the web: you can check out any time you like, but you can never leave. 

6. Working for the algo.  You hear a lot of concerns about AI replacing jobs.  But I’m also concerned about something else: about us working for algorithms as opposed to algorithms working for us. 

What do I mean?  Have you ever:

• Taken actions to improve your credit score?
• Put “link in bio” in an Instagram post?
• Followed LinkedIn hygiene tips in an attempt to go viral?
• Avoided certain topics to avoid getting de-boosted?
• Jiggled your mouse a bit while working from home?
• Executed a two-factor authentication (2FA)?

Then congratulations.  You’re not working for the man.  You’re working for the algo. 

CAPTCHA is my favorite perverse example because you have a human trying to prove to a computer that they’re human.  Or 2FA, where you have a human trying to prove to a computer that they are who they say you are.  Ponder that for a second.

In 2025, I think we’ll increasingly be working for the algo.  When I’m performing transactional tasks, I already feel like I’m spending as much time on CAPTCHA and 2FA as on the tasks themselves.  And I definitely dislike the menial work I do to tune my content for maximum reach. 

Working for the algo isn’t necessarily bad.  But it does pose a lot of questions about who is making or tuning it.  Bluesky’s custom feeds are one approach to solving one of the many problems here.  Passkeys help with security.  I’m sure we’ll see other solutions arise as well.

7. The death of SaaS is greatly exaggerated.  Satya Nadella made headlines with a three-minute commentary during a recent BG2 podcast appearance which many translated to:  SaaS is Dead!

While this is a somewhat fashionable thing to say these days, let’s first look at what Satya actually said:

“Yeah, I mean, it’s a very, very, very important question, the SaaS applications, or biz apps. So let me just speak of our own Dynamics. The approach at least we’re taking is, I think, the notion that business applications exist, that’s probably where they’ll all collapse, right in the agent era, because if you think about it, right, they are essentially CRUD databases with a bunch of business logic. The business logic is all going to these agents, and these agents are going to be multi-repo CRUD, right? So they’re not going to discriminate between what the back end is. They’re going to update multiple databases, and all the logic will be in the AI tier, so to speak. And once the AI tier becomes the place where all the logic is, then people will start replacing the backends, right?”

Translating the surprising amount of technobabble, he’s making an old-age argument that a business application is “just” a UI tied to a database with some business logic, the implication being:  how hard can that be?  Workday’s $70B, Salesforce’s $330B, and SAP’s $330B market caps all say “pretty hard” to me.  Or, if not technically “hard” per se, that there’s nevertheless a lot of value in tying those things together.

Satya builds upon this to say that the business logic can now be handled by agents, again a repackaged argument that once was made about rules engines, business process automation, and low-code development tools and one that trivializes the domain expertise built into business applications.  I think the quote says more about Microsoft and their worldview than it does about the future of business applications.

To bring some data to bear here, I found this interesting chart in this excellent deck from Aventis, which asks companies what inning SaaS adoption is in at their firms.

I think the best short answer I’ve seen to this question comes from Jason Lemkin:

To paraphrase Mark Twain, reports of the death of SaaS have been greatly exaggerated.

8. An unlikely revival of branding.  In an era of efficient growth and highly scrutinized marketing budgets, it’s surprising to predict a revival of branding.  But I think one’s coming because I’m increasingly hearing statements like:

• We’ve spent the past two years optimizing pipeline generation efficiency.
• Now we need to focus on winning more deals.
• SaaS products are increasingly lost in a “sea of sameness,” and we are thus unable to differentiate at a product level.
• Branding is therefore the last bastion of differentiation
• So, we need to win deals based not on product superiority, but on brand value and experience.

In short, since we can’t differentiate our product, we need to differentiate our company.

I have two problems with this logic:

• As someone raised in product marketing: you can always differentiate your product.  If you can’t, it’s time to turn in your marketing badge and gun.
• As someone with a child who works in CPG:  if my daughter can differentiate fermented milk (i.e., yogurt), then we should darn well be able to differentiate a complicated piece of enterprise software. 

But I do understand how a demandgen-oriented CMO – as most are these days – could get caught up in this logic.  So, before you embark on a branding program, ask yourself three questions:

• Are you sure you can’t increase your win rate the old-fashioned way — through product marketing and market research (e.g., win/loss analysis, sales enablement, sales training)?
• Are you guilty of Law of the Hammer bias?  Is branding the right solution, or are you simply more comfortable working on branding than product marketing?
• Do you have the time and money required to complete a successful branding program?  Will your tenure as CMO be long enough to see the fruits of your labor, or will your successor send you a posthumous medal of honor for your contribution?

Whether done for the right or the wrong reasons, I think we’ll see a revival of branding campaigns in 2025.  If you’re doing one, make sure you’re in the first group, by ensuring that you’ve exhausted product marketing solutions to the problem.

9. PR is the new SEO.  It turns out that one of the best ways to optimize inclusion in ChatGPT results is, per Rand Fishkin, “getting your brand mentioned alongside the right words and phrases in authoritative media.

In other words: PR. 

Here’s a link to Rand’s five-minute explainer video.

I guess that if you live long enough, everything comes full circle.  This is good news for marketing teams that kept an active PR function and agency during the dark times.  It’s bad news for those who turned off PR and will now need to restart from scratch.

There are numerous techniques that marketers must learn to build their LLM optimization skills while still running traditional SEO programs in 2025.  Here are a few of the better articles I’ve read on the topic.

• Emily Kramer’s post summarizing an interview with Flow Agency
• FirstPageSage’s guide to ChatGPT optimization
• PenFriend’s in-depth post on LLM optimization

10. LinkedIn enters the social media death cycle.  LinkedIn is at a fork in the road.  With users fleeing other social networks (e.g., Twitter, Facebook) and trolls, bot-nets, and the like wanting to increase their reach, there is an increasing amount of non-work content on LinkedIn.  For example, jokes, memes, snark, and political content.  And that’s not to mention the gray zone content where business leaders are making political commentary.

This trend has not gone unnoticed by users, and I think they generally don’t like it.

LinkedIn can go down the usual path to enshittification, relying on engagement as their North Star metric.  Because this content is highly engaging, the engagement scores are through the roof: look at the numbers in this screen clip.

The problem is, of course, by allowing and amplifying this highly engaging content, you get more engagement, right up until the point your site becomes a hellscape and nobody wants to use it anymore (e.g., X).  Then the hapless platform provider finds that network effects also work in reverse:  the more your friends stop using a site, the less incentive you have to go there.

Or they can make the tough decision and focus on their original vision, purpose, and positioning: a social network for work.  While they have taken modest steps, such as a feed preference to turn off political content, the features simply don’t work.  If they want to preserve their status as the social network for work, they’ll have to do much, much more.  And that’s not to mention getting core work social network functionality, such as job seeking, to work properly.

While I think they’re a smart organization, the sirens’ calls of engagement are strong.  I’m predicting that in 2025 they only take half-hearted measures to preserve their positioning and thus enter the social media death cycle.  Some would argue they’re already in it.

Thank you for reading all the way through.  I hope you’ve enjoyed this post, and I wish you a happy and healthy 2025.

# # #

Notes

[1] As described in my FAQ, I generally avoid political content on Kellblog unless I’m using politics to illustrate strategy and messaging.  My annual predictions post is an exception because I like to start at the macro level and, particularly in recent times, that will likely involve some intersection of business and politics.  Instead of attempting the impossible (i.e., pretending to be neutral), I will allow my views to leak out in the process but, rest assured, I’m not trying to change your mind about anything.  Note that I’ll delete comments that try to engage in political conversation as opposed to comments about the predictions themselves.  If you are interested in my broader views, you can follow me on Bluesky where I post on a broad range of topics (largely just sharing and commenting on what I read) including SaaS, VC, Silicon Valley, strategy, politics, current affairs, France, the Grateful Dead, databases, humor, and others.

The post Kellblog Predictions for 2025 appeared first on Kellblog.

In this, my second appearance on ProducTea (my first one is here), Leah and I have a pretty wide ranging discussion. Topics include:

• Understanding the Dual Role of a CMO
• The Ownership of Growth: A Collective Responsibility
• Organizational Design: Indicators of Health and Conflict
• The Complexity of Metrics and Data Overload
• Cross-Functional Collaboration: The Role of C-Level Executives
• The Importance of Internal Curiosity
• Lessons from the GE Hawthorne Experiments
• Feedback and Communication in Organizations
• Five Principles of Organizational Design
• Designing for Healthy Conflicts
• Rethinking Marketing Qualified Leads (MQLs)
• The Role of Incentives in Organizational Success
• Establishing Fail-Safe Processes
• Challenges Faced by Smaller Companies
• The Need for Humility in Startups
• The Complexity of Large-Scale Projects
• Understanding Time vs. Money in Enterprise Sales
• Practical Advice for Implementing Change
• Managing Big Projects Effectively
• Staying Alive in the Competitive Landscape

The episode is available on Spotify, Apple, and YouTube, with the video embedded below.

The post Appearance on ProducTea with Leah Tharin appeared first on Kellblog.

I’m talking about the much more difficult case where the company is performing well financially, but the CEO sees storm clouds on the horizon that indicate the future will quite probably not be as rosy as the past.

Four things combine to make this a difficult conversation:

• There is no data to suggest the firm is in trouble. In fact, the data suggests quite the opposite, that the future is bright. Think: “You’re on the same trajectory as our best-in-class firms.”
• The storm clouds are unfavorable trends, not performance problems. But by the time those trends manifest as performance problems, it may well be too late to get a decent valuation for the firm.
• Your viewpoint will likely be interpreted as self-interested. “What Joe’s really saying is that he wants to cash out.” Or, “Joe’s lost faith in the company because he’s lost confidence in his ability to lead it.” Conclusion: “We need someone running this company who believes in its future and who is capable of taking it to $250M.” Adios Joe. Even though poor old Joe never actually said either of those things.
• The “hits” nature of the VC business. Selling a winner early can have a devastating impact on the IRR of a fund (e.g., removing the top two performers can change a fund’s IRR from 35% to 12%. Think: “There’s no way we’re taking a double on this investment when I think this can be a home run. I’d rather get thrown out sliding into third.”

The incentives are all wrong. The VCs can’t see the problem in the data because it’s not impacting performance yet. They often don’t want to see the problem because the company may be one of a few winners in the portfolio that they’re hoping will return the fund. It’s very easy for the CEO in this situation to get fired. Think: “If I believed this could be a $1B company despite these storm clouds, then I’d want to fire me, too. But I don’t.”

The ultimate problem is that you can’t see around corners when you’re only looking at performance metrics. Even leading indicators like pipeline won’t be leading enough to show these problems. The best chance of seeing the problem in the data is if a few of your earliest, most visionary customers have moved onto the new thing. But due to switching costs, this doesn’t often happen. Other companies, who skipped your generation of technology, are more likely to be the first adopters of the new, new thing.

For founder/CEOs, recognizing the situation is even more difficult than for hired CEOs because:

• The founder’s inbuilt reality distortion field. Founders are gifted at willing the world into what they want it to be. This is awesome for driving disruptive visions. It is dangerous when used to imagine away a harsh reality.
• The “they said we couldn’t get this far” fallacy. Founders have been told so many times by so many people that, “you’ll never be able to do X” that when faced with stated obstacles they immediately rationalize them away. They said we’d never get PMF. They said we’d never get to $1M in ARR. They said the megavendors would crush us. They said we’d never be able to hit $10M. They said we’d never be able to sell enterprise. They said we’d never get to $50M. Most of the time, this obduracy is helpful. Except for the one time when “they” are right.

The are two morals here. First, founders need to try to objectively evaluate facts and disable their knee-jerk, “they said we never could do X” reflex. Second, boards — knowing how difficult it is for a founder to reach the conclusion that they may be in a Kobayashi Maru scenario — should immediately perk up when they hear a founder say that they might be.

To make this concrete, let’s provide a few examples of these storm clouds. I’m not talking about a squall here, I’m talking about clouds that presage a full-blown typhoon:

• Megavendors entering the category. Worst case, they give away their equivalent of your product to sell their own core product. But even when they sell an entry in your category, they may do so at a lower price. This begs the questions: How much is your differentiation really worth? At what price point can you still compete? How much is your neutrality worth compared to the integrated solution? Examples: Microsoft Power BI, Databricks Unity Catalog.
• Omission from a category consolidation. When a best-of-breed category is transformed into a suite by the market leaders, such as the integration of query, reporting, and OLAP in the 2000s, or the integration of conversation intelligence, forecasting, and sales engagement today. Actuate was left behind the first example, Fireflies might well be in the second. In rare cases, the neutrality of vendor independence is valued (e.g., Informatica) but, most of the time, the standalone solutions end up neither best-of-breed nor integrated.
• Category superannuation. The category is crushed by indirect competition that replaces it. For example, what many see AI doing to RPA today. Or MarkLogic (an XML database), which was superannuated by NoSQL. You don’t get beaten by a direct competitor, but simply replaced by a new category of software that does what you do as a subset.
• Imminent commoditization. While lazy marketers bandy this term about too freely, sometimes categories do commoditize either in the sense of a lack of differentiation among alternatives or, more commonly, price pressure brought about by loss-leader entries from megavendors or open source alternatives. You still offer something of value, but customers can get roughly the same thing elsewhere for free or nearly so.

Why do I call this a paradox? Because, unless you’re a founder with control provisions, it’s very easy to get fired just by starting the conversation. Can you ever tell the board that it’s time to sell a high-performing company without getting fired for doing so?

Having failed at this once myself, “perhaps” is my best answer. Here’s what I would recommend to maximize your odds of being able to successfully lead this conversation:

• Get the technical founder(s) on board first. If they don’t believe the industry changes are an existential threat, the whole debate may be impossible.
• Go slowly. Don’t drop the whole idea on the board in one fell swoop. Instead, signal over a series of board meetings that there is a potential storm brewing. This isn’t always possible (e.g., a surprise megavendor entry), but it usually is.
• Lead the horse to water. Ask questions, don’t make statements. Understand that saying, “I think we need to sell the company” is a bell that you cannot unring — so don’t say it. (Yet at least.) Use my formula for having meaningful strategic board discussions.
• Never signal any personal interests. Never talk about personal liquidity or your ability to scale the company, lest any comment made will never be forgotten. Always just talk about the business, its health, and its value.
• Bring data. Analyst reports, megavendor speeches, customer interviews, and buying intent surveys can all help show people what’s happening.
• Do business development. You do not need the board’s permission to build relationships with potential strategic acquirers, particularly when the context is strategic alliances, OEM distribution, or potential investment. (You should be doing this anyway.) This gets the lines open if and when the board does decide to explore options.
• Leverage personal relationships. Build and use your 1-1 relationships with board members to have these tricky conversations, particularly with the “lead” or “alpha” director.
• Be open to alternatives. If people raise valid pivot strategies, consider them openly. Never appear wedded to a given solution (e.g., sell the company) particularly when your ability to execute it is beyond your control.
• Leverage independent directors and advisors. Hopefully these individuals can be more objective in evaluating the firm’s situation and offer wise counsel on how to proceed.
• Watch your back. Try to detect if board members start meeting without you, e.g., by staying close to the founders or select board members you know and trust well.

I think selling a high-performing company in the face of a brewing storm is among the hardest tasks a CEO can undertake. Hopefully, in this post, I’ve provided a few good ideas for what to do if you find yourself in this situation.

The post The Paradox of Saying, “It’s Time To Sell The Company” appeared first on Kellblog.

So this year, I thought I’d share the top-ten Kellblog posts of 2024 (year to date) regardless of the year in which they were written.

1. Kellblog predictions for 2024. My tenth annual predictions post topped the list. I’m already working on my 2025 predictions which I hope to publish before the end of December.
2. What it really means to be a manager, director, or VP. Written in 2015, this continues to be a top post every year and, as a result, is the all-time #1 Kellblog post.
3. The top 7 marketing metrics for a QBR or board meeting. A 2023 post I wrote after a friend asked: “blank slate, what 5 metrics would you present to the board?” I cheated and did 7.
4. The key to dealing with senior executives: answer the question. Another perennial favorite, this 2012 post is the one people mention to me the most. Think: “I forwarded that to my team!”
5. The one question to ask before blowing up your customer success team. The first 2024 post on the list, I wrote this to encourage people to take a minute before Slootmanizing their CS department.
6. Demystifying the growth-adjusted enterprise value to revenue multiple. This 2024 post explains the metric and, in a quest for syllabic parsimony, suggests naming it the ERG ratio, after the PEG ratio.
7. Go-to-market troubleshooting, let’s take it from the top. If you’re chronically missing new bookings plan, then read this 2024 post and listen to the SaaS Talk episode that covers it.
8. Target pipeline coverage is not the inverse of win rate. I saw one too many people invert their win rate to set pipeline coverage targets and wrote this 2024 post to show them the error of their ways.
9. Simplifiers go far, complexifiers get stuck. This classic from 2015 starts with a poignant joke. Question: What does a complexifier call a simplifier? Answer: Boss. Learn why by reading it.
10. Playing to win vs. playing to make plan: the two very different worlds of Silicon Valley. This 2024 post explores how the valley has fractured into somewhat distinct VC- and PE-backed worlds.

Keep an eye out for my 2025 predictions later this month. And thanks for reading.

The post The Ten Most Read Kellblog Posts in 2024 appeared first on Kellblog.

Why?

• As a quote from the winner, it’s almost always survivor bias. “How’d I win? Well, I ran my own race.” Well, I’m sure most of the losers ran their own race too, but nobody’s interviewing them for pearls of wisdom.
• It’s frequently used to imply that you should ignore the competition. “Oh, I didn’t worry too much about the other runners, I just ran my own race and stuck to the plan.” The part they leave out is that the plan was designed to beat the other runners. Sticking to the plan was all about the other runners, even if reacting their every move wasn’t part of it.
• It sounds oh-so-good to say. So focused. So above the fray. So wise. So unencumbered by the competitive market. And it paints such a pretty picture: just run your own race, don’t get tangled up in distractions, and you will win.

Look, it’s not a bad response to an interview question. It’s arguably a very good one. But it’s rotten management advice. “Run your own race” isn’t strategy. It’s solipsism.

Sure, if you’re running a race recreationally, by all means run your own race and I hope you have a great time.

But if you’re in business, if you’re running a startup and trying to apply this advice, then you need to consider yourself an elite competitor. You are running to win. So, if running your own race means sticking to your race plan, and that race plan is a plan to win and one that you can execute, then go ahead and run your own race. Because, by transitivity, it is a plan to win. Which is one definition of strategy.

One thing I dislike is when great executives from great companies who were in unique situations talk about “running your own race” as if everybody should. Don’t be too focused on the competition. Just focus on customers. The rest will take care of itself.

And it will — if you’re, e.g., Tableau and it’s 2010. They had an amazing product, a great team, a greenfield market — and virtually no viable competitors. They ran their own race for a long time, and it worked. But just because they could run their own race doesn’t mean that you can run yours. If you’re the number four vendor in a high-switching cost, platform market, then running your own race is more dangerous than being an Alaskan crab fisherman.

In some deep vertical SaaS markets, I’ve found companies that can ignore the competition and run their own race. That’s either because there is no competition or it’s a handful of low-growth, bootstrapped companies in a space where everyone can achieve their objectives without smacking into each other too much. (Think: there’s enough market here for everyone.)

But if you don’t happen to be in that situation. If you’re VC-backed, you’re definitionally playing to win. If you’re PE-backed you’re either playing to win or playing to make plan. (Or win, if you define “winning” as making the plan that gets your investors a 3x in 4-6 years.)

Startups are a competitive sport. Exciting new spaces attract numerous brilliant teams to fight for the emerging market. Too many retired executives forget this, preferring to dish out nostalgia over practical advice. Markets are composed of deals and deals are often streetfights. There’s big money at stake in Silicon Valley startups and lots of management teams come playing to win. Don’t bring a knife to a gunfight.

This is not the environment to run your own race, focus on your world-changing vision, and ignore the competition. This is the environment that calls for strong execution of competitive strategy to win deals and company strategy to overcome your largest obstacles.

Sure, you can run your own race. As long as your plan is the plan to win and within your ability to execute. Then go right ahead. Otherwise, make a new race plan.

The post Please God, Don’t Run Your Own Race appeared first on Kellblog.

I understand that, in the throes of budgeting, CMOs can get dragged down into a lot of detail. Diving to a deep level of detail is important, because that’s usually the difference between a real plan and a basic budget.

But, remember people: when we’re talking to the board, we need to be board level. Otherwise, they’re going to mistake you for the VP of marketing operations. (Was the CMO out sick today?)

The board doesn’t want:

• Vapid marketing cheerleading, particularly if the company is missing plan
• Overwhelming volume (e.g., 28 slides with a 15-slide appendix)
• “Banker slides” that overload them with numbers
• Recycled QBR slides, built for a different audience and purpose

While I’m all in favor of a few introductory slides that present current-year marketing performance, they should be sober and matter of fact. Too often, when CMOs try to present such slides, they end up sounding like this:

So what does the board want?

• A short deck, maybe 5-8 slides (with a slide on 2024 performance, a list of key objectives, an organization chart, and an overall budget)
• Some slicing-and-dicing of the demandgen budget that discusses both coverage and efficiency
• Slides that are custom built for the board audience

And what are the questions that are actually on their mind?

• What are marketing’s key objectives for the year? Do they align to corporate strategy? Do they align to sales? Are they the right objectives?
• Where did the budget come from?  Was it trended off last year or built from a bottom-up model?
• If it was trended, is the total spend growing slower than revenue? Could it be growing slower still? Should it be growing faster?
• If it was built off a model, who built the model? Are they any good? Is there a single model for sales, marketing, and finance, or is there a cage fight behind the scenes? Can we hit plan if we rely on this model?
• What does marketing spend look like as a percent of revenue? As a percent of new ARR bookings? Are those percents going down over time? How do they compare to benchmarks?
• What is our CAC ratio and CAC payback period? How much is marketing contributing to each? Is marketing’s relative contribution going down or up?
• And if they’re good, what is the sales/marketing expense ratio and how has that trended over time? How does it compare to industry benchmarks? On whose back are we placing the GTM efficiency monkey, and what risks does that entail?
• Where does the CMO want to spend the marketing money?  How much is going to people vs. programs vs. infrastructure? How has that mix changed over time?
• Is there any marketing money outside marketing? Does the CEO carry a pet-projects budget for billboards? Do we run a massive user conference? If that money’s not in the budget I’m looking at, then where is it?
• Do the CRO and CMO seem aligned on the marketing budget and priorities? If not, where do they differ? Does the CMO seem caught in the middle between CEO and CRO priorities?
• Does the company have an overall model for who generates how much pipeline? That is, pipeline generation targets by pipeline source (aka, “horseman”) by quarter?
• Has each pipeline owner accepted clear responsibility for their portion of the pipeline and a have a clear plan to deliver it?
• Does marketing have a plan for how they are going to spend the proposed demandgen dollars? Can I compare that plan to our historical performance to see if it’s realistic?
• Is marketing focused solely on pipeline generation or do they also worry about pipeline coverage?
• Does the marketing plan show pipe/spend and cost/oppty ratios? How does the plan compare to our historical performance? Are we increasing efficiency? Is that spreadsheet magic or are there actual reasons why those ratios should increase?
• Where are we looking at using AI to improve marketing efficiency? What are we experimenting with? How big an improvement can we expect? Have we looked at AI SDRs?
• How much money is going into squishy things like branding? Can the CMO defend that proposed expense? Do the CEO and CRO agree that this squishy spend is a priority?
• Can I trust the CMO to execute this plan? If we give them what they ask, will they deliver on the pipeline generation goals and key objectives?

I’m not suggesting that you proactively answer each of these questions in your eight slides. But these are the questions you should be ready for. In terms of how I’d map these to slides:

1. Current-year marketing performance. Metrics on the left, OKRs on the right.
2. Next-year proposed OKRs.
3. Next-year proposed organization chart.
4. Top-down S&M analysis, e.g., CAC, CPP, sales/marketing expense ratio, history, benchmarks.
5. Top-down marketing budget analysis, e.g., spend by people/programs/infra, headcount, total cost/oppty.
6. Overall pipegen and coverage model, e.g., targets by horseman, how pipegen ensures coverage
7. Demandgen budget analysis, e.g., spend by channel, pipe/spend, DG cost/oppty, coverage.
8. Menu of 3-5 optional programs with benefits and costs — i.e., try to sell the top ideas you couldn’t fit into the baseline plan in a quest for incremental money.

(Edited 12/2/24 at 9:04am to include last section on slide mapping.)

The post Board-Level Questions On The Marketing Budget appeared first on Kellblog.

I love when a process works.  This started with an introduction from a trusted friend and category expert, Thomas Otter.  I met with the founders now and again over the years, via the odd Zoom or a coffee on California Avenue when they were in town.  I like this go-slow approach because you get to know the team and the company.  You watch them grow.  You stay in touch.  And then one day an opportunity to work together more formally appears.

Now, let’s talk about what I like about Techwolf:

• The founders, Andreas, Jeroen, and Mikaël.  Independent directors (known in Europe as non-executive directors) are more about coaching than governance.  Thus, you need great chemistry with the founders.  Your skills need to complement theirs.  And they have to want to learn from you. 
• The story.  Three computer scientists meet in college, win a hackathon together, found a company for recruiting, realize it doesn’t work, then pivot to a successful strategy around skills management.  C’mon.  Goosebumps.  I love every element of it.
• The space.  The skills-based organization is a powerful and transformative vision for the future of work.  It’s one that takes technology to implement.  And it’s a great use-case for AI, starting with the problem of building an inventory of skills for the people you have already — before hiring hundreds or thousands of new ones.  It’s a win/win vision because it means companies can do more with less all while providing employees with better growth paths and more stimulating work
• The validation.  More than my opinion, I was impressed that experts like Jason Corsello (former head of corpdev at Cornerstone), Thomas Otter (former head of product at SuccessFactors, and former Gartner RVP covering the space), and Andy Leaver (former head of EMEA at Workday) all seemed to love the idea, too.  Not to mention the implied endorsements of SAP, ServiceNow, and Workday.
• The data-centric approach.  Rather than building a classic app that simply links a UI to a database (and leaves the heavy lifting to someone else), the founders cut straight to heart of the problem.  Skills data is a data problem.  And the best data doesn’t live in HR systems; it lives in operational systems and external data sources.  Solve that and the rest is somewhat trivial by comparison.
• The timing.  I believe this company is in precisely the right place at exactly the right time.  The skills-based organization is a white hot trend in HR and you need technology like TechWolf’s to realize it.
• The board and investors.  They’ve built a great team here to support them on their mission.  And raised over $55M to pursue it.
• The fit.  Ever since I moved to Paris to work at Business Objects, I’ve been working with European companies on growth strategies and US expansion.  Thanks to my operating experience in Europe, my board experience at companies like Nuxeo, and my EIR work at Balderton, I feel pretty qualified to help with this sometimes thorny problem.

Thanks to Thomas for introducing us, and thanks to Andreas, Jeroen, and Mikaël, for welcoming me onto the team. 

The post Why I’m Joining the Board of TechWolf appeared first on Kellblog.

Either way, if you’re an enterprise software startup, there’s a good chance there is some debt on your balance sheet.

Debt providers typically aren’t very attention demanding. They don’t require board seats and they usually don’t ask for board observer rights. Sure, they want detailed monthly financial reporting, but your company is producing those reports anyway and it’s easy to add them to the distribution list. So debt providers don’t necessarily get a lot of mindshare from the executive team and board.

If you have debt, here’s my simple advice on managing it:

• Ensure covenant compliance tests are on featured prominently on your one-page key metrics dashboard that accompanies every draft operating plan and is presented at every QBR. This keeps covenants top-of-mind, where they need to be. Covenants are, simply put, existential.
• Try to use debt providers who already work with your investors. This will provide your investors with some leverage if things get dicey. Think: “if you call this loan, you will never do business with our portfolio again.” While such words are more impactful from a relatively big customer, they are also not by any means some kind of invincibility shield.
• Call when you’re in the yellow zone. Don’t wait until you trip covenants to have a conversation with your debt provider. There are a lot of other options besides calling the loan (e.g., refinancing) and it’s best to discuss them while you’re on the warning track, not against the wall.
• Build a relationship with your debt provider. As the saying goes, “build relationships before you need them, because by the time you do, it’s too late.” Return their calls quickly. Check in when not strictly necessary. Offer to do reference calls on new deals. Or, speak at their executive dinners. Say yes to the invitation to their baseball box. Appear as a guest on their podcast. Show them the respect you should show someone who just might be in a position one day to bankrupt your company. Because they are.

When having conversations with your debt provider, think of covenants in two ways:

• In the literal sense, they are part of the contract that you made for your debt. If you break one, you’re in breach of that contract, and they can take whatever remedies the contract provides.
• The intent of most covenants is to ensure the lender gets paid back. They serve as an early warning system to alert the lender of potential trouble. So, e.g., if you had a big deal slip from 9/30 to 10/05 and that threw off your required Q3 liquidity ratios, then you’ve already corrected the problem within two weeks. Hopefully, that calms repayment concerns.

Remember the lender is not only trying to see if you can honor your word, but more importantly, to see if there’s any incremental repayment risk.

Finally, remember that while covenants are black-and-white tests, what to do when they’re breached is not. The debt provider has a lot of different cards to play, and the vast majority of debt providers are not in the “loan to own” business, so they have no desire to take control of your company. The cards they choose to play will be not only a function of the business situation, but of existing relationships and people.

Which is why I always say that your CFO should be the customer testimonial on your debt provider’s homepage. Who wants to call that loan?

(Thanks to Ian Charles for teaching me this principle back in the day.)

The post Why Your CFO Should Be The Customer Testimonial On Your Debt Provider’s Homepage appeared first on Kellblog.

I was so happy with the conversation that I jotted down a bunch of notes to make slides so I could post them. But alas, work got busy (including joining three boards) so I’ve not had time — until today.

So here, finally, are the slides that I wished I’d made before my October Balderton Launched workshop. Thanks for everyone who came along to the session and to Greta Anderson for setting it up. The PDF is here. I’ve embedded the slides below.

I can’t wait to use these with the next cohort!

The post Five Success Principles For Startup Founders appeared first on Kellblog.

While that might sound obnoxious, it’s not if you understand how hard board seats are to come by and the process through which you get one.

There are three ways to get on a startup board.

• Found the company
• Invest a significant amount of money in the company
• Get appointed to an independent director seat

That’s it. There’s no other ticket. The composition of the board — the number of founder seats, the number of investor seats, whether smaller or strategic investors get observer rights, the number of independent seats and the process to appoint them — is all highly negotiated.

Once a board is formed, it’s generally one director, one vote. Whether you’re a VC who owns 40% of the company, a founder who owns 20%, or an independent who owns 1%. Board votes are not weighted by ownership. (Shareholder votes are, but they are only used for a small number of decisions.) That makes board seats, who sits in them, and the discussions among the directors quite important to your company [1].

It’s not an accident if one investor has two seats and another has only observer rights. It’s not an accident if the founder can unilaterally appoint an independent director or if they need to be approved by the rest of the board. There are no accidents. Not if everyone has good lawyers, at least. Everything is negotiated.

Let’s highlight a potential problem with a realistic example:

• A company has arrived at a board composition of two founders, one A-round investor, one B-round investor, and one independent. So five directors in total.
• Let’s say the B-round investor also negotiated for an observer seat. They want to use it as board training for a partner-track VP.
• Let’s say the observer starts acting like a director at board meetings. Speaks freely as the directors do. Asks questions of management. So much so that someone watching the meeting couldn’t correctly distinguish between a board member and an observer.

What’s happened? For all practical purposes, the B-round investor now has two seats on the board. But they only negotiated one. Sure, when it comes to voting (which takes about one minute), they’ll only get one vote. But during the discussions leading up to a vote (which take the other 99% of the meeting) they have two voices out of six (33% of the oxygen) when they’re supposed to have only 20%.

That’s the problem. It’s unfair to the other investors. It’s unfair to the other board members. It’s not what was agreed to.

This is not by any means to say that board observers should not add value. I think there are three ways they can do so:

• By answering questions posed to them. Boards shouldn’t pretend that observers aren’t in the meeting. If one of the observers is a category expert, and the board is discussing strategy, then they should ask them. When board observers can add value, the board should solicit their opinions.
• By debriefing offline with the CEO. They can make a list of topics to discuss with the CEO after the meeting. I do this all the time. It’s a great way to add value and often more effective than speaking too much during the meeting. (Just as board observers should avoid talking, board members should avoid grandstanding.)
• By sending the CEO questions in advance, after a pre-meeting review of the board deck. This presupposes observers get the board deck (which they typically do for the general session) and enables the observer to influence how the deck is delivered in the meeting [2].

Now, let’s review two scenarios where you almost certainly want to provide a board observer with more leeway.

• Debt providers. While they may not own equity, they may have millions to scores of millions of dollars invested in the company as well as restrictive covenants that can trigger a loan default. On the theory of “always be nice to people who can bankrupt your company,” I would provide more space here [3].
• Strategic investors. Strategics may decline board seats they’d otherwise be offered, and opt for observer rights in order to limit liability exposure [4] and avoid conflict of interest concerns. On the theory of “always be nice to people who might buy your company,” I’d allow strategics more space as well, particularly if the designated observer is an expert in the market [5].

Note that if your board has a strong, active, independent chairperson, then they will probably handle any issues for you. But, in my experience, most VC-backed boards don’t have one and only some PE boards do [6].

Some CEOs choose to adopt additional rules for observers:

• Allowing observers to attend only via Zoom, even if the meeting is live
• Reserving seats at the table for directors and management, asking observers to sit in chairs along the outside of the room

This can be seen as harsh, but I’ve seen meetings where early-arriving observers take primo seats at the table, relegating directors to the fringe, and that doesn’t work well either. Also, with some companies, when you add up founders, management, directors, and observers, you can get 20+ people in the room, and meeting dynamics become an issue.

Whatever rules you pick, use common sense in picking them, and be respectful in applying them. The best time to discuss rules is while you’re still negotiating the terms sheet [7]. Once a board gets in the habit of treating observers like directors, it can be hard to break. So from the outset, you should be ready to set norms for observers. Talk to new observers in advance so you know they understand the rules. And then respectfully enforce them if they get violated in the meeting.

Most of all, don’t be so busy ensuring that your observers primarily observe that you forget to rely on them when their expertise can help. The point is to have a quality meeting, leverage all the talent in the room — including the partner at your corporate law firm who’s typically taking the minutes [8] — all while respecting the highly negotiated agreements that determine the structure of your board.

# # #

Notes

Thanks to Martin Fincham, Jeff Higgins, and Bob Clarkson for their feedback on an early draft.

[1] This post is written largely for VC-backed boards where there is often a cat-herding problem. If you have a strong, independent chair, they will usually herd the cats for you. But when the founder is the chair — and they don’t visibly “switch hats” during the meeting to act in both roles — it’s effectively the same as having no chair. In boards where a PE firm has a controlling interest, the deal partner is the de facto chair (as well as majority owner) and, like most everything else, it’s really up to them to set the rules for observers.

[2] And implies the need to ensure there is a confidentiality agreement in place with the observer and/or their parent entity. As a friend pointed out: directors have legal agreements, why shouldn’t observers?

[3] And, having done some consulting on the venture debt side, I must say I’ve been surprised at how little respect some investors show to debt providers. While their IRR may not be as high and their MBAs not as shiny, if you start breaking loan covenants, your debt provider will have discretion on whether to call your loan and potentially bankrupt and/or be in control of your company. Be nice. They may be quiet, but they carry very big sticks.

[4] As the deepest pocket around the table.

[5] And/or have rights of first refusal on a future financing round or sale. And where the partnership may be operational as well (e.g., technology sharing, distribution channels).

[6] In my experience the strong, independent chair model seems a bigger thing in Europe than in the US, and bigger in PE than VC.

[7] This is not to suggest that they get codified in the terms sheet, but simply to have a discussion about the role of observers with someone who’s asking for observer rights.

[8] This is more of a US tradition than a European one. In the US, it’s common for the partner on the account from your corporate law firm to attend your board meetings and record the minutes.

The post The Proper Role of the Board Observer appeared first on Kellblog.

• Republicans want to save the country from a list of crises
• Democrats want to save democracy from a man

And that the Democratic message suffered from four key flaws:

• It was too cerebral — e.g., saving the American ideal and the soul of the nation.
• Fighting a man sounds vindictive while fighting for the country sounds noble. (Irony noted.)
• Democracy isn’t all that popular an idea. It’s often referred to as the least bad form of government.
• The message to cross-over voters was effectively: take one for the team. Vote for someone you don’t like in order to save the democratic system of government. (Hint: that’s not very compelling.)

I understand there are a hundred other factors that influenced the outcome and people will be studying that for years. But in this post, I want to take a quick look at some of the messaging tactics that I think worked to the Republicans’ advantage. I’m not going to touch on truth or falsehood both because that’s quicksand and because lots of other people do [2].

Here are the tactics that I think worked to the Republicans’ advantage:

• A simple, clear message. Put “Save America” against uh, well, I can’t even tell you what Harris’ message was. “Joy,” or “The Other Guys Are Weird,” or perhaps, “A New Way Forward”? [3] This is a big problem. You should always have a simple clear message for your candidate or, in technology, for your company and product [4].
• Talking about the problem. The Republican message recites a litany of problems with America. But it is very light on solutions (e.g., “I have the concepts of plan“) [5]. I have long believed that 80% of winning is about demonstrating understanding of the problem. In tech, we are so eager to talk about the solution (i.e., the product) that we fail to use the powerful technique of demonstrating absolute fluency in the problem. Complete your customers’ sentences when they’re describing the problem. They’ll love you for it. And then trust that you can solve it [6].
• Differentiation. While the Democrats did differentiate from the Republicans, they failed to differentiate from themselves. Given the unpopularity of the Biden administration, this was essential. But Harris offered no differentiation message. This enabled Republicans to position her as a continuation of the unpopular status quo. In tech, we must remember not only to provide differentiation from our direct competitors, but also our indirect ones, and sometimes from ourselves (e.g., our prior version). Most marketers build one generic differentiation message. They should build N specific ones.
• Tit-for-tat messaging. For example, “I’m not the threat to democracy, you’re the threat to democracy.” This goes all the way back to 2016 and “I’m not the puppet, you’re the puppet.” This tactic works because it muddies the issue. You don’t even need a strong counter-argument to neutralize the message because all you’re trying to do is gray it up [7]. The desired conclusion: “Well, they’re each a threat to democracy in their own way.” This works in technology. “We’re not the ones with scaling issues, they’re the ones with scaling issues.” Just build an argument to support the assertion. Even if it’s somewhat contrived, it can still work when you remember the job is not to win the point, but only to muddy it up.
• Attacking the opponent’s leaders, not their supporters. I think “the enemy within” can be seen as referring to key Democratic leaders. Whereas Hillary’s deplorables, Biden’s garbage, and Obama’s scolding were attacks not on the opponent’s leadership, but on their supporters. You don’t win votes by insulting people [8]. In technology, never attack the users of a product, but instead how the product has evolved. “Picking X was a good decision at the time, but now people are finding problem Y.” Or, “it was a great company back when you selected them, but new owners have come in, changed the leadership team, and killed innovation. We can help.”
• Speaking in plain language. Republicans generally express themselves in simple language. Democrats, not so much. Can voters correctly define facism? Regressive tax policy (in reference to tariffs [9]). Supermarket price gouging. Neoliberalism. Reproductive rights. Demagoguery. If someone needs a dictionary to understand your message, it’s a big problem. In tech, we should use regular language, as opposed to industry jargon, whenever possible. Confused people don’t buy from you. Especially when you’re a small startup.
• Consistent use of standard vocabulary. Open borders. Coastal elites. Immigration crisis. Invasion. Endless wars. Mass deportations. Election integrity [10]. Love these terms or hate them, Republicans picked them and used them over and over on the stump. Marketing is a game of repetition. Not only do the Democrats generally prefer more abstract words, they lack the discipline to repeatedly use them. Many technology companies do the same thing. They never settle on a common vocabulary, use multiple words for the same concept, and confuse people as a result. And the easiest thing for a confused buyer to do is nothing. That is, not buy your product.

No matter your views on the outcome of this election, I hope you can appreciate some of the messaging lessons that can be learned from it.

Peace out.

# # #

[1] While I’m not trying to evangelize my views, nor do I try to deeply bury them. So they have a habit of leaking out. If that bugs you, stop reading here. In regards to my own views on the election, I’ll just say that it looks like I picked the wrong week to quit stoicism.

[2] It’s difficult to compete against an opponent who lies constantly. (In software as in politics.) But it’s not impossible if you inoculate against their lies in your messsaging (e.g., our competitor is going to tell you XYZ, here’s why they do that, and here’s why it’s not true) and call them out when they do (e.g., using tactics like tit-for-tat below). In this election, the lying issue was muddied up using tit-for-tat (described in the bullets above) with the desired conclusion being: “all politicians lie,” which grays out the large differences in frequency and degree.

[3] “A New Way Forward” wasn’t a bad message, but it was neither fully developed nor hammered home. (I had to go to her campaign website to learn it was the message.) Moreover, The New Way Forward was absolutely gutted by Harris’ flub on The View, which basically said that the new way forward is the same as the old way forward. Talk about driving a stake through the heart of your own message.

[4] For a startup, your company message is your origin story. Why you exist.

[5] Or the slogan “Trump Will Fix It” which captures the spirit perfectly.

[6] The other advantage of not proposing detailed solutions is that you have no concrete plans to attack. While Project 2025 was a very specific plan, Trump immediately backpeddled when faced with its unpopularity. It won’t take long to find out the extent to which that backpeddling was disingenous.

[7] A lot of messaging is the basic battle between black/white and gray. You want black/white differentiators for your offering and you want to gray out the differentiators of your competition. Think: in fact we both have that feature, but we do implement it differently.

[8] Also, when attacking an opponent with a cult-like following, you should never attack the cult because it only makes it stronger. That’s why people were dressing up as garbage cans after Biden’s gaffe.

[9] Many people don’t understand tariffs let alone how they represent regressive tax policy. Or, for that matter, what regressive tax policy is. The correct counter-message would have been to position tariffs as a sales tax or as an inflation driver.

[10] Which surprisingly became a non-issue on 11/6/24.

The post Seven Messaging Lessons from the 2024 Election appeared first on Kellblog.

Increasingly, you’ll hear the term “business outcomes” popping up in software marketing.  While I initially loathed the phrase for its vagueness, over time I’ve come to believe that, much like the frequently-abused word “solutions,” there is a right and a wrong way to use “business outcomes.” 

Let’s start with two examples.

Example 1: 

Our skills-based HR solution helps you inventory your talent so you can better leverage the people you have and achieve the business outcomes you desire.

Example 2:

Our metadata platform helps you deliver clean data to business analysts so they can build better models, make better decisions using them, and deliver superior business outcomes.

These are ostensibly similar claims.  Both are in feature/advantage/benefit form (i.e., our feature X gives you advantage Y that delivers benefit Z). However, I strongly dislike the first one, while I’m almost-OK with the second.  Why?

Example 1 deals with a specific application where it’s possible to enumerate concrete benefits such as:

• Save money by making more productive use of your existing talent
• Save more money by eliminating new hires from the plan
• Increase employee satisfaction by providing more stimulating work (that leverages their skills)
• Reduce attrition and backfill costs as a result of increased employee satisfaction

Saying “business outcomes” instead of enumerating these advantages strikes me as either lazy (if you can’t be troubled to enumerate them) or ignorant (if you don’t know them). It leaves too much to the mind of the reader. The reader has to figure out the advantages of the feature. They have to answer the question “so what?” on their own.

Leaving too much to the mind of the reader by omitting advantages and benefits is — not to put too fine a point on it — the cardinal sin of marketing. Don’t make people figure it out. Tell them.

If laundry detergents can take you from the green spot (feature) which is an emulsifier to remove stains (function) to whiter towels (advantage) to happier spouse (first-order so-what = advantage) to kiss from your spouse (ultimate benefit), then we in technology can certainly take you from skills inventories (feature) to happier employees (advantage) to saving money (advantage) to getting promoted (ultimate benefit).

When you have something to work with, when you have an advantages stack to climb, failing to do so is a dereliction of marketing duty. You should be called before the Marketing High Tribunal, convicted, and sentenced to 10 years of writing technical documentation.

(I’d argue this is often caused by a subtle form of templatitis, an endemic disease in marketing, where you fill in the template with the name of the template field. In this case, business outcomes.)

Example 2 suffers from the same problem, but with one big difference. Because we’re marketing a low-level data platform that can be used for almost anything, it’s basically impossible to enumerate benefits without knowing more about what the customer wants to do with it.  If I were writing a solutions piece on customer relationship management, I could talk about how cleaner data means better churn prediction means less churn means higher NRR. If I were writing a fintech piece, I could write about how cleaner data means better fraud detection means less fraud means reduced fraud costs means higher profits.

So the question becomes:  are there benefits that you can reasonably enumerate? At some point you have to know what someone wants in order to climb the advantages stack. But even here, I think I could do better:

Our metadata platform helps you build a superior data infrastructure to drive your organization’s data culture, help analysts build better models, make better decisions, and deliver superior business outcomes across areas like finance, marketing, and operations.

It’s not great. Look, this is a tricky marketing problem. We’re trying to climb a generic advantages stack in a way that results in more compelling messages than save money by working smarter. Here’s what I did to try and improve it:

• Knowing that CDOs buy data platforms, I tuned the message toward CDO priorities (which I can learn through market research)
• I know that CDOs see their job as building enterprise data infrastructure, so I tell them directly that we help with that. Our product can makes yours better.
• Many CDOs have a strategic mission to build a data culture, so I make that explicit. If you buy our platform it will help advance you on your strategic mission.
• I remind them that better data means better models means better decisions means better business outcomes. Since I have no idea what outcomes they’re seeking, it’s hard to do better — other than being generic (e.g., “more profit”) which isn’t compelling or taking shots in the dark (e.g., “increase inventory turns”) that will either hit or miss.
• But since I know that most of our customers are delivering data to finance, marketing, and operations, I throw that out to be a bit more specific. Think: ask me about how we help finance teams.

As with many challenging games, sometimes the only wining move is not to play. Or to change the rules. How can we do that? Write a different piece.

Sure, every product needs its web page and product overview. And that’s the land of generic feature/advantage/benefit marketing. Climb the benefits stack as high as you can. But since it’s hard to get strong business benefits when playing the generic game, maybe you should invest less energy in product marketing and more in solution marketing. Write about use-cases instead. Write a solutions piece about how your metadata system can help customers build their customer data platform (CDP) and get the many marketing benefits of a CDP. Write a case study about how BigBank uses your metadata platform to help with fraud detection and save $60M/year as a result.

But either way, don’t get lazy and say “business outcomes” because you don’t want to enumerate them or, worse yet, because you don’t know them. Check yourself each time you write the words. Ask yourself: Can I climb the stack higher? Can I enumerate actual benefits? Am I truly in a situation where “business outcomes” is the best I can do?

If yes, say it. And then start thinking about the next piece you need to write so you can change the rules.

The post Should Marketers Say “Business Outcomes”? appeared first on Kellblog.

Our ostensible topic was What Really Matters in SaaS in 2025, but we ended up having a wide ranging and fast-paced conversation about many things, including:

• Will 2025 be the year of IPOs for PE-backed companies?
• What metrics are PE sponsors looking for in mid-market software acquisitions?
• What’s happened historically to the IPO bar, i.e., the minimum size you need to go public, and where is it today?
• Are PE firms looking for fixer-uppers or already-fixed businesses?
• Jason’s rule of 20/30/0 = to get PE interest, you need $20M in ARR, 30%+ growth, and 0% cash flow
• How to get a strategic multiple from a PE firm?
• A discussion of Andy Wilson‘s successful exit at Logickull where Jason was an investor and I was an advisor.
• What will be the impact of AI on SaaS budgets? (Here, we discuss some data from the Battery report on State of Enterprise Technology Spending.)
• How to target and win “experimental AI budget” (that is out there and in no short supply)?
• How some customer success orgs lost the plot, and became too focused on process (e.g., QBRs) and not enough on sales and renewals.
• My rule of 30: that expansion ARR should be 30% of new ARR, roughly. Too high and you’re milking the base, and too low and you’re ignoring it
• Why I love the healthy tension between sales and customer success when they are separated
• What a “slug” or “zombie” company should do if you’re $15M and growing at 15%
• Should companies lead or follow on pricing models? (We both firmly believe in using the same pricing model as the leaders in your sector unless you are a pricing model disruptor.)

Here’s the video. Thanks to Jason for a great conversation.

The post Video of My Appearance with Jason Lemkin on SaaStr Workshop Wednesdays appeared first on Kellblog.

It’s clever alright, but it’s not that helpful. It’s a meta-answer that sidesteps the question of value. In this post, I’m going to offer a simple rule for when you should consider selling your company if someone comes along and makes a serious offer.

Selling a company is a hugely difficult decision that involves both personal considerations and big, strategic questions like:

• If I say no, what might that strategic suitor do instead? Will my top partner become my top competitor, potentially overnight?
• What is happening to the space at large? Are my competitors being gobbled up? Might I be the one left without a chair when the category consolidation music stops?
• Does the market require a Switzerland, an independent vendor who’s not owned by any of the megavendors? (For example, as data integration has historically.)
• If I “keep on keeping on” — given my size, growth rate, and financing ability — where I am likely to end up? As a clear market leader? As an undifferentiated, fifth-place also-ran? (And those don’t trade for median multiples.)
• Or, will I “pull a VMware” and sell a business for $625M that will one day be worth $60B? (And conversely, if I own 30% of it, how much difference will that 100x uplift actually make in my life?)

In this post, we’re going to keep it simple by focusing not on whether you should sell your company, but on whether you should consider selling it.

And, as is often the case, I have a simple rule. You should consider selling your company when an offer takes three years of risk off the table.

What does that mean?

• Go look at your three-year model (and this is one of many reasons why you should have one)
• Find your ARR is twelve quarters out
• Multiply that ARR by what you think will be a reasonable ARR multiple given your future growth rate
• If the offer on the table is greater than or equal to the number you just calculated, you should consider selling.

Why do I think this formula works? Because:

• Most three-year models are optimistic. For most companies, you’re looking at a best-case estimate of what ARR will be in three years.
• You’ll probably overestimate the “reasonable” multiple. If the market data suggests 4-6x, you might round up to 6-8x. Human nature.
• You’ll therefore generally arrive at a generous future valuation that will take no small amount of work to realize.
• And a lot of shit can go wrong along the way.

Why consider taking that valuation? Simple. Because building companies is hard. As one founder often said, “not just harder than you think, but harder than you can possibly imagine.” I’ve played a leading part in building four enterprise software companies and I’d say it’s just plain hard. But maybe that’s because I have a better imagination. Or an imagination fueled by more experience.

So, if you want to pay me now what I think the company will objectively be worth in three years — if everything goes smashingly — then I’m going to need to seriously consider that offer.

Note that I am not suggesting you should automatically take any offer that’s N times your 3-year forward ARR (for whatever value of N). You need to answer all those personal and strategic questions first. But I am also saying that — unless conversely you see storm clouds on the horizon — that you shouldn’t invest too much time considering offers that take 1-2 years of operating risk off the table.

Why?

• Your one-year-forward ARR is basically your operating plan. And, if you’ve followed my planning advice (“make a plan that you can beat“), then you should have pretty high confidence in that plan. So an offer that takes one year of risk off the table shouldn’t be that compelling.
• Your two-year-forward ARR is not the layup that your one-year plan should be, but if you think your model is realistic — and I admit this is completely subjective — selling off two-year-forward ARR just doesn’t seem worth it. You’re trading away future upside for a number that you’re still pretty confident you can hit. Two years is a long time, yes, but not that long.

For me, at three years, the tone changes. A lot can happen in three years. New competitors. Category consolidation. New vendors entering the space. Bad C-level hires. Product development disasters. Failed acquisitions. Geographic restarts. Channel programs that don’t take. And other scary things that go bump in the corporate night.

Yes, everything may go right over your coming three years. Maybe you’ll even beat that optimistic three-year model.

But at three years, I start to do some hard thinking about both strategic and operational risks. I’ll need to feel very good about the future to say, “no thanks, we’ll roll the dice.” Particularly if that suitor is a megavendor with intent to enter the market anyway. Or, if multiples are currently high relative to historic averages — meaning that I might get offered 10x $50M today, but by the time I’m actually at $50M, the market may be trading at 6x. That’s three more years of work for 40% less money assuming everything goes great. All because multiples moved down on me.

Let’s insert a little model to make this concrete:

Using my rule, if someone offered me $675M for this company, I’d have to seriously consider it. Note that’s 45x this year’s ending ARR and 22x next year’s. But since it’s November, I’m already worth $233M, the 15x multiple coming courtesy of my 107% growth rate. (Feel free to quibble with me on the numbers; I think they’re representative, but the approach is the point.)

• If someone offered me $354M, then I’d say no and roll the dice on achieving my operating plan. Because once I do, I’ll be objectively worth that in 12 months.
• At $535M, I start to get queasy because that’s 2.3x what the company is objectively worth today. This is why I made the model — to make things concrete — because I might well consider that offer, particularly because my decelerating growth drops my 2027 multiple to 8x, meaning only $141M of incremental value is created in 2027. I wouldn’t definitely consider it, but I probably would and I’d be arguing the whole time that we can make it worth more given “only” three years’ work. (My change in tune here is a negotiation posture.)
• At $676M, I’d definitely consider the offer for the reason stated above: that’s what the company should be objectively worth in three years if everything goes well. And a lot can go not-so-well over a three-year period.

Note that I did one sneaky thing in the model. I included the number of AEs I’d need to make those numbers using that as a rough proxy for work. To make the plan work, I’m going to have to hire 35 reps — net of attrition — over the next three years. And all the support resources they need and/or generate, e.g., SDRs, SCs, managers, post-sales consultants, CSMs. For me, it helps to make the anticipated work visceral.

Let me address some anticipated objections to this approach:

• It doesn’t consider long-term strategic value. You’re right. It doesn’t. That’s why you need to consider that under the big strategic questions. Don’t pull a VMware. Or, arguably, a YouTube.
• If I did this three years ago, I’d have sold for pennies. To be concrete, let’s say the company’s annual ARR ramp was (0, 1.0, 2.5, 7.5, 15.5), which dovetails into the table above. That means you’d have seriously considered an offer of $46.5M three years ago when you were $1.0M in ARR. If you’re VC backed, there’s no way you’d sell, so you can consider it as much as you want. And if you looked at personal and strategic considerations, you probably would have said no anyway. But yes, the rule doesn’t scale particularly well back to $0.
• This will backfire going into a valuation bubble. And it will. Say multiples now are 6-8x for your growth rate and you model off 6-8x in your three-year calculation. If the market gets frothy, those multiples could double to 12-16x. Ergo, this formula will underestimate your future value by half. I have two responses: (1) the opposite is true as well; you win when you apply bubble multiples to future non-bubble ones, and (2) the ARR figure is probably over-estimated which should mitigate but not eliminate that effect.
• It doesn’t include a hockey stick when we hit some market inflection point. That’s Silicon Valley speak for the model might underestimate three-year ARR because of [insert miracle here]. And it might. But for every 100 companies I see waiting for those miracles, maybe 2-4 get them. It’s rare. And if you really think that inflection point is going to happen, put it in the three-year model.
• All the great founders are all-in, YOLO. I have two words for you: survivor bias. I know lots of great founders who were all-in, got sunk on the river, and wish they’d taken some money off the table. Ultimately, this will come down to your personal goals. (And it’s one reason why I think VCs love second-time founders. Dave Duffield was never going to sell Workday too early because he had all the money he ever needed from PeopleSoft. And since VCs would generally rather sell too late than too early — because it’s a “hits business” — that creates a deep, natural alignment.)

My purpose here was to give you a rational framework for thinking about this decision, and here it is: think about how many years of risk gets taken off the table. That’s what you get in exchange for trading away whatever potentially bright future awaits.

The rest is up to you — and your board. Good luck with it.

# # #

(Revised 3:59pm. Sorry, this was accidentally published before final spellchecking and copy editing was complete.)

The post A Simple Rule For When To Consider Selling Your Startup appeared first on Kellblog.

I’d recently moved to Paris and heard about this layer atop the Internet that relied on hypertext transfer protocol to connect web browsers and web sites. Having seen what Apple had done with hypertext up that point, I wasn’t prepared to be impressed. So I fired up a browser and, to reconnect with home, I went to sfexaminer.com. The headline read:

“God, I hate this thing!” As an inveterate deadhead, the news was devastating if not entirely surprising. The episode set my web adoption back by a few years. While I won’t dive into my history following the band, I’ll show you the back of the car that we keep at our house in Oregon. (Morning Dew.)

Four years earlier, we’d lost the power in our Marin County home the night Bill Graham died in a helicopter crash west of Vallejo. If I was connected to Graham’s death via a power line, I was connected to Garcia’s via the web.

An SMS message connected me to last Friday’s death of Phil Lesh. Sent by a friend from so long ago that my phone no longer recognized his number, the news arrived as anonymous text message, containing only a link to the story.

I’ve written before (and as recently as three weeks ago) about business lessons from the Dead, so I won’t cover that again. Instead, as my tribute to Phil, I’ll write briefly about the power of metaphor using one of the relatively few Dead songs he wrote: Box of Rain. It’s also one of my favorites.

The song deals with Lesh’s feelings during the lingering, terminal illness faced by his father. Grateful Dead lyricist Robert Hunter worked with Lesh on the lyrics, and they are some of Hunter’s finest.

You can hear the frustration and impotence in battling illness via lines like:

“What do you you want me to do, to do for you, to see you through?”

The power and beauty of the song, however, comes from the primary metaphor: the box of rain.

“Just a box of rain, wind, and water. Believe it if you need it, if you don’t just pass it on.”

But what is this box of rain? It’s a metaphor. In fact, it’s a metaphor within a metaphor.

Hunter was thinking along the lines of a “ball of rain,” but that was probably both too obvious and insufficiently poetic. So the ball became a box. (Hence, the inner metaphor.)

So what is this ball of rain, wind, and water?

It depends on perspective, and in this case you’re going to need a wide one. Seen from far enough away, that ball of rain is our home. The earth.

“It’s just a box of rain, I don’t know who put it there. Believe it if you need it, and leave it if you dare.”

Once you understand the metaphor, that’s pretty literal.

“And it’s just a box of rain, or a ribbon for your hair.”

Hunter loved to write about certain things, such as calliopes and ribbons. While hard to interpret, I think this line is another metaphor. What do ribbons do for hair? Make it more beautiful. What does the box of rain do for the universe? The same thing. The earth is just a ribbon in the hair of the universe.

It adds a sense of utter smallness, rivaled only by how Tralfamadore used the earth in The Sirens of Titan.

(I feel obliged to say that Hunter hated to interpret his lyrics, maintaining that it wasn’t about what the words meant to him: it was about what they meant to you. He didn’t want his meaning to ruin your meaning. At first, the puzzle-solver in me found this offensive, but over time I’ve come to realize that it’s actually pretty cool.)

Now we’re ready for the last line.

“Such a long, long time to be gone and a short time to be there.”

There, of course, being here. On our box of rain. In this life. On this earth.

Thank you Phil for always putting the music first, the culture, and the reminder. May we all spend our time here as well as you spent yours.

The post A Box of Rain Will Ease the Pain appeared first on Kellblog.

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• Should marketing report into sales?
• Should engineering and product management (PM) report into a combined product org?
• Should we unite customer success and sales?
• Should North American and Europe report into a single head of sales?

The argument for combining teams is always about reducing span of control. This is a goal that many CEOs (and some boards) share, but one that somehow escaped one of the world’s most successful entrepreneurs, Jensen Huang, who has about 60 direct reports.

While 60 seems a bit much, I’ve frankly never understood span-of-control reduction as a top organization design goal. As CEO, you should be managing senior people so they shouldn’t take that much time. So, why not have 8 or 10 direct reports? If you can’t handle that, maybe the problem isn’t that you have too many reports, but that you’re managing them too closely. Maybe the solution isn’t to reduce their number, but to loosen the reins.

I have two rules for organization design:

• Design for conflict. Specifically, design your organization for the conflicts you want to hear about.
• Ensure value-add. Don’t put thing B under thing A unless the executive in charge can add value to both.

Design for Conflict

When you put, for example, engineering under product, what don’t you hear about anymore? Conflicts between PM and ENG about the time and resources required to build things. Those conflicts get silenced because the SVP of Product will suppress them, resolving them in the family.

When you put marketing under sales, what don’t you hear about anymore? Conflicts about whether sales strategy is too unfocused to enable marketing targeting. Or whether sales follows-up on new oppties in a timely manner. Those get silenced because the CRO wants to manage their own house. “Let’s resolve that at the sales QBR, not the e-staff meeting.”

When you put customer success under sales, what don’t you hear about anymore? Conflicts about whether sales is overselling to the point that customers won’t be successful and ergo won’t renew. If churn looks high, well, it must be the product. It’s not delivering, but against what expectations, set by whom? All silenced.

The rule is simple. By combining two departments, you are asking one person to resolve the conflicts between them. They’re not evil to do so; it’s the job you asked them to do. They will keep these conflicts in the family. And, as the organization grows, you will hire increasingly senior people to do just that. But with each layer and with each combination, you get more insulated from the ground truth.

So the question is simple: which conflicts do you want to hear about? Which do you want to pay someone else to resolve and which do you want brought to your office? Which are strategic to the company and potentially involve Crux-level issues?

• If you separate PM and ENG, you’ll hear a lot about specs, resources, and timelines.
• If you separate sales and marketing, you’ll hear a lot about awareness, leads, and follow-up.
• If you separate customer success and sales, you’ll hear a lot about over-selling.

There’s no magical answer here. Just a framework for thinking about it. Determine the conflicts you want to hear about — presumably because you can add the most value in resolving them — and then design the organization to make sure you do.

Ensure Value-Add

The other principle is to always ensure value-add, beyond the (sometimes merely assumed) alignment that comes from having a common boss. So, sales wants the SDRs to report to them? Why? Has the sales VP managed an SDR team before? Are they good at it? Are they even interested in it? Can they add value? Are they sufficiently metrics and process-oriented, particularly if the VP comes from an enterprise background?

This principle drives a number of positive effects:

• It defeats empire building. Sometimes the VP wants the SDR team not because they care about them, but because they want a bigger organization. Or they think it will look good on their resume for their next job search. They’re not actually interested in the job. They’re interested only in being able to say that they did it. That’s not good enough.
• It encourages learning and development. When the VP of sales first asks about managing the SDRs, you can tell them to go make themselves a valid candidate. Get close to the SDRs now. Understand their challenges and offer to help out. Network with friends and colleagues on SDR team management. Read up on best practices. Convince me that you’d make the short list of candidates and then we can have a conversation.
• You attract stronger department heads. Everyone should work for someone they can learn from. Saying the boss is the boss because, “well, we had to plug the team in somewhere,” is a terrible reason for an organizational structure. If you apply the value-add rule, functions will tend to report higher in the chain, creating a flatter org, and be placed only under those who can add value to them. This, in turn, attracts stronger candidates to run them. Who wants to be the CMO when it reports to a CRO who understands nothing about marketing? Nobody.

One great example is whether the VP of European Sales should report to the existing VP of Sales when you expand into Europe. If your VP of Sales is clever, they’ve already given themselves the title “VP of Worldwide Sales,” and you let them do it because it was moot at the time. But now they’ll argue it’s a demotion if Europe doesn’t report to them. And they’ll argue that they know how to sell the software in North America (really, the USA) and that knowledge should translate anywhere. And that everybody does it this way. You can almost hear them screaming: pick me, pick me!

But what they should be screaming is: I can add value, I can add value! And if they can, you should listen. But my questions would be:

• Do you have a passport? (This wipes out about half of Americans.)
• Have you ever lived in Europe?
• Do you speak any European languages?
• Have you ever sold and/or managed people in Europe?
• Do you you have a network of people we can hire in Europe?
• Do you have relationships with contacts at target customers in Europe?
• Do you know any strategic partners we can work with in Europe?

So, other than not having a passport, never having been there, knowing no one, and not being able to communicate, you strike me as an outstanding candidate for the job.

We do this all the time nevertheless, and Europeans have grown accustomed to reporting into people who can’t add value. But for my nickel, I’d rather hire a VP of EMEA who had great answers to my questions and reported directly to me.

Mitigation Strategies

As your organization grows, you will invariably combine teams and lose your line of communication into certain conflicts. I know three ways to mitigate this:

• Build a culture of transparency where direct reports into e-staffers are encouraged to and rewarded for speaking frankly about in-the-family problems.
• Run an extended QBR. Don’t just invite the e-staff to the quarterly business review, but also invite people among their direct reports. For example, the head of customer success if it reports into the CRO, or the head of engineering if it reports into product. Ask them to deliver the same, standard presentation that the e-staffers do. This effectively flattens the organization by creating an extended leadership team that goes beyond the CEO’s direct reports.
• Use reporting. Good reporting can reach through organizational layers and keep you in touch with what’s happening. For example, even if customer support doesn’t report to you and isn’t represented on the extended leadership team, you can still keep an eye on metrics and KPIs as well as simply on OKRs.

In this post, I’ve argued that the primary goal in organization design should not be reducing of span-of-control, but in surfacing conflicts most important to the company. I’ve also introduced a value-add rule that says no department should report into an executive who can’t add value to it. And finally, knowing that consolidation is inevitable over time as a successful company scales, I’ve offered three strategies to mitigate some of the signal loss that comes with such expansion.

The post Design Your Organization for the Conflicts You Want to Hear About appeared first on Kellblog.

The short summary is:

• The concept of ARR is already challenged by monthly-varying pricing, e.g., usage-based pricing.
• AI will exacerbate that problem, bringing new forms of value-based pricing, e.g, unit-of-work or outcome-based pricing.
• There are two schools of thought on dealing with this: (1) split the ARR baby into baseline and variable, then analyze the baseline as if nothing has changed, and (2) spend is truth, where we substitute trailing spend for ARR. I’m in the second camp.
• AI will, gasp, require us to think about cost, something we don’t really like to do in the software business and something we’ve historically been able to kind of ignore.
• All the heavy lifting is going to move to the pricing model.

In short, to know ARR we used to read contracts. In the future, we’re going to read invoices, instead.

Yes, for internal reporting we will do a lot of pricing model analysis and examination of the base/variable split. But for external reporting, the big six SaaS metrics all depend on ARR and going forward that won’t change. We’ll just use some proxy for ARR, as many quietly do already today.

Like a duck, nothing will change much on the surface, but they’ll be a lot of activity underneath. And the metrics won’t mean quite the same thing as they once did. For example, ARR and NRR will become less forward looking and work less well as leading indicators.

I’ve embedded the slides below.

You can download a PDF of the slides here.

Thanks for coming!

The post Slides from SaaS Metrics Palooza 2024: The Impact of AI on SaaS Metrics appeared first on Kellblog.

It’s hard for a DNA-level marketer and inveterate deadhead [2] to review a book that fuses both. While I first read Marketing Lessons from the Grateful Dead not long after its publication in 2010, I’d never wanted to write about it. There was too much to say, the subject too close to home, and the Dead were becoming passé anyway — who’d want to learn lessons from a band that supposedly peaked on 5/8/77 and that played its last show on 7/9/95?

Since then, three things happened:

• Since 2015, John Mayer has breathed new life into the band, bringing new and younger fans to the community. Dead tribute bands blossomed and now play at swanky Nantucket venues [3]. My son started to borrow my old t-shirts to attend NYU college parties. Unexpectedly, the Dead became cool again.
• I internalized that one of the authors, Brian Halligan, was the founder/CEO of HubSpot and that should, well, automatically make the book significant. [4] [5]
• I recently had a series of conversations with Jacquelle Amankonah Horton, the dynamic founder of Fave, where I found myself talking about community to someone in the music business, something I find pretty much impossible to do without talking about the Dead. Those chats re-energized me on the whole topic.

So here we are. Let’s do my long overdue review of Marketing Lessons from the Grateful Dead.

Before diving in, let’s note that this book is not the only attempt to take business lessons from the Dead. Other notable efforts include:

• Everything I Know About Business I Learned from the Grateful Dead by Barry Barnes, an excellent book which extracts ten core principles from the Dead’s success.
• Radical Marketing by Sam Hill and Glenn Rifkin, which includes the Dead among several examples of companies that built strong brands through customer focus as opposed to heavy marketing spend.
• Competing on the Edge: Strategy as Structured Chaos by Shona Brown and Kathleen Eisenhardt, which uses the Dead of an example improvisational strategy, relying on real-time communication and a small number of simple rules [6].
• Strategic Focus: I’m Just Trying to Get My Space Together [7], a Kellblog post which argues that startups need to “get their space together” in terms of focus before defining company and go-to-market strategy.
• A Grateful Dead Analysis: The Relationship between Concert and Listening Behavior, which shows that, even for a customer-centric band like the Dead, the fundamental tension still exists between “what we want to play” and “what they want to hear.” [8]

Once in a While You Get Shown the Light in the Strangest of Places [9]

So, does this book show us the Dead’s marketing light in a way that can be applied more generally to business? Yes. It does. Though, to split hairs, I’d argue it’s more a book about strategy than marketing [10].

The book is structured in three sections. Each chapter discusses a principle from the Dead and a company that demonstrates the principle. While the chapters-as-lessons structure works, the consistent alternation between principles and examples makes the book feel somewhat formulaic. And, because tech changes a lot in 15 years, while the principles are timeless, the examples are often not (e.g., Mashable, MySQL, StumbleUpon) [11].

The first section takes lessons from the band:

• Create a unique business model. The Dead made money selling tickets, not albums. This was, and is, pretty unusual in the music business.
• Choose memorable brand names. The Grateful Dead is a memorable name, and far superior to their previous one, The Warlocks.
• Build a diverse team. The musicians came from different backgrounds. Garcia loved bluegrass, Lesh was trained in classical jazz, and Pigpen was a blues harmonica player. You can get a 1+1 = 3 effect from the fusion.
• Be yourself. The Dead were authentic in general and transparent about mistakes.
• Experiment, experiment, experiment. A product of the Acid Tests, the band was born experimental and improvisational. They were always trying new and creative things (e.g., a song in 11/8 time).
• Embrace technology. The most notable example was The Wall of Sound, a 600-speaker public address system that delivered unparalleled sound quality (and took four semi trucks to carry), but quickly proved impractical for a touring band [12] [13].
• Establish a new category. The Dead defined a new category of music, which only after several decades got a name (“jam bands”). They were very much a category creation story [14].

The second section takes lessons from the fans:

• Encourage eccentricity. Eccentricity was embraced by the band and its fans, allowing everyone to be themselves.
• Bring people on an odyssey. The Dead, starting with a mailing list hooked to a San Rafael post office box, made fans an equal part in the journey (aka, the long strange trip).
• Put fans in the front row. The band set up their own direct ticketing operation to ensure fans fair access to tickets [15].
• Build a following. The Dead were pioneers in database marketing (and subsequent techniques) to cultivate the fan base.

The last section takes lessons from the business:

• Cut out the middleman. Their direct ticketing operation not only enabled fair access, but it reduced costs, allowing the band to gross 100% of ticket sales.
• Free your content. The Dead allowed fans to tape concerts, nearly unique in the industry, and an open source approach that helped new fans enter the scene.
• Be spreadable. Concert tapes became collectibles that were copied and traded [16], whether between individuals or in Shakedown Street swap meets before and after shows.
• Upgrade to premium. While the band allowed taping, they nevertheless released live albums, with amazing sound quality such as Europe 72 or Skull and Roses [17].
• Loosen up your brand. While the Dead had standard icons, they defined a broad visual style and allowed improvisation within it. Contrast this to typically strict corporate branding style guides that stifle creativity in the name of consistency [18].
• Partner with entrepreneurs. Rather than ban the use of their imagery, the Dead licensed it to entrepreneurs who could make and sell their own merch.
• Give back. From benefit concerts to community support to the Rex Foundation, the Dead gave back, long before it was corporate-fashionable to do so.
• Do what you love. My survivor bias detector triggers whenever I hear this, but the book’s point is more fundamental: if they didn’t love what they did, they couldn’t have kept doing it for more than 2,300 concerts.

Sometimes We Walk Alone [19]

When writing about the Dead, most people capture the contrarian, rule-breaking nature of the band. It’s hard to miss. The book does that and extracts 19 individual lessons that can be taken from the Dead’s success. Frankly, I’d have preferred if they took fewer lessons and examined them in more depth, as does Everything I Know About Business I Learned from The Grateful Dead.

What the book really misses, however, is the strategic consistency across the lessons. Using the language of Richard Rumelt, the book notes 19 different actions undertaken by the Dead, but fails to recognize that they are coherent actions driven by an overall guiding policy. Much as Costco works spectacularly well not because of the fifty things they do differently, but because of how those fifty things work together, so it is with the Dead.

The improvisational format means every show is different. That enables fans to attend consecutive shows, which enables fans to tour with the band, which in turn enables the Shakedown Street tailgates and community. The ability to tape concerts combined with the improvisational format means that each show is unique and ergo recordings become collectibles, further enabling the community. Licensing merchandising rights to small vendors enables the tailgate scene and nomadic vendors, who can effectively earn a living touring with the band (and using proceeds to buy tickets). Commitment to fan experience means playing small venues, and improvisational shows enables playing small venues for consecutive nights. This enables the community scene while the direct ticket operation keeps ticket prices reasonable despite the restricted supply.

You can allow taping, but if every show is the same, the tapes won’t be valued. You can play small venues, but if you don’t control ticket sales, pricing will box out your best fans. You can play in an improvisational format, but if you don’t repeat nights at venues and move in slow and systematic fashion, you won’t enable fans touring with the band and the community parking lot scene.

It is not any one of the lessons that matters. It is the strategic consistency across them that creates the magic.

Switching to Blue Ocean Strategy as my strategic reference, the Dead re-invented rock music in the same way that Cirque du Soleil re-invented the circus. They took the fundamental levers of the business (what the authors call a strategy canvas) and reset them to create an entirely different type of product.

For the Dead, this is best reflected by the old deadhead saying, “there is nothing like a Grateful Dead concert.” That’s because the levers are set differently.

Come and Join the Party

I’ll conclude with the original invitation to the Grateful Dead party: the first verse of The Golden Road to Unlimited Devotion, the first song of their first album. A song about The Summer of Love [20].

See that girl, bare-footin’ along,
Whistlin’ and singin’, she’s a carryin’ on.
There’s laughing in her eyes, dancing in her feet,
She’s a neon-light diamond and she can live on the street.
Hey, hey, hey come right away.
Come and join the party, every day.

That invitation, some 57 years later, remains open.

# # #

Notes

[1] Opening line of Deal, an upbeat song in the band’s Americana catalog. Guitar players (or those interested in music) should check out this Guitar Teacher Reacts video by Michael Palmisano.

[2] 300 or so shows and counting, including all variations.

[3] I’ll refer to the Dead as a capstone term for The Grateful Dead, the Other Ones, The Dead, Furthur, Fare Thee Well, Dead & Company, and other variations. When a band is 50+ years old, you can end up with numerous incarnations.

[4] I knew it in 2010, but somehow forgot it over the years and was frankly surprised to rediscover it only fairly recently.

[5] And the co-author, David Meerman Scott, is no schmo. He’s a well-known marketing author and speaker.

[6] The general idea being that all strategy is improvisation because things change so quickly, and ergo that companies should view strategy as structured chaos.

[7] “So can I go into the show,” to complete the famous fan quote from the The Grateful Dead Movie.

[8] Surprisngly, two of the three authors were at the Theoretical Division, Center for Nonlinear Studies at Los Alamos National Lab. Technically, this paper isn’t really trying to extract business lessons from the Dead, but I think we find one anyway in that fundamental tension, which in software would be expressed by “what we want to build” vs. “what they want to use.”

[9] One of the most-quoted Dead lyrics, from Scarlet Begonias. The complete line is “once in a while you [can] get shown the light, in the strangest of places if you look at right.” In 1976, they inserted the [can], which subtly changed the meaning, for the worse in my opinion. But to demonstate the depth of the Dead rabbit hole, go to the Good Old Grateful Deadcast for a two-hour exploration of this one song.

[10] Strategy is a little tricky to classify. Corporate strategy is definitionally company-level and I’d classify most of the lessons in this book as corporate strategy, not marketing strategy. For those who consider corporate strategy formulation a marketing duty (and I’m one of them), remember that marketing (or corporate development) typically drives the strategy formulation process (e.g., the offsite, the agenda, the topics, the pre-reading and data). But driving the process and “owning strategy” (i.e., its result) are two different things.

[11] Timeless principles can hopefully be illustrated with timeless examples. They did this in cases (e.g., Ronald McDonald house), but perhaps the temptation to use hot contemporary examples (that invariably date the work) was too strong. Songwriters face the same tension and Dead lyricist Robert Hunter did a fantastic job of resisting it, writing timeless lyrics as a result. In fact, the famous spat that ended collaboration between Hunter and Bob Weir was over Weir’s insertion of “Jump like a Willys in four-wheel drive” into Sugar Magnolia. Hunter strongly objected, I’m guessing, not only for artistic reasons, but because it would date the lyrics.

[12] A short video overview of the three-story Wall of Sound is provided here.

[13] Some enthusiastic fans recently built a half-scale wall of sound.

[14] Dispelling the myth that the category name matters enormously. Focus on building the category, the name can come later.

[15] While Billy Joel never tried to disintermediate ticket sellers, he does seem to have a fans in the first row policy as well. Few others do.

[16] In the analog tape world, copying was not lossless, so tapers would careful try to track the “generation” of the tape (original, copy, copy of copy, etc.)

[17] The amazing quality was achieved not only through high-quality audio equipment, but also through some level of studio doctoring of the recordings, e.g., overdubs.

[18] My definition of visual branding is: do we look like us? Attaining this goal is typically done via strict standards, but a few organizations manage to define a broader look and allow innovation within. The Dead were great at this. Salesforce does a pretty good job as well.

[19] From Eyes of the World.

[20] The Golden Road to Unlimited Devotion. The Summer of Love was also the inspiration for the more famous, San Francisco Be Sure to Wear Flowers in Your Hair by Scott McKenzie, and one far more famous song than that, All You Need Is Love by the Beatles.

The post Marketing Lessons from the Grateful Dead by Scott and Halligan: A Belated Book Review appeared first on Kellblog.

You go a mid-quarter ops meeting. The CMO shows quarter-to-date MQLs. Someone asks how we define MQL. A 15-minute conversation ensues. The SVP of Alliances talks about influenced ARR. Someone asks how that’s defined, but only five minutes are spent on definition. However, a controversy erupts and 20 minutes are spent reconciling different answers from different reports. The CRO discusses the forecast, using seemingly standard words like best-case and worst-case. But nobody, including the CRO, is clear on what they mean. Twenty minutes is lost in a discussion trying to figure out what the forecast actually is. The SDR manager talks about outbound SALs. The usual attribution skirmish erupts, taking 30 minutes on whether we are accurately and fairly giving credit.

Question: what’s happening in that meeting? Are you having a discussion about the business? Or a discussion about the numbers?

Answer: you’re having a discussion about the numbers. And those are usually painful. Everyone leaves a little frustrated. Nothing gets resolved. And the best part? You get to do it again in a few weeks, because many companies just can’t seem to get beyond having conversations about the numbers.

This is a pretty simple issue.

• The goal is to have discussions about the business,
• And to have those discussions using numbers,
• But not have discussions about the numbers.

There’s a big difference between saying, “I see MQLs are on a steady downtrend, what’s happening with the major new campaign we’re running?” and “how do we (for the 37th time) define MQL?” The first is a conversation about the business. The second is a conversation about the numbers.

Conversations about the business are way more productive. And fun.

But here’s the trick: you don’t get to have a conversation about the business using numbers, until you’ve done the gruntwork of defining the numbers and making sure everyone in the conversation understands what they mean, where they came from, and how they’re defined.

You have to earn that conversation. To do that, you’ve have pay the ante. And if you don’t, guess what happens? Literally endless conversations about the numbers. Painful, slow, unproductive conversations about the numbers. And think of the opportunity cost. All the time you’re discussing the numbers, what aren’t you discussing? The business.

How do you earn the ability to have the right conversation? Here’s my favorite way:

• Start with a weekly sheet. Not a bunch of dashboard screenclips, but a shared Google sheet that your ops person populates every Sunday night.
• Decide what you want on it, and where you’re pulling that from. For an e-staff meeting, you’ll want the booking forecast (for both new ARR and churn), pipeline coverage, and bookings to date. Maybe you’ll want to see some triangulation forecasts. Or this/next/all-quarter pipeline. Maybe you’ll want to look at both count and dollars. Maybe you’ll want to separate segments (e.g., corporate vs. enterprise).
• Present it with context. Whenever you show a number, present context with it. What was it last quarter? Last year? What’s this quarter’s plan? This quarter’s forecast (if applicable)? How much is it forecast to grow year-over-year or quarter-over-quarter?
• Revew it each week at the Monday staff meeting. After a brief welcome, dive into spreadsheet. See how it works as a tool to drive conversations about the business. Whenever you hit a conversation about the numbers, realize it, stop, and delegate people to resolve the issue(s) offline. A few times per quarter, ask if you should add or cut any rows. Talk about how well the tool is helping you have the conversations we want to have.

Over time, you will refine the sheet into near perfection. Conversations about the business will become the norm. Newbies will realize that they have to learn the numbers — all of them — if they want to contribute to discussions. And nobody will think that they can stall or evade by questioning the data.

The part that people miss is how long that takes. I think it’s measured in quarters, not weeks or months. It takes that long to retrain everyone how to think. And what the numbers mean. And to decide which numbers you really want.

Every leadership team should strive to have conversations about the business using the numbers. The only way I know how to do that is to pay the piper first.

The post Talking About the Numbers vs. Talking About the Business appeared first on Kellblog.

I’m speaking this year on The Impact of AI on SaaS Metrics at 9:00 AM Pacific on 10/8/24.

I picked this topic because I wanted to force myself to learn more about it (the reason I write many Kellblog posts) and that process seems to have worked. I started out asking how will SaaS metrics change because of AI? That led me quickly to pricing models. And that led me all the way back to the existing weaknesses in SaaS metrics created by violations of the simple SaaS model (e.g., usage-based pricing, or anything else that drives variation in monthly spend).

Here’s what we’ll cover:

• How we got here: a brief, salient history of SaaS metrics
• When SaaS metrics break — e.g., when monthly spend varies or contracts are prepaid
• How ARR has become the Achilles’ Heel of SaaS metrics, a big problem given that virtually all top SaaS metrics depend on ARR
• How proxies for ARR have emerged as a result
• How and why public companies don’t generally report ARR, and how analysts use Implied ARR instead
• The four impacts of AI on SaaS metrics, specifically (1) rethinking ARR, (2) the need to consider cost (he says, gasping), (3) rethinking pricing model drivers, and (4) how the heavy lifting will move to the pricing model
• A quick case study on Piper, the impressive AI SDR from Qualified, how she is priced, and some nuances in estimating the value she delivers when you “hire” her.

The presentation will feature a brief moment of melancholy, a reflection back on the history of software gross margins.

The best aside will be when I present my three rules of pricing.

You can register to attend SaaS Metrics Palooza here. My session is at 9:00 AM Pacific time on Tuesday, October 8th. I will do a separate post after the presentation with a link to the video and a link to the slides.

I hope to see you there and thanks for attending!

The post Come to My Session at SaaS Metrics Palooza: The Impact of AI on SaaS Metrics appeared first on Kellblog.

“Other than asking me for more headcount. They’re pretty darn good at doing that,” the CEO chuckled, gazing pensively over the whitecaps from the patio of the luxury resort where the executive leadership team (ELT) was huddled.

“Wait. I’ve got an idea. We can rely on our people. They’re great. They’re out there every day with our customers. I bet they have tons of ideas on what we can do better. How we can improve our organization and processes. What features we should put into the product to make customers happier. And maybe even what new products we should build.”

“Eureka!” the CEO thought. “Since we feel kind of stuck here, I’ll declare the Leverage Our Super Team initiative. We’ll ask every employee for a good idea about how to improve the company. They’re going to love this. People are going to feel so engaged and they’ll love that all their voices are being heard.”

The road to hell is paved with good intentions. And there are plenty of good intentions here. Intentions are not the problem.

But what happens Monday morning, when everyone sees the company-wide message announcing the new initiative?

By noon, half the company is polishing up their resumes. And that’s after having called their significant others to delay any pending large purchases.

This is what you might hear, if you could eavesdrop by the virtual watercooler:

• We’re screwed with a capital S.
• Jeez, I thought we were in trouble before, but now I’m sure.
• Our leadership team is quite simply out of its depth. Nice people, but they don’t know how to run the company.
• We’re caught in a strategic squeeze and the best they can come up with — after three days at an offsite — is asking us what to do?
• Don’t they get the big bucks in order to actually run the company?
• Sure, we’re in a tough situation right now, but we can get out of it. What we need is leadership, but this ain’t it.
• Did anybody notice that the acronym for the new, save-the-company initiative is LOST? Leverage Our Super Team. That’s just perfect.

This happens all the time. I’ve seen it at just about every company I’ve worked at for 30+ years.

The first time I saw one of these initiatives, it came complete with a marketing graphic, a depiction of teamwork featuring an eight-person crew boat with everyone pulling together — but missing the coxswain.

You literally cannot make this shit up.

The point here being:

• People expect leaders to lead.
• They get scared when they don’t.
• People understand when the company’s in a tough spot.
• All they generally want to hear is that leadership is aware of the situation (i.e., not in denial) and has a reasonable plan to address it.

As for the “idea bake sale” (think: sales will make brownies, marketing will make cookies, and finance will make cupcakes to save the company), it’s a terrible idea because it does the opposite:

• Leaders abdicate instead of lead.
• It clearly demonstrates that there is no plan. (Let’s hold the idea bake sale to make one.)
• In some cases, it’s patently absurd. (Let’s ask the receptionist what new products to build.)
• And, if everyone were doing their job, for the most part we wouldn’t need one.

Isn’t it PM’s job to know what features customers want? Isn’t it management’s job to work on improving our processes? Isn’t it the CEO, CPO, and CTO’s job to work on product strategy, including product line expansion? If those processes aren’t working, let’s fix the root cause. Not have an idea bake sale.

I’m all for the odd hackathon to flush out potential new features. Or the brainstorming meeting with PM to discuss product line strategy. Or the town hall with the sellers to hear concerns about our sales process. Or the partner summit where we can hear from people outside the company about how we’re doing. These events harness energy and drive discussion. And they’re all normal parts of a leader’s job.

But that is not the same as throwing your hands in the air and effectively saying, “we don’t know what to do — what do you people think?”

The moral: one day, if you find yourself in a meeting where somebody suggests an idea bake sale, do these three things:

• Kill the idea off quickly
• Directly address any failing core processes that made it seem necessary
• Ensure that you’re doing the usual communications and strategy events

Most importantly, recognize that the bake sale might just be a distraction from an elephant in the room, and what needs discussion is the elephant, not the bake sale.

You can use The Crux to help you do that.

The post The “Idea Bake Sale” and Why It’s a Bad Idea to Have One appeared first on Kellblog.

Since selling Host Analytics five years ago, I’ve tried to stay in touch with my finance roots. I’ve done some advisory work on the FP&A side of the house (e.g., advising the rocketship that is Pigment) and kept in touch with up-and-comers like Mosaic and Causal. I’ve worked with CPQ disruptor CacheFlow. I’ve kept an eye on next-gen spreadsheets like Rows and invested in a sense-maker called Decipad. But, other than being lucky enough to make an investment in FloQast, I’ve not done much on the other side of the house: the land of accounting and controllers. Until now, that is.

I’m pleased to announce that I’m joining the board of directors of Vic.ai, a company focused on bringing the benefits of AI to the accounting department, selling solutions used by hundreds of firms worldwide. Vic.ai has raised over $110M in VC financing from top-tier investors including Cowboy Ventures, Notable Capital, and Iconiq Growth.

Here are some of these reasons why I’ve decided to join the board:

• Founder/CEO chemistry. The independent director role is all about working with the CEO on the challenges of building and scaling a company. In the past few months, I’ve spent quite a bit of time with Alex and am certain that we’ll enjoy working together to accomplish great things.
• Working with Alex is like teaching the 301 class, not the 101 class. He’s already a successful entrepreneur, having built and sold his first company in 2014 after nearly a decade’s work. So it’s a more challenging and demanding job than usual. He keeps me on my toes.
• I like marketing and selling to finance teams. They’re busy people. They’re not the most experienced buyers (unlike marketing or IT they don’t buy a lot of stuff). They’re a staff function so priorities can change overnight. They don’t like fluff. Finance is the show-me state of corporate functions. That makes marketing and sales somewhat more challenging, but more rewarding once you nail it.
• I like what Vic.ai does. The product solves practical problems for busy people who know they need to be experimenting with and learning about how AI can help them improve operational efficiency. I think the product-market fit is outstanding.
• The benefits are real and tangible. With due respect to my Future of Work friends, we’re not selling intangibles like stronger culture or improved collaboration. We’re selling improved cash flow, time saved, money saved, and errors reduced. Hard benefits. Yum.
• The VC investors are great. It’s been great to meet Ted and Will from Iconiq, Jeff from Notable, and Jillian from Cowboy. While I have worked with some of their partners in the past, I’ve not yet worked with them and am super excited to do so. It’s also great to reconnect with Greg from Costanoa with whom I worked closely for years at Alation.

I don’t know the whole team executive well yet, but have been psyched to meet the CMO Mark and the CRO Ben, and any Kellblog reader will know I have one message for them: sales & marketing is a three-legged race, so let’s perfect the art of working together.

Finally, I also look forward to working with cofounder Kristoffer and to helping him and Alex take Vic.ai to the next level.

The post Back to Finance: Why I’m Joining the Board of Vic.ai appeared first on Kellblog.

“How’s it going at StartCo?” I asked.

“Great,” the CMO replied. “We hit 105% of our pipeline generation (pipegen) goals last quarter, and with a healthy pipe/spend ratio of above 10.”

“Nice,” I said. “How is sales doing?”

“Oh, that’s another matter,” the CMO said. “They landed at 82% of new logo ARR plan.”

Quick: what’s wrong with this conversation?

Answer: if the purpose of marketing is to make sales easier, marketing cannot be “doing great” when sales is 82% of plan. Period. Always.

What’s driving this problem? Part of it is me-, me-, me-oriented metrics like pipegen. Or more specifically, pipegen from marketing, which is about how marketing did relative to its pipeline generation goals. But let’s remember the point of pipeline is to ensure sales has a shot at success every quarter. And that marketing is not the only pipegen game in town. And that different pipegen sources have different conversion rates (or, as I like to say, nutrient density). Oh, and even if the entire pipegen machine is firing on six cylinders, that we can still end up with pipeline shortages.

What’s the underlying problem? Call it myopia, parochialism, or stovepiping. Or (as my English friends might say) that marketing is simply missing the bloody point.

Let’s use a table to make things more concrete.

The first block shows that the company, with one small exception, is generally delivering on its pipegen targets and that they hit 105% of plan last quarter.

The second block shows that our friends in sales are struggling. Sales performance has consistently decreased for the past six quarters, from beating plan with 109% to coming up well short at 82%.

The third block shows that while pipeline conversion has been pretty stable at around 34%, starting pipeline coverage has been steadily deteriorating from 3.1x to 2.4x. Most companies can’t make plan when starting with 2.4x coverage. It’s clear that we have a starting pipeline problem.

But the fourth block shows that while the performance across pipeline sources is somewhat varied, that we don’t have an overall pipegen problem. While SDRs and sales are struggling, their contributions are a small part of the mix (10% each) and the gap is more than offset by above-target contributions from marketing and alliances. Moreover, because alliances pipeline usually converts at a higher rate than SDR- or sales-generated pipeline, the mix change should impact yield favorably.

So, what the heck is happening? How are we consistently beating our pipegen targets, but consistently behind on starting pipeline? Three thoughts come to mind:

• Our model is wrong. We built a model for pipeline generation targets that relied on assumptions about win, loss, and slip rates as well as pipepline expansion and shrinkage. Somewhere that model is deviating enough from reality that we are hitting pipegen goals but missing starting pipeline coverage goals. Maybe we made mistakes in the first place or maybe reality has drifted away from that model. But let’s remember that God didn’t send us the model on stone worksheets and that hitting model-driven targets is not the point. Generating sufficient pipeline coverage is.
• The most common reasons for model drift are decreased win rates, increased average sales cycles, and decreased average deal sizes. But here we’re seeing healthy and consistent week 3 pipeline conversion which makes me want to look elsewhere for an explanation.
• This is actually a tricky situation to diagnose. We’re hitting increased pipegen targets, but starting pipeline is flat. The normal diagnosis would be increased loss and/or slip rates, but starting pipeline conversion is both healthy and consistent. Hum. This leads me to think that timing is off — while we’re generating the right amount of pipeline, not enough of it is landing in next quarter, suggesting that buying timeframes may have lengthened. This is one reason why I care so much about pipeline segmented by timeframe and not just rolling four quarters or all-quarters (aka tantalizing) pipeline.

Back to our main argument: the point of the entire pipegen machine is not to beat model-driven pipegen targets. It’s to give a sales a chance to make the number each quarter. And that is far better measured by starting pipeline coverage than by pipeline generation. And that’s why great marketers look starting pipeline coverage first and then pipeline generation after that.

Good marketers say, “I hit my marketing pipegen goals. Go me!” Great marketers say, “We helped tee-up sales for success this quarter. Go us!”

And the best marketers don’t think their work is done at stage 2 — they know there’s plenty marketing can do both to increase close rates down the funnel and expansion in the bow tie thereafter.

But that’s the subject of another post.

The post Why Great Marketers Look at Pipeline Coverage, Not Just Pipeline Generation appeared first on Kellblog.

There are few symptoms, other than the odd cringe that often goes unnoticed. But the outcomes are clear if the reasons are not:  you’re omitted from meetings, passed over for promotions, underperform in nine-box reviews, find advisors suddenly too busy to work with your company [1], and VCs surprisingly passing on your financing round [2].

The problem is that nobody wants to tell you what’s wrong. It’s like this scene from Young Frankenstein.

Frankenstein: You know, I don’t mean to embarrass you, but I’m a rather brilliant surgeon. Perhaps I could help you with that hump?

Igor: What hump?

It’s just easier to move on.

But make no mistake, these problems can kill your career.  Or, more commonly, simply stall it out. Nothing is more lethal to a career than these eight words: “Kelly’s a great director, but not VP material.” [3]

Savoir faire is related to savvy in English, but not quite the same. I think of savoir faire as know-what as opposed to know-how [4]. Diplomats have great know-what when it comes to knowing what to say or do in a delicate situation. Vintners have great know-how on the subject of making wine.

Savoir faire is thus a social skill and largely about adherence to unwritten rules of conduct. In Silicon Valley, land of engineers, we don’t talk much about social skills [5]. We prefer our numbers, facts, and figures.

But to say these rules are unwritten is not to say they don’t exist [6]. I’ve been repeatedly reminded of them in the past few months, courtesy of several encounters with people who were simply, for lack of a better term, doing it wrong.

More interestingly, when I tried to give these people feedback, the reactions I got were defensive and sometimes hostile. You can argue this is about me, my direct style, or my not having earned the right to offer such feedback. But I believe it was primarily about the nature of the feedback itself.

While giving feedback is always tricky — see Jason Lemkin’s recent announcement that he’s going to stop giving feedback in 2024 — feedback about savoir faire is different:

• It’s personal, about behavior, and thus even more likely to cause offense
• It’s seemingly subjective or arbitrary, particularly when you don’t know the rules
• It’s not up for debate. We’re not debating strategy A vs. B. I’m telling you that you don’t treat a successful founder who just sold his company for $600M like a used-car salesperson.

And this is why most people don’t offer it. There’s almost nothing but downside. It’s easier to simply:

• Not invite the guest who talked politics to the next dinner party
• Not return the recruiter’s call seeking a blind reference on the executive who wrote an inappropriate social media farewell thread
• Become too busy to advise the executive who bungled the introduction to a high-value contact
• Ghost the founder who wants copious free advice, but who just can’t get around to signing an advisor agreement
• Say “I need to go mingle” to the clueless employee who challenged you on the utility of MBAs at the company reception [7]

It’s simply not worth the time or risk to explain to these people how dumb they look. And they probably won’t listen anyway. So on they go, like Igor with his hump, unaware of the problem, yet inexplicably not getting results that they want.

If I were to make a tagline for this problem it’d be this:

Savoir Faire: If You Don’t Have It, You Don’t Know.

Almost definitionally [8].

So, this is why, if you ever find yourself lucky enough to be on the receiving end of “you’re doing it wrong” feedback, you should do the following:

• Realize that you’re receiving it. Think: “Hey, we’re not debating options here. I’m being told I mishandled a situation.”
• Understand that you’re being offered a gift. The giver certainly knows it’s potentially offensive, but is offering it nevertheless.
• Recognize that this is likely your last interaction with the giver if you don’t handle the situation well.
• Avoid defensiveness or debate. They’re telling you the equivalent of “use the silverware from the outside in” or “don’t wear white pants after Labor Day.” They’re not looking for a debate.
• Avoid rationalizations like “oh, they’re old and that’s how it used to be done.” A lot of things change in Silicon Valley over time. But a lot don’t.
• Consider the possibility that they may be right. You obviously disagree because they wouldn’t be offering the feedback if you didn’t. The odds of them being correct increase with the size of the experience gap between you.
• Thank them for offering the feedback. Remember that most people wouldn’t, their intent is likely good, and they probably wouldn’t bother if they didn’t see something they liked.
• Apologize if you feel it’s indicated. I literally once told a founder, “you mishandled that introduction so badly that it embarrassed me,” and was told in response, “there’s no reason it should have embarrassed you” [9]. This three-fer wiped out working with me, my friend, and quite probably the VC firm that referred him to me. All because they refused to hear what I was saying (“you embarrassed me”) and offer a simple apology in return.

I hope this post helps you develop your career by explaining why, when someone tells you that you’re doing it wrong, that you should perk up and listen.

# # #

Notes

[1] Despite oddly having time for an exploratory meeting.

[2] Despite saying, “we love what you folks are doing and stay in touch.”

[3] Often, in my experience, such damning statements go unchallenged in a group setting, particularly when made by the boss.  The typical response is nods, “oh yes,” “it’s a shame,” and “the world needs directors, too.”

[4] This is an imperfect simplification.  I remember it by defining know-how as about tying a bowline or making beef bourguignonne – i.e., knowing how to do something – technical knowledge.  Know-what is knowing what to say or what to do, particularly in a difficult situation – social knowledge.  Unfortunately, you could just as easily define the latter as know-how:  knowing how to behave at a funeral or knowing how to handle a drunk relative at a party. 

[5] And when we do it’s about arguably naïve ideas like radical transparency. While I like it as a correction to our default behavior, taken literally, it breaks down quickly:  are you going to be radically transparent about the CRO who steps down to enter a rehab program or the planned acquisition that falls through?  In the latter case, such transparency is not only ill-advised, it’s typically a breach of contract (e.g., an NDA).

[6] This begs a question about privilege. You could (correctly) argue that a wealthy upbringing teaches you proper table etiquette at a country club and thus that people who grew up in the bottom wealth quartile would not typically have access to that knowledge.  (See the infamous SAT oarsmen-to-regatta analogy question.)  But I am not talking about either standardized tests or immutable traits, I’m talking about behaviors that can be learned.  While privilege might afford you the advantage of already knowing some of these things, it might not. And the good news is that, either way, they can be learned if you are willing to admit there might be things you don’t know, and if you are lucky enough to encounter someone willing to teach you. 

[7] In this case, the perpetrator was the 28-year-old me squandering a once-in-a-lifetime chat with Bob Waterman, co-author of In Search of Excellence (and a Stanford MBA), by asking a stupid question. You see, there’s a reason why I’m passionate about this topic. I needed a lot of help myself and was usually — but not always — lucky enough to get it.

[8] A derivative of a tagline we once used at Business Objects — Business Intelligence: If You Have It, You Know.

[9] I’m the one who decides if I’m embarrassed, not you.  Whether you would have been, or whether you think I should have been, is beside the point.

The post Hump? What Hump? Why You Should Listen When Someone Says You’re Doing It Wrong. appeared first on Kellblog.

Founder mode became all the rage last week, following a Brian Chesky speech (whose contents are seemingly not available online) and a Paul Graham blog post about that speech.

Since everyone’s weighing in on founder mode, and a few long-term readers have specifically asked for my take, that will be the subject of this post.

Founder-mode is defined in opposition to manager-mode, the conventional wisdom as taught in business schools, about how to manage an organization. The basic idea is that b-school teaches managers to delegate and empower, lest they be guilty of micromanagement. But, in founder mode, founders dive heroically into details penetrating the gaslighting of their value-destroying C-level execs, which include — and this is an actual quote — “some of the most skillful liars in the world.”

Let me start by saying I have a 270+ degree view on this problem: I advise and sit on boards (so a well meaning advice giver), I’ve been the CEO of two startups, and I’ve also been one of those (presumably duplicitous) C-level execs. But the one thing I’ve not been is a founder. I’ve worked with plenty of them. I’ve been appointed CEO of a company with a founder staying on the team for years alongside me.

Without re-stating the article or the debate about it, let’s just cut directly to my take:

First, we are not the same. It’s an excellent point and one that needs stating. Now, usually I cover it from the other side. And I’ve gotten myself into (at times, deep) trouble by not recognizing these differences. As a professional manager:

• You have less moral authority to drive change. It’s not your baby after all. You have positional power, but not necessarily moral authority.
• You are replaceable, some might say disposable. You’ve been hired to do a job and we can hire someone else if and when we need to.
• You get fewer mulligans. You have more short-term accountability. Hired CEOs should think in quarters. Founders (perhaps unless you’re public) should think in years. Or maybe even eras.
• You are assumed to be around for an evolutionary phase. If you’re proven in the $20 to $100M size range, maybe we run with you to $200M. But at some scale, unless you’re absolutely crushing it, the board will ask if now’s the right time to hire the next-phase booster CEO.

Second, founders should therefore manage differently from professional managers. Why wouldn’t they? They have more moral authority. They are forever. They are irreplaceable. They get more “lives” as a result. And they have no “sell by” date stamped on their forehead. They are — and I think A16Z had a lot to do with swinging this pendulum back — presumed to be the best person to run the company. Just knowing these differences, how could you ever argue that founders should run a company the same way as a professional manager?

Third, I find most of the arguments against the conventional management wisdom to be strawmen. In business school, I wasn’t taught to never do skip-level meetings or to only meet with the executive staff. I did learn, from both school and work that “listen bottom-up, direct top-down” was a great way of operating. I wasn’t taught to hire great people and get out of their way. I was taught to try and make a distinction between delegation and abdication because you need to empower executives to take on their own challenges, but still hold them accountable for results.

I wasn’t taught that you couldn’t have 60 direct reports, but we did discuss the pros and cons of a large span of control. (And personally, I’ve always hated 1-1s but that started back when I was in technical support and forced to have them with my manager.) I did learn that managers would game me with metrics (i.e., “what gets measured, gets managed” — and not always in a good way). And I did learn to use metrics and surveys to see inside organizations and cut through the layers and find out what customers and employees were thinking.

I take massive exception to the characterization of C-level execs as gaslighters and liars. Yes, they try to paint their organization in the most positive possible way, but the good ones understand the difference between spin and lies. And they answer direct questions with short, direct answers.

Now I don’t doubt what Graham says is true. That many founders get well meaning advice about becoming more hands-off with the business and that can result in founders feeling pushed up and out of their own businesses. And I do believe that people are too quick to accuse bosses of micromanagement — and bosses too quick not to push back.

Aside/example, back when I was a $1B CMO: “Well WTF is my job anyway if not to ensure all our marketing output is of high quality? The marketing quality buck stops here. And who is some PR flack with two years’ experience to accuse me of micromanagement? And, in a true quote: “you say this is a hostile work environment? Well, let me think. Actually, you know what? I want it to be a hostile work environment for people who write like you do.” Yes, I wasn’t about to win any sensitivity awards, but I did have a sense for what my job was, despite the hinderance of being both C-level and an MBA.

But I feel like it’s a giant game of telephone where the signal gets lost across the hops. Think: yes, you need to empower people more and I might have said “get out of their way,” but I never actually meant to not inspect their work or meet with their direct reports. I suppose the real moral here is to have deep conversations with your advisors. So you get beyond the slogans and soundbites into what they really mean. For what it’s worth, I’d argue that Chesky seems to agree, based on this video released subsequent to the Founder Mode brouhaha.

While Silicon Valley is the world’s finest innovation machine and one of the world’s finest wealth creation machines, I don’t think we’re a paragon of managerial practice. Thus, to infer causality between “founder mode” and “great businesses” is a bridge too far. We make great businesses. We have some great managerial ideas (e.g., OKRs, pod organizations, or going back to shortly after the big bang, The HP Way). We’re strong on disruptive strategies and systematic expansion strategies. But to say that great technology businesses were created because of, irrelevant to, or in spite of our management practices, I’m not sure. Don’t be too quick with invoking post hoc, ergo propter hoc when you see an appealing idea in Silicon Valley.

I’ll close by citing one response to Founder Mode that takes a data driven approach to these questions, based on research with 122 founders, 50 personality elements, and 46 different areas of 360-degree feedback. It’s an article worth reading and concludes that the benefits of founder mode are largely a myth.

Much like the myth — that I first learned about in business school — that captains should grab the yoke and fly the plane alone in an emergency. It turns out that cockpit crews get far better performance by maximizing teamwork and communication in emergency situations. Coincidentally, this practice is called CRM (crew resource management).

I understand the attraction of founder mode. It sounds cool. But while it’s more romantic to imagine the founder grabbing the yoke to drive the company, it’s more effective to have a strong team working together to face challenges.

The post We Are Not The Same: The Obligatory Post on “Founder Mode” appeared first on Kellblog.

I’d known this company for a long time.  I’d helped their VCs with diligence.  I’d worked with them in the early days as an advisor, and received a stock option as compensation.  So when I contacted the company to sell the shares (from the option I’d exercised long ago), I was surprised to find a folder called “Grant 2.” They’d given me a second grant that I’d actually forgotten about.  OMG, this was my lucky day.

I told the stock administrator that I wanted to exercise the second option grant. 

“You can’t,” she said.  “It’s been canceled.”

“Why?” I said, watching a G-Wagon disappear before my eyes.

“Because you discontinued your service to the company.”

“No, I didn’t.  You just stopped calling.  That’s the nature of advisory work.  You call and I answer.”

This launched a multi-week argument about if and when I’d stopped providing services and many questions about why I’d not been notified of any termination — so I’d have at least known the 90-day option exercise window had started.  The dispute eventually escalated to the GC who, in firing me, started my 90-day clock, admitting the option was still valid.

Why do I tell you this story?  First, because it’s hilarious.  The GC could easily just have said he’d honor the option, but instead chose to communicate that good news by firing me.  Second, because this is what happens when you use paperwork written for employees to engage with advisors. 

In this post, I’ll share what I’ve learned over the past fifteen years advising startups to give you a better idea of what to do when someone asks you to advise their startup.  See my disclaimers, remember that this is not legal or financial advice and that you should hire professionals to get advice specific to your situation.

When asked to be an advisor of an early-stage company, I think you should do four things:

• Decide if you want to advise the company
• Discuss and set clear expectations on both sides
• Agree on compensation, which is typically but not always, stock
• Put a good, quick legal agreement in place

Decide If You Want to Advise the Company

This may seem like an obvious first step, but sometimes the answer really should be no. There are lots of good reasons to advise a company.  For example, you can:

• Build your network with the company’s team and investors
• Learn by seeing more business situations
• Sow seeds for potential downstream relationships (e.g., employee or board)
• Pay it forward

But there are also some good reasons not to advise a company, including:

• Bad chemistry.  Life’s too short.  Avoid deals where the investors are trying to impose you as an advisor to a reluctant executive.
• Poor fit.  You want to be asked about problems you’ve seen dozens of times.  You’ll give better answers and it will take less of your time.
• When they have a one-shot problem.  Advising is a long-term, relationship game.  If a company just needs help with one thing (e.g., positioning, re-organization), everyone could be better off if you do a cash consulting deal or just offer some input for free.
• Restrictive terms in the advisor agreement, such as non-solicitation, no-hire, or non-compete agreements.  Once you’re advising company A, you may discover that you wish you were advising company B.  While switching horses may or may not be prohibited under the agreement, it’s always bad form and bad for your reputation.  This is now the top reason I decline advisory deals. There is an opportunity cost associated with advising that many overlook. 

Discuss and Agree on Clear Expectations

I don’t think this needs to be contractual — or even necessarily written down since most agreements feature bilateral termination for convenience anyway — but I do think it’s important to have a detailed conversation about expectations. 

These are all great topics to include in that conversation:

• The specific areas where the company wants help.  For me, that might be SaaS metrics, marketing, board presentations, and CEO mentoring.
• The form of the help.  Some people want you to build their financial model; I just want to provide feedback to your finance head who can then iterate and improve it themself.
• Interaction frequency.  For me, that’s typically measured in times per month.
• In-office attendance.  Some of my friends like to show up once/week and have watercooler encounters to really get to know the company and its scuttlebutt.  Me, not so much.
• Periodic vs. ad hoc meetings.  I like to meet because there’s something to discuss, not because it’s Tuesday.  But different people see this differently and compensation structure impacts this as well.
• Time commitment.  Some people like to agree on hours/month, I don’t.  When you buy experience, you’re paying for fast answers, so hours is the wrong metric.  Value is measured by how quickly I can solve a problem, not how slowly.

Agree on Compensation

Because I’m talking about early-stage (e.g., seed, pre-seed) advisory, the typical company doesn’t have much cash and wants to pay advisors in stock, typically measured in the tenths of a percent. 

Let’s do some sample math which will rely on a bunch of assumptions:

• Shares granted:  0.2%
• Anticipated dilution before exit:  50%
• Shares after dilution:  0.1%
• Assumed exit price:  $500M
• Value:  $500K
• Probability:  5%
• Expected value:  $25K 
• Hours invested over 7 years at 2 hours/month:  168 hours
• Expected value, hourly rate:  $149

You might see the $500K here and think “amazing money,” and you’d be right — in the unlikely case this actually happens. In a more realistic $500M exit scenario, those shares might be worth $250K due to debt and preferences.  In a dire scenario, e.g., with multiple liquidation preferences and a large amount of capital raised, they might be worth $0. 

(Note that this example assumes no debt, no participating preferred shares, and an exit value that clears the preference stack by enough that all investors take their pro rata ownership instead of their preferences.)

So, to go back to the point – the majority of these deals pay $0 to the advisor. When you average out the occasional big hit across the rest you might end up making $150/hour.  And the cashflow is lumpy. It’s a fun hobby, but it’s no way to make a living.

So, when evaluating an advisory opportunity, do the math and run some scenarios.  Then think about the commitment you’re willing to make. And, more than anything, do the work because it’s fun and you grow by doing it — not because you’re banking on any big hit.

If you consider any compensation whatsoever as upside, you’ll always be happy — and maybe sometimes when you get lucky — very happy.

Put a Good, Quick Legal Agreement in Place

The problem here is that neither side is interested in spending money on a good advisor agreement. The cash-starved startup, running on the founder’s savings, doesn’t want to. Nor does the would-be advisor, who’s well aware their most likely outcome is $0. Who wants to spend a few thousand in legal fees on that?

So you end up running on a stock-option agreement written for employees that you repurpose for advisors (which was the case with my company in the opening story). Or a consulting agreement. Or a simple letter. Or nothing at all.

None of that sounds very good to me.

What do I do in this situation? I start with the FAST advisory agreement.  I like it because:

• It’s standard. To my knowledge a lot of people use it.
• It’s neutral, not having been created by either the company or the advisor.
• It covers many of the obvious basics (e.g., non-disclosure, independent contractor relationship, non-conflict).
• And, if you want to use their matrix, it even includes compensation.

I’m not a lawyer (and you should ask your own), but the FAST agreement strikes me as doing a reasonable job of providing a stage-appropriate agreement for advising an early-stage company. Per the Founder Institute who’s created it:

The FAST Agreement is used by tens of thousands of entrepreneurs and advisors per year to establish productive working relationships, trading advice and support for a standardized amount of equity. […]

With just a signature and a checkbox on the FAST Agreement, entrepreneurs and advisors can agree in minutes on how to work together, on what to accomplish, and on the right amount of equity compensation.

The only catch is that while I like the compensation matrix and the idea of using stage vs. expertise to determine compensation, I don’t like the specific definitions in Schedule A because, among other reasons, they include fixed time commitments that I think are too high.  So I either negotiate them out or rewrite that schedule.

I hope this post will help you in thinking about your next steps when and if someone asks you to advise their startup.

The post You’ve Been Asked To Be a Startup Advisor: Now What? appeared first on Kellblog.

• This isn’t easy. Though I’d never thought I’d say this: it’s given me some empathy for early-stage venture capitalists. Be prepared for ten-year-plus exit horizons and to lose every penny you put in.
• I mock VCs (a little) less. It’s easy to mock VCs for being too fashion-driven, too pedigree-oriented, and too herd-like in their behavior. But when I’m actually in their shoes evaluating seed-stage opportunities, I see myself adopting some of these very same behaviors. (Think: “it’s an AI data play, the founders dropped out of Stanford PhDs, and DCVC is leading the round? I’m in!!”)
• Access is everything. You can’t invest in a deal that you never see. This leads me to believe that you should only consider angel investing if you have access to amazing group of people who you think will do great things in the future (e.g., by having worked together at a startup seemingly destined to spawn many more).
• I can see the power law at work. While I have confidence that my returns will be quite good in the end (and marking them to market today is largely meaningless), I can already see that they will be concentrated in a few superstar investments.

I learned one of my more impactful lessons at an A16Z event a few years back. After the presentation, I went up to one of the partners and we spoke:

DK: Hey, I was looking at XYZ Co the other day thought you might be interested in taking a look at them.

A16Z partner: Yes, we know them. I recently had a few team members do a full run-down on that space — there are about a dozen companies in it — and we decided that while we like the space, they weren’t the team we wanted to bet on.

And a light bulb went off. When I find an seed-stage deal, I evaluate the founder, the story, the product, and then make a decision. When professionals find a seed-stage deal, they put a team of three MBAs on finding and evaluating every startup in the emerging space.

That’s a big difference. I never know if they’re the best team in the space. I can only know if I think they’re impressive.

This observation backed me into what I now use as my four-question framework for evaluating angel opportunities:

• Why this idea? Do people need this? Will they pay for it? How big can it be? Is this really a company or just a feature?
• Why now? Is this idea too far ahead of its time? Or, is this company too late?
• Why this company? Do they have a technological head start? Do they have uncommon expertise that is difficult or impossible to replicate?
• Why this leadership team? Why is this particular group of smart people (hint: they’re all smart) going to win? What is special about the founders that makes me believe they will beat everyone else and/or change the race as need be?

I know many such frameworks exist. I’m not making any uniqueness claim on this approach. I am saying, however, that this is my current framework when wearing my angel investor hat. And I think it’s really useful for founders to switch perspectives from time to time, so they can get a better sense for what the people on the other side of the table are thinking.

And, in the end, they’re thinking like investors. The more you can too, the better you’ll do when fundraising.

The post The Four Key Questions About Every Angel Investment Opportunity appeared first on Kellblog.

Because this is focused on incubation-stage startups, we walked mostly about $0 to $1M (or $2M) issues, both how to win deals as founder (largely covered through Q&A) and how, once you’re winning them, to transition from founder-led sales to sales-led sales. I’d say there are four key parts of that process, overall.

• Throw spaghetti at the wall and see what works
• Identify key clusters on the wall — particularly those with smart, well-informed prospects who bought from you (anyway). We can’t scale luck or chance.
• Hire your head of sales 6-12 months before you want to build a sales team. And then lash yourselves together so you experience all GTM leaning together. Then free them to go build a sales team and teach them what they have learned.
• When it comes to hiring, we don’t want people learning how to the job (e.g., managing a small sales team in the prior bullet). We do want them learning how to do the job here. Beware the difference.

The slides are embedded below and downloadable as a PDF here.

The post Slides from a Balderton Launched Go-To-Market Workshop appeared first on Kellblog.

And, by the way, I’m not joking with that answer. I’m not saying you need 17 patents. I’m not saying you need any patents at all — but you probably do. I am saying that despite the frenetic life of running a startup, you need to step back from time to time — maybe once a year or maybe on a cycle before your product releases — take a minute to learn (or remind yourself of) the basics of patents, and then sit down with your technical leaders and your intellectual property (IP) counsel to have a chat.

That is, you may not need to file any patents right now, but you do need to understand patents and devise your company’s strategy for them.

Waiting is not a great option for two reasons:

• If you don’t have patents when you need them, it will be too late
• You can lose important rights to your inventions if you fail to patent them before certain milestones

To help you with this process, my Balderton colleagues Dan Teodosiu, Andrew Wigfall, and I recently published a Balderton Perspective entitled: Why Your Startup Needs a Patent Strategy.

The three of us work as EIRs at Balderton, where our role is to advise portfolio companies on operational matters. Dan’s a product/technology guy, Andrew’s a lawyer, and I’m a marketer and former CEO. So we’re kind of the perfect trio to write such an article and provide a holistic view of the subject.

The piece discusses the following topics:

• Why to file patents
• A quick introduction to patents
• What a patent strategy is and why you need one
• How to define your patent strategy

I encourage all founders and technology or product leaders to read it and then get cracking on your company’s patent strategy.

The post Does Your Startup Need a Patent Strategy? appeared first on Kellblog.

We ended up not duplicating Car Talk’s radio call-in format, and unlike many podcasts, we’ve also chosen not to center the podcast around guest interviews. Instead, we talk — some might say bicker — for about 20 minutes roughly every week about SaaS metrics, benchmarks, and reports. We are the perfect treadmill companions.

For example, some of our recent episodes were focused on SaaS metrics like the Burn Multiple, the Rule of X, and the LTV/CAC ratio. We’ve also recently covered the Rubrik S-1 and the 2024 Bessemer State of the Cloud Report. This week we published our 50th episode, which was on the OneStream S-1 and subsequent IPO.

Because these topics can be, well — kind of dry — we try to have some fun with the podcast, squeezing in the odd bad (and some would say, “dad”) joke and generally trying to seize any opportunity to lighten up the content. One example is our legal trailer which not only covers important legal terms, but does so in a light-hearted way. (Then again, we rarely get feedback on the jokes, so I wonder if people aren’t listening to the trailer. It couldn’t possibly be that the jokes don’t land.)

So in the spirit of fun, we decided to do some Car Talk-style credits to celebrate our 50th episode. Here they are. While the first three rows are real, the rest are Car Talk-style. You can listen to the credits at the end of the episode.

The SaaS Talk Official Credits

The post The SaaS Talk 50th Episode Credits appeared first on Kellblog.

Driscoll translated this concept to SaaS, creating a line that provides, across a range of ARR sizes, a growth rate below which a company is not on a venture-backable trajectory. In short, if you’re above the SaaS Mendoza line, VCs will want to invest in your company, and if you’re not, they won’t. So it’s an important concept and one that helps answer a very difficult question for founders: how fast should we be growing?

Here’s the Mendoza line from Rory’s original post:

Note that the Mendoza line is a growth trajectory rule and thus should be considered along with other growth trajectory rules and metrics like T2D3 (triple, triple, double, double, double) by Battery’s Neeraj Agrawal, the growth endurance observations periodically discussed by Janelle Teng of Bessemer, or my Rule of 56789 published with my then-colleague at Balderton, Michael Lavner.

The inspiration for today’s post was a recent update to the SaaS Mendoza line published by Scale’s Eduard Danalache in July. Since the line changed with this update, I’ll refer to the original as the 2018 Mendoza line and the new one as the 2024 Mendoza line.

Now, let’s dive in.

The Two Assertions Behind the 2018 Mendoza Line

O’Driscoll said the Mendoza line was based on two assertions (paraphrased):

• That most venture investors prefer to invest in companies with a chance to become standalone public companies. Looking at the (then-)realistic low bar of what that takes, this implied ARR of $100MM at the time of IPO, while still growing at 25% or greater.
• That, most of the time, growth rates decline in a way that is fairly predictable. For a best-in-class SaaS company, the growth rate for any given year is between 80% and 85% of the growth rate of the prior year. Scale refers to this as growth persistence and argues this assumption holds true from about $10M on.

Some quick comments:

• My how times have changed. Last week, I heard OneStream, at nearly $500M in ARR, referred to as “on the small side” for an IPO today [2]. That’s five times bigger than Rory’s bar, set only six years ago. Since a viable path to IPO is inherent in the definition of the SaaS Mendoza Line, the math needs to be updated to account for this.
• What Scales calls “growth persistence” is now commonly called growth endurance. I’ll use the latter term henceforth.

There were some objections to the Mendoza line when it was introduced and Scale responded to them with a follow-up post. It’s a good read, but I won’t dig into it here. The headline news is simple: somebody needs to update the math.

The 2024 Mendoza Line

Last month, Scale released an update entitled, The Path From Zero to IPO: Revisiting the Mendoza Line in 2024.

The first thing they did was change the IPO criteria:

For the sake of this analysis, we’ll use a more ambitious target of $250M ARR growing at 25%, with a clear path to profitability. Again, don’t take this as gospel truth for when to IPO, but for the math to work we have to draw a line in the sand to aim for, and we believe this is a fair target in today’s world.

While this raises the bar significantly, I think it’s still too low. Anecdotally, per some recent Meritech S-1 breakdowns:

• OneStream just went public with ARR of $480M growing at 34%
• Rubrik went public earlier this year with $784M of ARR, growing at 47%
• Klavyio went public in 2023 with $658M of implied ARR, growing at 51%

The last Meritech breakdown that resembles their target is Hashicorp, which went public in 2021 with $294M of ARR but was growing at 50%. But that stock, after some initial highs, was basically a dud in the public markets, so it’s perhaps not the ideal case study.

If I had to guess, I’d say the IPO bar today for software companies is more like $400M growing at 40% than $250M growing at 25% [3]. Many, me among them, would argue that the bar is much higher than it needs to be, but there are things we can’t control and this is certainly one of them.

Once you pick the destination (in terms of size and growth rate) and the growth endurance factor (Scale picks 85%), the rest is just a math problem.

But with one catch. Where do you start? The 2018 Mendoza line chart goes all the way down to $1M in ARR [4]. In the 2024 version, they basically say we don’t care how you get to $10M, but once you get there the Mendoza line takes effect.

Here’s the chart they published that compares the 2018 and 2024 Mendoza lines.

And here’s me backing into a curve based on the targets for ending ARR, growth rate, and growth endurance [5].

Running this model forward is all just about powers of 0.85. You pick a starting size, growth rate, and GE factor. You then use the GE factor (85%) to shrink the growth rate every year. So, for example, after 3 years your growth rate 0.85^3 = 61% of what it started at.

The problem with powers of 0.85 is they don’t scale very well. Scale realized this in scaling down, hence the 2024 advice to apply the rule only at $10M+. But, by picking the $250M ending target, they also avoided a degree of scaling up, pushing the rule to the edge of where it stops working because after about 10 years the growth rates it produces are too low. For example, the year 11 the growth rate is only one-fifth of what it was at the “start” [6].

So I’d say the 2024 Mendoza line is decent, but it doesn’t scale infinitely and is best used within a range, starting at $10M and for about the next 10 years.

Comparison to Other Growth Trajectory Rules and Metrics

Let’s conclude by comparing the Mendoza line to other rules and metrics for thinking about startup growth trajectory. Namely:

• T2D3, which says once a company hits $2M in ARR it should seek to triple twice and then double three times.
• The Rule of 56789, which says that startups should seek to break $10M in 5 years, $20M in 6 years, $50M in 7 years, $75M in 8 years, and $100M in 9 years [7].
• The 85% growth endurance rule, which says you should pass $1M at some very high growth rate, then retain 85% of that growth every subsequent year [8]. I only now realize this is essentially a Mendoza line rose by any other name — which highlights a disadvantage of using catchy names (e.g., magic number, Mendoza line) over descriptive ones [9].

Here’s a tabular comparison of these rules [10]:

The outlier is T2D3 which I suspect has some lingering ZIRP growth-at-all-costs logic built in. The other rules tend to generate similar trajectories, none of which (by the way) get you to an IPO in 12 years. This is consistent with what I’m guessing is today’s median of around 14 years to IPO. More than ever, building a startup from inception to IPO is a marathon, not a sprint. Spend your energy accordingly.

Finally, for those who prefer charts, here is a visual comparison of those trajectories.

In this post, we’ve examined the 2024 Mendoza line for SaaS and learned a few things in the process:

• That the 2018 Mendoza line is hopelessly out of date given the market changes in IPO requirements. This might explain why it never became as popular as other Scale creations like the magic number.
• That since top VCs want to invest in companies that have a shot of going public, that founders should keep an eye on growth trajectories and the outcomes to which they lead. Specifically, if you’re clearly on a trajectory that cannot lead to an IPO, maybe should raise money through PE, regional and/or lower-tier VCs with lesser ambitions, venture debt, or revenue-based financing.
• That the Mendoza line is a fancy way of saying retain 85% of your growth each year and (in the 2024 version) that you should start applying it after $10M. Personally, I’ll just use the 85% growth endurance rule as I think it’s simpler and comes without the somewhat arbitrary provisos.
• That these rules only work within certain zones. T2D3 works from $2M to $144M and is undefined after that. The 2024 Mendoza line works from $10M to $250M, but beyond that produces growth figures that are too low. The 85% growth endurance rule works across the broadest range, but relies on starting at small size with an amazing growth rate from which to decay.
• That rules reflect the environment in which they were created. The 2018 Mendoza line took you to $100M, which was Scale’s assumed IPO bar in 2018 [11]. T2D3 has an in-built high-growth bias reflective of the ZIRP era and best applied today only in greenfield markets where you have lots of capital available (e.g., AI).
• That God did not decree that growth needs to decay every year. While this is certainly a common pattern, I have run startups where we accelerated growth (i.e., GE of >100%) and the average growth rate in Meritech’s public comparables is 19% today, higher than the age-driven growth rates which the Mendoza line would imply [12].

Thanks for reading. The spreadsheet I used in making this post is here.

# # #

Notes

[1] Fans will be happy to know that Mendoza ended his career with a .215 batting average. The lowest hitting shortstop today is batting .219.

[2] To really blow your mind, back at Business Objects, we went public in 1994 off $30M in revenues, which was fairly normal at the time. The IPO bar has gone way, way up over the decades and changed many things in Silicon Valley as a result. For example, creating the entire asset class of VC/PE growth equity which was unnecessary when companies went public with a $100M round off $30M in revenues.

[3] The IPO bar is ever-changing, somewhat ill-defined, and not something you can easily get data on unless you’re friendly with a bunch of investment bankers. For more data, go here, here, and here.

[4] Which is odd because the text of the article says the growth endurance behavior doesn’t start until $10M. I suspect the comment was added after the initial posting and the spreadsheets weren’t changed.

[5] Note that Mendoza line is presented as a curve which makes me think they calculated the equation for this curve and then plugged in the nice neat 10, 20, 30, etc. ARR sizes along the bottom. See my spreadsheet where I do that using an Excel trendline and accompanying formula.

[6] That is, your growth rate in the year in which you passed $10M.

[7] Reminder: this is about a trajectory and break means break, not hit. Some people misread the rule by translating the thresholds to growth rates which is not correct. By the way, those figures were arrived at by seeing what it took to be top quartile in the Balderton universe of data.

[8] I called it Growth Retention Rule in the 56789 blog post but don’t like how that abbreviates to GRR, so I switched to Growth Endurance here both to use today’s more commonplace language and avoid ambiguity around GRR (which means gross retention rate to most).

[9] It maps pretty well to the 2018 Mendoza line, though today’s now starts at $10M per Scale.

[10] Where the second and third rows are not the only possible trajectories, but each an example of a reasonable rule-compliant trajectory.

[11] I still think that was a low-ball estimate even in 2018.

[12] Though, in defense of Scale, they argue the Mendoza line is a tool for determining if you’re on an IPO trajectory and it was not designed to work beyond the IPO timeframe.

The post Startup Growth Trajectories and the SaaS Mendoza Line appeared first on Kellblog.

In this post, I’ll discuss what you can do about it – specifically, how you can apply some of the ideas I’ve discussed in Kellblog to troubleshoot go-to-market (GTM) performance.  I’ll focus on troubleshooting new business (“newbiz”) ARR plan attainment, the area where most companies seem to be having the most trouble [1].

Don’t Knee-Jerk Blame the Plan

The immediate temptation when missing plan is to blame the plan.  “It’s not realistic.”  “It was driven by the fundraise, not the bottom-up.”  But blaming plan is a poor place to start for two reasons.                        

First, you signed up for the plan when you submitted it to the board for approval.  Next time, if you don’t believe in a proposed plan, don’t be so quick to fold in the face of internal pressure.  Remember the old Fram oil filter commercial and think, “you can fire me now or fire me later” so if you’re asking me to sign up for a plan that I don’t think I can achieve, you might as well fire me now [2].  The need to make such difficult judgments is the price of admission to the sales leadership role.  Cop out at your own peril, because they will indeed fire you later.

Second, when you follow the approach in this post, if the plan is unachievable it will emerge from the data.  So, bite your tongue, avoid any initial temptation to blame plan, and instead go look at the funnel.

The Two Questions and Two Metrics

Recall in this post, I argued that you should ask two questions when you’re missing plan.   Every quarter: 

1. Are we giving sales the chance to hit the number?
2. Is sales converting enough of the pipeline to hit the number?

That’s it.  Everything comes down to these two questions.  No matter the root problem, it will be revealed in answering them.  Remember, the way to make plan for twelve consecutive quarters is one at a time.  So why not focus on next quarter?  And if you’re chronically missing plan, why not make a steady-state assumption to simplify things further? [3]

Starting with the above two questions makes things simple by breaking the entire funnel in two.  Simplifying the problem is important because you can quickly and irrecoverably descend into analytical quicksand.  When I first meet them, many companies are neck-deep in such quicksand, comparing dashboard clips, reports, and spreadsheets derived from different systems, lost in an endless sea of non-footing detail, having completely lost the business forest for the salesops trees.

Note that neither of the two above questions assigns blame.  As a consultant, I have the distinct advantage of not caring where the trouble is, making me a disinterested party, de facto impartial.  I encourage CXOs to adopt a similar approach, simply stating facts, avoiding blame (e.g., inferred causes), and acting as dispassionate analysts when analyzing GTM problems.  While you will eventually need to ask why you have certain problems, it’s always best to start with simple statements of fact, get agreement on them, and build from there.  For example:

• “We consistently start quarters with insufficient pipeline coverage” is a blameless statement of fact.  It does not say whose job it is to generate pipeline (if that’s even been detailed out across sources) or why they are failing to do so.
•  “We are converting a below-normal percentage of our week 3 pipeline,” less obviously, is also a blameless statement of fact.  While it’s clearly the job of sales to convert pipeline, the statement makes no assertion as to why we are seeing abnormally low conversion rates (e.g., pipeline quality, change in competitive market, sales execution).

When it comes to metrics, the first of the above questions is measured by pipeline coverage, more precisely week-3 pipeline coverage [4] [5]. The second is measured by a conversion rate, specifically week-3 pipeline conversion rate.  Notably, this is not a win rate, and please read this post to ensure that you understand why.

Are We Giving Sales a Chance to Hit the Number?

Make a chart like this one to answer this question.

Here you see, for newbiz ARR for the trailing nine quarters, week 3 pipeline dollars, week 3 pipeline coverage (pipeline/plan), ARR booked, week-3 pipeline conversion, and the pipeline coverage target implied by the week-3 conversion rate (i.e., its inverse).  Pipeline conversion rates are more interesting when viewed in conjunction with plan attainment, so I’ve added ARR plan and plan attainment as well. 

Analyzing this chart, we can see a few things:

• From 1Q22 through 1Q23 we converted about 33% of the pipeline
• We were also consistently hitting plan in that timeframe
• Starting 2Q23 we started with only 2.3x coverage, converted a healthy 40% of it, but still came up short, at 91% of plan. 
• That rough pattern continued in 3Q23 and 4Q23
• 1Q24 started with the weakest coverage in the past nine quarters (1.9x)
• While sales is forecasting record conversion of that pipeline (45%), we are nevertheless forecasting to land at only 86% of plan
• I’m not sure I believe the forecast because 45% conversion is borderline unrealistic and could simply be the CRO trying their best to hold the line

I conclude that this company is starting with insufficient pipeline.  That is, they’re not giving sales a chance to hit the number.  How do I conclude that?

• By comparison to pipeline coverage benchmarks.  3.0x is the typical pipeline coverage goal and you’ll note that in the good times (1Q22 through 1Q23) we consistently started with 3.0x+ and we consistently made plan. 
• By comparison to pipeline conversion benchmarks.  33% is a standard conversion rate.  Here we are running at 40%+, which is best-in-class conversion.  Pipeline conversion is not the problem.
• More importantly, by comparison to ourselves.  In our recent history, we consistently made plan when we started with 3.0x+ coverage and missed it when we started with 2.3 to 2.4x.  This quarter (1Q23) we’re starting with 1.9x, forecasting record conversion, and still only 86% of plan.

The solution to the insufficient pipeline problem is, unsurprisingly, to make a plan to generate more pipeline.

Here are some of the high-level steps in making that plan:

• Define pipeline generation targets across the four major pipeline sources.  It’s surprising how many companies don’t start with this basic step.  For bonus points, over-allocate the goals to target 110% of what you need. [6]
• I prefer to set these targets by opportunity count, not pipeline dollars, because I think it’s more visceral and less easily gamed [7].
• Do a cost/oppty analysis across your pipeline sources to get an idea of how much money any given pipeline source (e.g., alliances, demandgen) would need to create, for example, 20 more oppties next quarter.  Remember to focus on variable, not average, cost [8].
• Be sure to check with the leader of each pipeline source on their ability to absorb extra money to generate more pipeline.  If you have 12 SDRs reporting to one manager, they may need to bring in another manager before hiring 3 more SDRs.  Alternatively, sellers may have extra time on their hands and the ability to put more time into outbound.  Alliances may have a hot candidate they want to hire, but no open headcount, and could execute quickly if one were opened.  It’s not just about money; it’s about the ability to productively spend it.
• Accept that you may be overallocated to sales versus pipeline generation.  In this case, the best solution might well be to terminate the bottom N sellers and convert the newly liberated budget to pipeline generation — so that everyone else has a chance at success.  This is painful, but sometimes necessary, and after you’ve had to do it once, you’ll be more careful to plan holistically in the future.

This all goes without saying that no pipeline analytics will work if you lack basic pipeline discipline – i.e., if you don’t have clear definitions for stages, close dates, oppty values, and forecast categories, and if you don’t regularly enforce them via periodic pipeline scrubs.

The Floating Bar Problem

Before diving into pipeline conversion, let’s address a special case of insufficient pipeline:  one where the pipeline initially looks sufficient but burns off at an above-average rate across the quarter.  You can see this by looking weekly at to-go pipeline coverage.

What’s usually happening in these cases is that some material percentage of your week-3 pipeline is effectively fake.  This happens because, when pipeline is scarce and if sellers are under pressure to each carry 3x coverage [9], they will take lower-quality opportunities into their pipeline.  For example, long-shot oppties that appear rigged for the competition, immature oppties where sellers hope to create a buying timeline, or self-nurture leads that may only become real oppies in the future.

I call the tendency to work on lower quality oppties in tough times, the “floating bar problem” because sales silently lowers (or in good times, raises) the bar for admission into the pipeline.  This is insidious because the result is fake pipeline that creates an illusion of coverage which disappears as the quarter progresses.

The solution to his problem is simple in theory, but hard in practice.

• Sales management needs to hold the line on what gets into the pipeline, applying the same standards in tough times as good ones.
• If sales management wants to allow sellers to work on low probability “oppties,” that’s fine but, well, get them out of the opportunity management system.  Use tasks to track work.  But only promote a lead to an oppty when it meets the standard for being an oppty.

If, for example, SDRs are passing low quality stage-1 oppties to sales that should not show up in the numbers as a reduced pipeline conversion rate.  Instead, it should show up in a higher stage-2 rejection rate.  This point is completely lost on most sales managers so please make sure you understand it.  If you maintain pipeline discipline, lower quality oppties should show up not as a reduced stage-2 to close rate, but as an increased stage-2 rejection rate.  And pipeline discipline starts at stage 2 – where sales decides to accept or reject oppties.  It’s wrong to accept sub-standard oppties, pollute the oppty management system with fake pipeline, convert little of it, miss plan, and wreck the company’s pipeline analytics in the process.

I’m not trying to prevent sales from working on whatever sales management wants them to work on.  But I am saying one thing:  whatever they are, don’t call them oppties in “my” oppty management system if they don’t meet the defined standards for oppties [10].

Is Sales Converting Enough of the Pipeline?

While it’s the job of sales to convert pipeline into ARR, that doesn’t mean sales execution is the only factor that drives conversion rates.

Here you see conversion rates plummeting, dropping by 11 percentage points between 1Q23 and 2Q23 and then by another 5 percentage points by 4Q23.  By the 1Q24 forecast, the pipeline conversion rate has been effectively cut in half from ~32% to ~16%.  Note that during the recent dark times (from 2Q23 to 1Q24) we have been starting with ~3.0x pipeline coverage, but converting so little that we’re landing in the dismal range of 47% to 65% of plan.

Let’s assume we have the operational basics covered, so this is real pipeline, validated and scrubbed by sales management, and held to consistent standards over time.  But we’re converting a lot less of it than we used to.  Thus, I conclude that the company’s problem is pipeline conversion, not pipeline coverage.

What possible factors could be driving reduce pipeline conversion rates?  Well, there are a lot of them, so we’ll talk about each.

• Changes in averages (i.e., ceteris non paribus).  Most productivity models assume a constant average sales price (ASP) and average sales cycle length (ASC).  If ASPs go down, you will hit your count-based targets, but miss your dollar-based ones.  If ASCs increase you may preserve your eventual close rates, but stretch them out over time, reducing quarterly conversion rates and plan attainment. 
• ASP decreases.  Typically, due to budgetary pressure and increased price competition, but also can be due to an overreliance on discounting.  Some of this is inevitable in a downturn.  You can mitigate it through pricing and packaging changes (e.g., new add-ons to preserve price and/or offset churn at renewal).
• Slip rate increases.  When ASCs lengthen, more deals slip to the following quarter(s).  Pipeline scrubs can provide early detection and deals reviews can offer re-acceleration strategies.  The biggest risk is that these deals never close at all and simply hit no-decision or derail.
• Win rate decreases.  Win rates usually decrease when a new competitor enters the market or when an existing competitor leapfrogs your product or your market position (e.g., passes you in market share).  Competitive research, sales training, and selling the roadmap are the usual responses.  
• An absence of big deals.  Some CROs run their business as a mix of baseline deals to hit say 60-80% of plan, topped up by big deals that provide the rest.  During a downturn those big deals may evaporate leaving only the run-rate business.  The usual response is a strategic accounts program to focus on generating big deals and a focus on pipeline generation in the run-rate business to cover the gap.
• Pipeline substitution.  This is a subtle problem due to a change in pipeline mix, with low-converting pipeline substituting for high-converting pipeline.  This is dangerous because you “look covered” at the start of the quarter but end up below plan at the end.  Let’s drill in a bit here.

Pipeline Substitution

Not all pipeline is created equal.  Pipeline for certain products often converts at a higher rate than others.  Pipeline conversion rates typically vary by source, e.g., with outbound SDRs typically converting at a low rate and alliances converting at a high rate.  Pipeline conversion might also vary by geography, with established geographies delivering high conversion rates than emerging ones. 

See this chart for an example:

In this example, we start every quarter with $10M in pipeline.  In 1Q23 through 3Q23 we convert 25% of it, but in 4Q23 we convert only 20%.  What happened?  The pipeline mix changed.  Starting in 4Q23, we substituted $2M in high-converting pipeline (from sales/outbound and alliances) with $2M in low-converting pipeline (from SDR/outbound).  Blended pipeline conversion thus dropped from 25% to 20% as a result of this change, effectively substituting nutrient-rich pipeline for nutrient-poor pipeline while keeping the overall amount the same. 

Identifying these problems is a lot of work because you’ll need to segment pipeline by multiple variables — such as pipeline source, product, geography, business segment (e.g., enterprise vs. corporate accounts) – to get historical average conversion rates and percent mix, and then see if changes in pipeline composition are driving reductions in conversion rates.  If so, the usual solution is to re-aim your pipeline generation back to the high-converting segments.

In this post, we have shown how you can troubleshoot go-to-market problems by splitting the funnel in two and focusing on two questions:

• Are we giving sales the change to hit the number each quarter, as measured by pipeline coverage.
• Is sales converting enough of the pipeline to hit the number, as measured by pipeline conversion.

I’ve also provided numerous notes and links that you can use to deepen your knowledge of how to solve these problems.

# # #

Notes

[1] The same analysis approach can easily apply to expansion ARR, which should be analyzed independently via its own funnel because it typically has different conversion rates and shorter sales cycles. 

[2] Deadheads will understand that I had to resist writing, “nothing else shaking, so you might just as well.”

[3] Think:  given that we’re off rails, forget the plan for a minute and let’s analyze what do we need to do to add $4M in newbiz ARR every quarter?  This liberates you from needless, complexifying math that makes it harder to see the answer and is a great way to start in the crawl-walk-run exercise of getting back on track.

[4] More precisely, day-1, week-3, current-quarter pipeline coverage.  Snapshotting Sunday night before the start of week 3 gives you a consistent point to compare across quarters.  Waiting until the start of week 3 gives sales (more than) enough time to clean up the pipeline after the end of the prior quarter but is still early enough to be considered “starting pipeline.”  Note that you may need to apply corrections for any deals that close in the first two weeks of the quarter.  A high-class problem, at least.

[5] Or, in a monthly cadence, day-3 pipeline coverage.  See my post on the mental mapping from quarterly to monthly cadence for more on this concept.

[6] There is a cost to this type of insurance; it’s not great for your CAC ratio if you don’t end up over-performing plan (which ceteris paribus, starting with 110% of your pipeline target, you should).  But it does reduce the risk of missing plan.  To me, the correct sequence is to focus on making plan first, before focusing on efficiency — but you need to have the cash to underwrite that philosophy.

[7] For example, one big deal that masks what’s otherwise a pipeline starvation situation.  If you’re going to set targets on dollars (which typically involves using some placeholder value) then you should create the oppties with a close date far in the future (e.g., one year) that sales can pull forward once they further qualify the account.  The alternative is usually generating lots of fake pipeline that is auto-dumped into next quarter that gets pushed out in the first weeks of the quarter.  Also, see this for more on ensuring pipeline coverage by seller, and not just in aggregate.

[8] You’re not going to hire an extra CMO, an extra PR agency, and an extra product marketer to generate 20 more oppties.  Those costs are effectively fixed.

[9] And putting them under such pressure can run in diametric opposition to pipeline discipline and enforcing pipeline standards by encouraging reps to enter dubious deals as pipeline to get their manager off their backs.

[10] I say “my” oppty management system to remind people that carrying sub-standard oppties has impacts well beyond themselves and that oppty management system is the company’s property, not theirs.  For old movie fans, when speaking of the oppties in “my” oppty management system, I’m always reminded of Cool Hand Luke: “what’s your dirt doing in Boss Keen’s ditch?”

The post Go-To-Market Troubleshooting:  Let’s Take It From The Top appeared first on Kellblog.

Yes, I’m known as something of a brand curmudgeon, but I’m also very much a marketer and I do care a lot about branding — but approach it with pragmatism, caution, and healthy skepticism.

Here are some of the highlights from the episode:

• To me, branding is about trust. My definition is quite meta, but it’s trusing that you will be you. Trusting that you look like you. That you sound like you. That you act like you. That your execution is consistent with your vision. Basically, have you defined a clear character and are you staying within it? You be you.
• It starts with hygiene factors: is your corporate image even professional? Is your copy error-free and mechanically consistent (e.g., via style guide). They’re called hygiene factors because you get punished if you land beneath the bar, but get no extra credit for being above. For example, you might not hire someone who arrives in very dirty clothes, but I doubt you’ll pick the top candidate based because they wore the cleanest. So any money invested in coming in above the bar is effectively wasted. Clear the bar, but by an inch.
• Consistent messaging across deliverables is arguably a hygiene factor. I wouldn’t think your terribly professional if I heard a different story across SDRs, sales, the website, a live webinar, and in reviews from industry analysts. Consistency is key to communications effectiveness, but it’s also a hygiene factor. Some people notice. The smart ones, I think.
• You can stay consistent by making a marketing blueprint deck, a deck that captures the answers basic questions (what is it, what are the benefits of using it, why is it different, etc.) in a master deck that you continually update and reference as you build marketing deliverables.
• Consistency is hard with genius founders who often have too many great ideas. To manage this, you need to get their buy-in about their best delivery and then standardize on it. And then hold accountable for not changing it every day.
• If there’s a technical story behind your product, you should make a seminal white paper that tells it in about 8-12 pages. You’ll be surprised how hard this is — particularly with platform software — and it will help you both tighten your story and tell it consistently. It’s literally the first thing you should at a zero-to-one startup on the marketing front. (Writing it will also help you get started on the style guide.)
• Why do I say “wait until you’re $100M until you focus on brand?” or “if you want to build a brand, go sell some software?” Because capital-B branding — hiring an agency that has no intent on tightening product message or increasing pipeline, but instead helps you determine brand values, brand promise, brand platform, etc. — usually can wait. Some marketers want to do it too early. Heck, it’s fun. But don’t do it too early. Simply put, if you’re $15M and not growing, nobody actually cares what you stand for. So don’t spend $300K trying to figure it out. Get big enough to be relevant, then tighten up who you think you are.
• Most $100M companies still don’t have tight product and corporate messaging. Get your priorities right. Fill the pipeline. Helps sales win deals with product, competitive, and corporate messaging. And when all that is working (and if you’re hearing about troubles due to a lack of branding) then go hire an agency to work on capital-B branding. And I am not universally opposed to this! For example, I recently had a great experience with Twenty-First Century Brand on a branding project. My argument isn’t never. It’s be sure you’re doing it at the right time and place in your evolution.
• Building a strong leadership message will help you win more deals than capital-B branding, so do it first. If you can convince people that you are the technology leader, the market leader, and the vision leader, you are going to win a ton of deals in a growing category. Why wouldn’t you want to buy from someone who you thought was all three?
• I view content marketing and thought leadership as demand generation, not branding. So that may be a source of confusion as well. Kellblog itself is demand generation and awareness generation for my serivces as a advisor or board member. It monetizes indirectly and is more demand generation than demand capture, but it works and I practice what I preach in this department.
• A review of the two archetypal marketing messages: why buy one vs. why buy mine?
• Lot’s wife‘s law (don’t look back) — why you should never communicate externally about smaller competitors.
• Why, in competitive, the rule should be, “if they go low, we go lower” and not, “we go high.” Enterprise software sales is a full-contact sport. You need to train and arm your sellers to go play it. In a perfect world, they’re so well trained that they’re eagerly waiting what used to be the toughest attack points.
• The other rule is, “when they go low, we get on the phone.” Never try to resolve competitor attacks via email. Use the attack as an opportunity to get on the phone and spend more time with the customer.
• Why you should allocate enough budget to measure your external demand funnel: awareness, opinion, consideration, trial, purchase.
• How to combat the age-old, “if we just had more at-bats we’d win more deals” or “nobody’s ever heard of us” claims from sales.

Thanks again to Amrita and Janessa for having me. The episode is here.

The post Who, Me? The Brand Curmudgeon? Appearance on the Standout Startup Brands Podcast appeared first on Kellblog.

“Win rates declining [over the two-year period] from 23% to 19% might not seem all that significant. But in terms of required pipeline, it represents a dramatic shift from 4.3x to 5.3x coverage.”

It’s the kind of sentence that you might read, nod your head in hasty agreement, and then keep going. But you’d be wrong to do that. Quite wrong. And a lot of people make this mistake.

Thus, in this post, I’ll explain why it’s wrong to invert win rate to calculate target pipeline coverage, demonstrate that with a spreadsheet, and then give you a better way to determine target pipeline coverage.

Before diving into the math, let’s take a second to sanity check the conclusion reached above: you’re going to need 5.3x pipeline coverage [1]. Given that the rule of thumb for pipeline coverage is 3.0x, how do we feel about requiring 5.3x? My thoughts [2]:

• I wonder who’s going to generate that? In many companies, it’s primarily marketing. So this potentially passing the buck: “hey marketing, we’re not closing as much as we used to, so we need more coverage.” It’s your problem, now.
• At what cost? Let’s say that it costs $4K to generate a sales-accepted (aka, stage 2) opportunity [3]. If we needed 3x coverage before — e.g., 30 opportunities (“oppties”) to generate 10 deals — now we are going to need 53. That’s 23 more oppties at an incremental cost of $92K. Who’s going to pay for that? What’s that going to do to our CAC ratio and CPP?
• Why do we lose so much? Sales is telling us that they can win only 19% of the oppties that they accept as valid sales oppties? That strikes me as low. If a tougher macro environment means lower quality stage 1 oppties, then why is sales accepting them? Lower quality stage 1 opportunities should show up in a higher stage 2 rejection rate, not a lower win rate [4].

So, if the answer is that we need 5.3x pipeline coverage to make plan, I’m going to have a lot of questions without doing any math at all. But now, let’s cut to the math.

What is Win Rate?

Most people define win rate as follows:

For all oppties that reached a terminal state during the quarter, win rate = wins / (wins + losses). I call this narrow win rate because it excludes no-decisions (also known as derails) where an oppty hits a terminal state without anyone winning it — for example, where the customer decided to stick with the status quo or the whole evaluation gets derailed by a surprise merger [5]. Because derails can happen a lot [6], I define an additional metric, broad win rate = wins / (wins + losses + derails).

Note that both of these win rates exclude slips, when the close date for an opportunity is moved out of the current quarter into a future one. Slips happen a lot. In fact, my basic rule of thumb is you win a third, you lose a third, and you slip a third [7]. Also note that I’m doing this on a count basis, not a dollar basis, which is my default preference [8].

You should already see why inverting win rate is not a great way to determine pipeline coverage requirements:

• It’s ambiguous. Which win rate, narrow or broad?
• Slips are common, but excluded from win rates. (Definitionally, because slipped oppties do not hit a terminal state in the quarter.)
• The timing is wrong. We use pipeline coverage at the start of the quarter to see if we have a chance at hitting the number. But win rates are based on when oppties die, not their start-of-quarter status.

What is Close Rate?

I define close rate as a cohort-based metric that answers the question: given a set of oppties, what percent of them do we close/win [9] in some time period. For example, the six-quarter close rate for the cohort of stage-2 oppties created in 1Q22 = oppties in the cohort closed in the period [1Q22 to 3Q23] / oppties created in 1Q22. Let’s show it with an example:

The first block shows oppty count, the second shows percent. Here, we see a 27% six-quarter close rate. You can also run a cumulative rate along the bottom of the table that would show, for example, that the four-quarter close rate is 23%.

Win rates are period metrics that tell you what happened to the oppties that a hit a terminal state in a given period. Close rates are cohort metrics that tell you, in the fullness of time, the percent of a set of oppties that we win.

• They are different.
• They are both valuable.
• Win rates are great for tracking progress against the enemy.
• Close rates are great for knowing how much value we expect to extract, and when, from a set of oppties.
• Neither is good if you want to invert something to find required pipeline coverage.

Week 3 Pipeline Conversion Rate

Let’s look at a different metric. Instead of starting with the fate of oppties in the pipeline, let’s start with an early-quarter snapshot of the current-quarter pipeline and then compare it to how much we close. Ideally, we’d take the snapshot on day one of the quarter, but that’s not realistic because sales invariably needs some clean-up time after the end of a quarter. Ergo, I typically use week-3 starting pipeline. If you have a monthly cadence, I’d suggest doing this same analysis on a monthly basis and using day-3 starting pipeline [10]. You can then calculate week-3 pipeline conversion rate = new ARR closed / week-3 starting pipeline. See [11] for some notes on this metric.

Because the conversion rates often differ significantly between new and expansion business, most people segment week-3 pipeline conversion rate by new business (newbiz) vs. expansion. In my endless desire to keep things simple, I always start with the total, unsegmented pipeline and break it out later if I need to. The reality is that while the conversion rates are different, if the mix remains roughly constant, it all comes out in the wash.

Here’s a table to show this concept at work:

To get implied target pipeline coverage, I take a trailing nine-quarter average of the week-3 pipeline conversion rate (34%) and then invert it to get 2.86. You could also have fun with the percent-of-plan row, asking questions like: what pipeline coverage do we need to hit plan 90% of the time?

In this post, I’ve hopefully blown a hole in the conventional wisdom that you can invert win rate to get target pipeline coverage. And I’ve provided a far better metric for accomplishing that task: week-3 pipeline conversion rate.

My metrics brother Ray Rike and I recently released an episode of our podcast, SaaS Talk with the Metrics Brothers, on this very topic. The spreadsheet for this post is here.

# # #

Notes

[1] When I used to help my kids with math homework, I’d always include a sanity check review of the answer. If you’re calculating the mean summer temperature in Alaska and the answer is 451 degrees, then go back and check your work.

[2] And I find that rule of thumb high in many situations. At the last company I ran, we could consistently hit plan with 2.5x coverage.

[3] In practice, the average cost of a stage 2 oppty varies considerably. I think a range of $2K to $10K probably covers 90% of cases, with a mean around $4-5K. These are mid-market and enterprise figures. SMB is presumably cheaper. These are sales-accepted so the cost is equivalent to your stage 1 oppty cost dividied by your stage 2 acceptance rate (typically 60-80%).

[4] Yes, I’m aware of the “desperation effect” whereby sellers with weak pipeline accept lower-quality opportunities, but sales management must fight to hold some objective quality bar to preserve pipeline discipline, to ensure resources are only put against quality oppties, and to ensure the validity of pipeline analysis. So yes, the effect is real, but it’s sales management’s job to limit it. (See the “floating bar” problem discussed here.)

[5] Many people code no-decisions as losses and then have a reason code for no-decision. I think this potentially blurs up win/loss analysis because losing to a competitor is different from a no-decision. (Plus, it usually precludes putting no-decision codes on no-decisions which I also want.) The fact is they are two different cases: losing to a competitor vs. an evaluation process ending without selecting a winner.

[6] Particularly in new markets where people are primarily exploring whether they want to buy one at all. In more developed markets — where the customer is more likely thinking, “I’m going to buy one, the question is which” — you should see lower derail rates. And those derails should be more surprise-driven — e.g., we got acquired, the CFO quit, we missed a quarter, we failed an audit, we’re being sued, etc.

[7] Which implies in a 50% narrow win rate, a 33% broad win rate, a 33% slip rate. This is realistic if you are one of two competitors going head-to-head in a market segment. If it’s more of a horse race, I’d expect to see a lower rate. Also, the “a third, a third, a third” rule excludes derails which you can skim off the top. For example, if 20% derail and the balance split by thirds, then you win 27%, lose 27%, and slip 27% of your deals.

[8] I prefer counts to dollars because they’re more visceral and less messed up by big deals. If you are running two sales motions (e.g., corporate and enterprise), I’d first try to stay count-based, but segment the analysis before going to a dollar basis. But there’s a time and a place for both.

[9] Which some might prefer to think of as a “closed/won rate,” but that’s too many syllables for me.

[10] Both generously allows about 10% plus or minus of the period to elapse before snapshotting: 3 days out of 30 (10%) and, depending on how you calculate weeks and what day the quarter starts on, up to 14 days out of 91 (15%).

[11] This assumes (a) sales cycles much longer than the period (e.g., 6-12 months) and (b) no sales are made prior to the snapshot. It ignores (a) deal expansion or shrinkage after week 3, and (b) where closed/won deals came from (e.g., they may be in the week-3 snapshot, created after it, or pulled-forward from a future quarter). This asymmetry bothers some people but it’s really supposed to be a macro measure. The real risk you face using it is when ceteris aren’t paribus.

The post Target Pipeline Coverage is Not the Inverse of Win Rate appeared first on Kellblog.

• That you’re in a new category.
• That it’s a huge greenfield market, ripe for the taking.
• That once you took it, switching costs were high so you could keep it.
• That if you didn’t take it, somebody else would.

Be the gorilla of the space.  Just ship in the tornado.  Design the category to become king.  Sound familiar?

Those assumptions were largely valid in the world in which I grew up:  the dawn of relational databases, ERP, SFA, CRM, data warehousing, and business intelligence. 

A subtle part of what drove those assumptions were some underlying facts about exits:

• If you finished win, place or show in the category, you could go public.  You’d get a very different valuation multiple as a function of position, but you could make it out.
• If you finished in fourth or beyond, you’d end up a zombie.  You couldn’t go public.  But there were no PE firms to buy you (either standalone or as part of a roll-up).  So, you’d limp along half alive, half dead with no real future.
• A zombie’s only hope was to be acquired by one of the few zombie-eaters like Computer Associates who’d put you out of your corporate misery — firing your exec team, your R&D team, several other teams, and then jacking up your annual maintenance fees faster than your captive customer base could flee.

That was it.  So, you needed to play to win.  The markets were new, huge, and greenfield — and there was nothing else to play for.  Fight to win, hope that you do, and worst case end up in the top three.  Otherwise, may God have mercy on your corporate soul.

But things have changed. 

• Many software markets aren’t new or greenfield, but replacement.
• Markets aren’t always huge, but we have collectively realized you can build a nice business in what was formerly seen as a niche (e.g., account reconciliation).
• Switching costs aren’t that high.  At least compared to their on-premises predecessors.  Customers can change systems.
• There are more exit options available for both venture- and PE-backed, as well as bootstrapped, startups.

While there certainly remains a large winner-take-all world (e.g., AI) that dominates most Silicon Valley thinking, there is now also a parallel, more mundane world.  The danger is when strategic thinking designed for one gets applied to the other.

That’s the distinction between “playing to win” and “playing to make plan.” 

Because I grew up in the playing-to-win world, my inclination was to call the latter “playing to play,” but that’s too pejorative.  It’s not playing for playing’s sake.  But nor is it playing to win.  It really is playing to make plan.

While I’m going to end up using the word “win” in two different contexts here, one of my favorite strategy authors defines strategy as the plan to win.  That definition quickly begs the question:  for us, in our situation, what is winning?

• For some, it’s changing the world by creating a new market and dominating it, delivering a 100x or more return to early investors.  Here, winning means winning in the market. 
• For others, it’s selling the company in 4-6 years for 3 times what the investors paid for it.  Here, winning means making plan, because – trust me — you can be sure that any board-approved plan will put or keep you on track to deliver that 3x.

These are two different worlds populated with different companies, backed by different investors, and usually inhabited by different employees.  Let’s contrast them by comparing the advice I give to CEOs in each.

The World of Playing to Win

When I meet a CEO in the playing-to-win world, I say things like:

• If you don’t win this market one of your competitors will.  A first-place valuation can be 10x second which in turn can be 10x third.  You must win.  Second prize really is a set of steak knives.
• For the next several years, you need to get in touch with your inner barbarian.  When the smoke clears at the conclusion of the hypergrowth phase, you must emerge the winner.  After you’ve won, you can do TechCrunch interviews about the difficulty of maintaining your values during hypergrowth.  But win first.
• Hire a VP of Sales who makes you uncomfortable.  They should be so aggressive you get worried.  You don’t want shipping bricks or channel-stuffing aggressive, but you do want a strong dose of, “you want me on that wall, you need me on that wall.”  They should be respected more than liked, feared more than loved. 
• You must build a hyper-competitive sales force who will win deals at nearly any cost.  Turnover will be high.  Consultative selling will be low.  You’ll hear negative things about how aggressive your people are from time to time.  That’s fine.  You’re trying to win market share, not the vendor congeniality award. 
• Even though you’re larger and growing faster than your competitors you need to plan to keep growing at extraordinary rates.  You can’t let up and risk someone catching you.  One day this will end in a phenomenal plan miss with internal chaos, but you won’t care.  You’ll have won the market.  Then you can clean up the mess.
• Make a loose discounting policy that provides sales with lots of discretion.  Then throw that policy out the window when a deal heats up.  You must win.  You’ll be accused of slashing prices to sell a deficient product, but you don’t care. 
• Market your leadership.  Your top marketing message needs to be, “we’re #1” in any of its various forms.  #1 is safe.  #1 is the default choice.  #1 has the biggest ecosystem with the most partners.  Make competitors explain why a prospect should pick them despite the fact that the market has chosen otherwise.  Crush any misguided claims that the company should be more humble or that some customers prefer to buy from underdogs.
• Track relative market share.  At every QBR, put up charts that show relative ARR (triangulated in all likelihood), burn rate (ditto), and headcount so you can get a clear sense of who you’re gaining on and who is gaining on you.  Yes, this is paranoid but you have to do it – to build the leader you must be realistic about where you stand, and to stay the leader you must detect and eliminate threats early.
• Blunt all competitive threats.  You never want to let a smaller competitor – particularly a high-profile one — get a toehold in the market.  So, when someone announces a new feature, you announce it too, ship an anemic version at once, and then round it out over time.  Deny differentiation. “Grey” is your watchword.  Any time a competitor tries to establish a black-and-white differentiator, you immediately grey it out.  Eventually, they’ll stop trying to differentiate or their differentiation will become so esoteric as to be irrelevant.

The biggest risk here is losing, having missed the usually once-in-a-lifetime opportunity to define and dominate a category.  For more on what to do in this situation – and how to blunt common tactics if you’re on receiving end — see my post entitled The Market Leader Play.

The World of Playing to Make Plan

When I meet a CEO in the playing-to-make-plan world, I say things like:

• Is the total available market (TAM) big enough to support your 20-25% growth targets over the next few years?
• If not, how can you expand into adjacent markets and/or offer new products that will help ensure you can grow?
• How can you get to a point of indifference between a 5x ARR and a 15x EBITDA valuation?  (Hint:  the answer is 33% EBITDA margins.)
• What is your plan to get EBITDA margins to 20% and then closer to 30%+ over the next few years?
• Is the operating plan achievable?  Have you inspected all the key levers to ensure that we have a high chance of achieving it?  To the extent we’re assuming changes to productivity metrics, what plans have you put in place to achieve them?  Or are they simply wishes?
• Are you identifying and doubling down on success sufficiently in your go-to-market plans to increase GTM efficiency?
• Against whom are you competing, and do you have a playbook such that you know you can win your fair share of deals?
• Remember my definition of solution selling, that we can win the deal if we convince the customer of three things: (1) they understand my problem, (2) they can solve my problem, and (3) I want to work with them.
• Are you exit-ready?  Have you done due diligence on yourself — and put plans in place to correct problems — so that you can be reasonably sure there will be no problems when one day an acquirer’s due diligence process occurs?

The biggest risk here is that your make-plan focus inadvertently becomes blinders to external changes in the market (e.g., a new, disruptive entrant) that can ultimately inhibit your ability to make plan going forward. Think: you know you need to grow at 20% and crank up EBITDA to 30%. But you forget to answer the question: why would anyone want to buy from us again?

The other risk is execution, which is why PE investors prefer working with proven leaders who they know and trust to take on these assignments. When you’re only shooting for singles and doubles you can’t afford many strike-outs.

When The Whole Category Just Wants to Make Plan

One interesting case is when an entire market goes into “make plan” mode.  I’d argue that my old category, enterprise performance management (EPM), is largely there.  All the major independent players are owned by PE firms and presumably more focused on making plan than on winning in the market.  Thus, innovation has slowed.  The level of competitiveness has diminished (also helped greatly by Workday’s acquisition of Adaptive Insights, a well-funded, aggressive, price-slashing competitor back in the day).  It strikes me today as a sleepy category with a bunch of PE-owned firms all grinding out their “make plan” goals, hoping to get sold for 3x+ their invested capital in the coming years. 

What happens then? Alas capitalism works.  There is now a crop of VC-backed startups like Cube trying to fill the Adaptive void or Mosaic in financial analytics.  France’s Pigment is the most aggressive grower and capital raiser in the space, but more focused on mid-market and the Anaplan void in enterprise.   But there is a short answer to the question, what happens when the whole category ends up PE-owned and focused largely on “making plan?”  A crop of new startups enter to seize on that opportunity. (And see my disclaimers as I’m working with several of them in different ways.)

Conclusions

Let’s wrap up this post with a summary:

• Back in the day, the only real play in enterprise software was playing to win.
• Much of enterprise software strategy was thus defined in books primarily focused on the problem of winning in large, greenfield markets.
• Over time, as new exit options emerged, companies could either plan to win or play to make plan.  Both were good ways to make money.
• But those are two different worlds that require two different strategies and often attract two different types of employees.
• Your company will work best when you identify which mode you are in and then define strategy, hire employees, and run the company accordingly.
• Beware the big risk in each world:  losing in the playing-to-win world, and blinders in the playing-to-make-plan world.

The post Playing to Win vs. Playing to Make Plan: The Two Very Different Worlds of Silicon Valley appeared first on Kellblog.

For no apparent underlying reason, a lot of the companies I have worked at over the years have been quite successful in Japan.  Thus, while I don’t write about it much, I have a fair bit of experience working with Japanese subsidiaries and/or distributors in the enterprise software market. 

On a recent trip to Tokyo, I met with an old friend who’s lived in Japan for well over a decade and who is now the GM of Japan at a large enterprise software company.  Over a beer, she validated two of my basic beliefs about how enterprise software companies should approach business in Japan and the purpose of this post is to share them.  To be clear, these are my beliefs — not hers — but they at least passed muster during a nice chat with an expert.

Let’s start at the top.  Much as I believe, at the first order, that there is no Europe (only France, England, and so on), I believe that there is no Asia-Pacific:  only Japan, Australia, and so on.  While in the organizational hierarchy, East, West, Central, and Japan might all sit at the same level (sub-regional), there is a world of difference between a US sub-region and a country.  And that world of difference is perhaps no larger than when we’re discussing Japan. 

The odds that an Australian expat running Asia-Pacific out of Singapore can effectively conduct business in Japan are low.  Sure, if they’re an experienced Asia-Pacific leader they’ll have previous exposure to Japan, but that usually means they know much more about how to manage a Japanese operation than they do about how that Japanese operation works on the inside.  Contrast that to how your head of Americas works with the RVP in Chicago – the central region is not a black box at all, and the Americas head absolutely knows how the operation works and how to conduct business within it.  Hierarchically, it’s the same level in the org chart, but there’s a world of difference in the interface.

With that warm-up, let’s cut to the two big choices you have when working in Japan:

• Direct or indirect?
• Insider or outsider as general manager (GM)?

The First Choice:  Direct or Indirect?

The first decision you make in Japan is whether to go direct (i.e., build a subsidiary that markets and sells to customers directly) or to go indirect (i.e., enter the market through signing one or more partners to market and sell your software). 

The default play is to start indirect, but you’ll quickly realize that the Japanese partners are much more demanding than US partners and ergo require much more support.  You can try to support them remotely, but that doesn’t usually work.  Hence, the smart play is to do both – sign a small number of partners to represent you and open a direct office in Japan to support those partners.  The “small number” part is important (and may reduce all the way down to one) because the more partners you work with the less incentive any single partner has to develop the market.  I’d call this model indirect distribution with a high level of local support.  The subtlety is that many people equate “indirect” with “no local presence” and that does not have to be the case.  In fact, the smart play in Japan is to do both.

I’ll make an odd analogy here that should hopefully resonate:  Japan is a lot like Federal.  Wut?  How? 

Well, in Federal:

• You usually distribute indirectly through partners.
• Those partners are often large, somewhat ungainly, organizations.
• Those partners require a lot of support.
• You create a local organization whose primary purpose is to support those partners.
• They speak kind of a different language.
• They generate their own idiosyncratic product requirements.
• The deals can be large, complex, and require a lot of system integration.
• If you half-ass the investment, you’ll probably fail.

I’m always amazed at the similarities between these rather odd bedfellows, but there are indeed many.

Now let’s move to the second big choice.

The Second Choice:  Insider or Outsider GM?

Gaijin means “alien” or “outsider” in Japanese.  You can read the Wikipedia entry for more, but I don’t take it as having a negative connotation.  I believe it’s simply matter of fact:  you’re not from here, you’re not one of us.  Without diving into topics I know little about, let’s just accept that it’s a common word and one used to make an important distinction:  insider or outsider.

And the question for you is:  who do you want running your Japanese operation?

The easiest way to answer this question is to transform it into (what I believe) is a semantic equivalent:  do you want to know anything about what’s going on within your Japanese operation? 

• If yes, hire an outsider.
• If no, hire an insider.

And by outsider, I don’t just mean any outsider.  That “outsider” may speak reasonable Japanese, be married to a Japanese national, and have lived there for 20 years.  But they’re still an outsider.

Put differently again:  do you want your Japanese operation to be a black box?

• If yes, hire an insider.  They might well do a wonderful job building the business.  You just probably won’t understand much about what happens within it.
• If no, hire an outsider.  They will serve as the interface between the Japanese world in which they live and work and the larger world of the company. 

Some would dispute the semantic equivalence of these three questions.  And I’m sure there are exceptions such as insiders who can build the bridge quite well.  I’m just saying I’ve never met one of them, and in my experience, if you can hire the right outsider, you can have your cake and eat it, too – you can build a successful business in Japan without losing visibility into it.

Personally, because I have a strong preference for understanding what’s going on, I’d recommend the outsider GM in most cases.  But decide for yourself.  Just be aware of what you might be deciding, because you’re not just deciding who runs the organization, you’re quite probably deciding your relationship with it.

The post The Two Big Choices Enterprise Software Companies Make in Opening Japan appeared first on Kellblog.

Despite numerous warnings, I continue to see many European founders fall into the same trap when it comes to hiring in the first years of their US expansion.  Sadly, it feels like one of those lessons you can only learn from experience.  That’s painful because failing on your first US hires comes at a high cost in both dollars and, more importantly, time. 

So, I thought I’d take one more run at solving this problem, based on a chat I had the other day with a European founder.

What causes the “these people are gods” problem? We Americans are:

• Natural storytellers who build narratives that can potentially mislead.
• Take credit much more freely than Europeans.
• Embellish our resumes and profiles, often turning the hype up to 11.

That’s why behavioral interviewing — drilling down into claims, asking “tell me about a time” questions that describe specific moments — is so important in the US. Here’s an example question sequence:

“So, you ran the budget process in 2023 as the head of finance?”

“Tell me how you laid out the process, the milestones, and timing?”

“Did anyone have problems with it?  Were there any disagreements?”

“When you ran the first budget meeting, what major challenge did you encounter and how did you manage it?” 

This approach eliminates generic answers discussing budgeting philosophy, telling you instead how they manage a budget process and, most importantly, whether they actually did.  Because few embellishers can survive multi-layer, drill-down questioning, you might eventually get a response like, “well, the CFO actually laid out the process that year and ran the first meeting.”

In the US we’re used to discounting career claims.  We drill down.  We ask, “what precise role did you play?” We use behavioral questions.  We check references, both those provided and backdoor.  It’s all a normal part of the process.  We do it without thinking.

But European founders are not used to all this.  They come from an understated culture where people tend to discount their accomplishments. To understand a European resume, an American might need to amplify it.  Think:  “yes, we grew the company from $20M to $200M but I was only part of the team that did that,” when they were actually its leader who built it from nothing.

What happens when a culture of understated accomplishment meets a culture of overstated achievement?   

“These people are gods.”  That’s what happens. 

Worse yet, remember this happens in the context of hiring your first US employees, who are typically salespeople and ergo top decile in storytelling and embellishment.

It’s a wonder that anyone successfully expands at all. 

Since this appears to be a lesson best learned by experience, I discovered a great trick the other day chatting with a European founder.  Provided you’ve already begun your US expansion:

Go look at the LinkedIn profiles of the first people you hired and then subsequently fired and see what they say today about their experience at your company.  You might be quite surprised at what you find.

Or, if you’re not that far along, go look at the LinkedIn profiles of the people you’ve hired and who are still on board.  Now that you know them, what do you think of how they described their past experience? How are they describing their current experience?

In both cases, you’ll get real, first-hand experience with American resume and profile inflation that should help you not just intellectually — but viscerally — understand the problem.  And it appears that without this visceral understanding, in many cases you won’t be able to fix it.

The alternative is literally to lose 12-24 months building, firing, and rebuilding your entire US team.  And I’m desperately hoping to help you avoid doing that.

The post These People Aren’t Gods: How European Founders Can Stop Making The #1 Mistake in US Recruiting appeared first on Kellblog.

I love Rumelt’s work for two reasons:

• He takes a wrecking ball to the garbage that is often passed off as strategy. Aspirations are not strategy. Goals and OKRs are not strategy. Financial projections and forecasts are not strategy. SWOT analyses and five forces analyses are not strategy. Driving results is not strategy. Deciding to be a butcher, baker, or candlestick maker is not strategy. You may, like me, find reading these takedowns not only educational, but therapeutic.
• He whittles strategy down to the head of a pin. First, by defining strategy as identifying and planning to overcome a company’s most important challenge (aka, challenge-driven strategy). Then, by capturing what he calls the kernel of strategy: a diagnosis, a guiding policy, and a set of coherent actions.

Much as I love the kernel idea, in one assignment a few years back we tried to apply this framework and stumbled into a problem. We arrived at a diagnosis fairly easily, but got stuck trying to create a guiding policy. We found that the diagnosis alone wasn’t enough to arrive at a guiding policy. We kept needing to insert a few assumptions (or beliefs) about the future before we could agree on a guiding policy. We drifted to a modified framework that looked like this:

• Given diagnosis X,
• And beliefs Y,
• We choose guiding policy Z,
• And coherent actions 1-5 to implement it.

I was so excited with this discovery that I emailed Rumelt. While he kindly did reply, I don’t think my point landed. He directed me to his then-upcoming book and suggested it would be addressed there. The Crux was subsequently published and I don’t think it was. Never meet your heroes, as Flaubert wrote, a little gold always rubs off when you do.

Undeterred, I continued to use Rumelt’s framework, but added beliefs as an explicit part. I’ve always felt that diagnosis was by far the hardest part of strategy, as I believe does Rumelt, given this excerpt from his first book:

“After my colleague John Mamer stepped down as dean of the UCLA Anderson School of Management, he wanted to take a stab at teaching strategy. To acquaint himself with the subject, he sat in on ten of my class sessions. Somewhere around class number seven we were chatting about pedagogy and I noted that many of the lessons learned in a strategy course come in the form of the questions asked as study assignments and asked in class. These questions distill decades of experience about useful things to think about in exploring complex situations. John gave me a sidelong look and said, “It looks to me as if there is really only one question you are asking in each case. That question is ‘What’s going on here?’ ” John’s comment was something I had never heard said explicitly, but it was instantly and obviously correct. A great deal of strategy work is trying to figure out what is going on. Not just deciding what to do, but the more fundamental problem of comprehending the situation.”

I believe Rumelt would say that what I call beliefs are simply part of the diagnosis. For example, he said, “Netflix’s overall challenge (in 2018) was that it could no longer count on contracting for existing good TV and studio films at reasonable prices.” I’d argue that Rumelt’s Netflix diagnosis is actually two statements in one. Writing from the viewpoint of Netflix:

• That today we find ourselves increasingly hit with large price increases and/or a non-desire to renew distribution agreements for content.
• We believe the vast majority of the content producers will enter the content distribution business via streaming services in the next few years and ergo will not want or need to work with us.

First, that’s one hell of a “gnarly challenge” as Rumelt likes to call the crux issue. Second, I like splitting it because, particularly when working with a good-sized group to build strategy, it helps to distinguish between what we are seeing right now versus what we anticipate in the future. The former are facts, the latter are beliefs — and most of the interesting debate is not about the facts, but the beliefs.

I was happy with this modified framework until a strange thing happened the other day. I was talking with a founder and — lightbulb moment — I realized I could further distill strategy simply by looking only at beliefs. Not a laundry list of them (which can easily get generated in such a process), but what I call the primary belief, the big one, the one that resolves the crux issue and drives all the rest.

I immediately tried to apply this idea to my experience at Business Objects where, for nearly a decade, I worked as one of the top executives as we grew the company from $30M to $1B. I found it was pretty easy to divide 16 years of history into four eras categorized by primary belief:

• Era 1 (5 years). We believe customers will pay 5x the price of commodity query and reporting (Q&R) tools for an enterprise solution.
• Era 2 (4 years). We believe that Q&R and online analytical processing (OLAP) tools should be integrated in one product.
• Era 3 (4 years). We believe the Internet will require a wholesale rewrite of business intelligence (BI) and enable both existing internal and new external use-cases.
• Era 4 (3 years). We believe that customers will increasingly want to buy an integrated suite of BI tools, including Q&R, OLAP, and enterprise reporting.

These beliefs were largely heretical at the time. $500/seat for a Q&R tool? Insane. Integrating Q&R and OLAP? Can’t be done (and “they” were nearly right). Extranet BI? Never, corporate data is highly proprietary. BI suites? No, customers still want best of breed!

But those four beliefs took us from $0 to $1B in revenues. The beliefs alone are not enough, of course. You need to build strategies (e.g., product, go-to-market) and execute against them. In era 1, we needed a highly targeted strategy to break into the market with this radical idea. In era 2, we needed to build and market the integrated product. In era 3, we needed to devise the right web product strategy, a task that befuddled several of our competitors.

But I can and will argue that it all flowed from the underlying primary belief.

I worked with Alation in various capacities for many years, so I feel I know their evolution pretty well. Let me try the same exercise, as an outsider looking in, separating Alation’s history into three eras and assign a name to each:

• Era 1 (search and discovery). We believe that companies will need a centralized data catalog to help people find the data they need, and that machine-learning can help with that finding.
• Era 2 (data governance). We believe that data catalogs (almost surprisingly) turn out to be an ideal tool for data governance, particularly the non-invasive variety.
• Era 3 (data intelligence platform). We believe that customers will increasingly want to buy a data intelligence platform that includes data search & discovery, governance, and lineage.

I’m probably missing the company’s strong commitment to cloud platforms as part of era 3 and there may be a new era 4, but you get the idea. Again these beliefs were often heretical at the time. A lot of people didn’t believe data catalogs were even needed. Most people believed data governance was a distinct category and that the “prevent access” ethos of data governance ran strongly counter to the “enable access” ethos of data catalogs. Until recently, many people didn’t believe in data intelligence platforms (but with help from IDC and Databricks that debate has been put to bed). Again, beliefs alone are not enough. There are numerous also-ran data catalog companies who presumably shared some of these beliefs, but built the wrong strategies in response or lacked Alation’s relentless drive in execution.

I often say that strategy is best analyzed in reflection. Meaning that somehow everything is clearer and simpler when you look back 10 or 20 years to reflect upon what happened. In fact, I often encourage people to do a future look-back when formulating strategy: “imagine it’s ten years from now and your company won in the market — now tell me why.”

My conclusions from all this are:

• Read Rumelt. Both Good Strategy, Bad Strategy and The Crux.
• Add beliefs to the framework. More precisely, separate the diagnosis into present truths and future beliefs.
• Work to find the one primary belief for your current situation. If you’re a new startup, that belief is probably embedded in the answer to, “why did you found the company?” If you’ve been around for a while, start by analyzing your history and trying to break it into belief-driven eras.
• Once you’ve found a potentially era-defining primary belief, resume the Rumelt exercise: define guiding policy and coherent actions around it.

The post Strategy as a Series of Beliefs appeared first on Kellblog.

For example, the term “exit” is a misnomer when seen from the employee point of view. An IPO is usually anything but an exit for executives who first will be subject to a 6-month lock-up period and restricted to certain trading windows thereafter. A PE acquistion is usually not an exit for the executive team who may be expected to stick around, sign employement letters as a condition of the sale, and rollover a certain percentage of their proceeds into the deal. These transactions are, strictly speaking, best described as an “exit” only for the investors.

So it was with great one surprise one day, a decade or so ago, that I learned I was not a CEO, but an “operator,” when seen from the viewpoint of a VC. Frankly, I found it odd to be referred to as an operator, but I hoped that at least I was a smooth one.

From the VC point of view, there are four types of people:

• Founders. The entrepreneur(s) who found a company.
• Operators. The executives who build and run companies
• Investors. The venture capitalists who invest in startups.
• Employees. The 95%+ of the staff not on the executive leadership team (ELT) but who, paraphrasing George Bailey, do most of the working and paying and living and dying in the company.

So, what might you do with this morcel of knowlege? While I believe that the mere act of trying to see things from others’ viewpoint is always developmental, there are some practical takeaways here.

Let’s assume you’re an operator. What should you know and do?

• You are not a founder. Many operators, particularly ones who join early, start to think of themselves as one of the founders. While the founders may even treat you as such, from the VC/board point of view you are not. The founder invincability cloak does not apply to you, no matter how much you’ve contributed nor how much you are valued. You are an operator. You may be beloved, but your replacement is always one phone call to 1-800-DAVERSA or 1-800-HEIDRICK away. Don’t overplay your hand. Some do.
• You are valued for different things than a founder. Boards value operators for their experience, common sense, reliability, and professionalism. If given only one adjective, I’d say board members want “brilliant” founders, but “solid” executives. Operators are not creating the vision; they’re executing it. I don’t need an artiste to replace my fence, just an experienced fence builder. Be solid and low drama. This doesn’t diminish your value in any way: such operators are often quite hard to find.
• You are positioned by expertise and sub-expertise. You are a velocity sales leader. Or a big-deal CRO. Or a demandgen marketer. Or a CRM product leader. You become what you repeatedly do. Actively manage your work so that you position yourself where you want to be. If you want to be positioned as an ABM marketer or a post-agile engineering leader, then go lead an ABM marketing or post-agile engineering team.
• Fighting positioning is pointless. Positioning works because the human mind likes to simplify. If you think you can position yourself as the SMB and enterprise, transactional and artisanal, process-driven and relationship-driven sales leader, then you are mistaken. If you fail to position yourself, don’t worry, the market will do it for you — you just may not like the result. Actively position yourself and don’t fight the tide in so doing.
• You are positioned by size-range. Think: we need a $10-30M CRO. Or a $30 to $150M CFO. Or a $100M to $300M CEO. The size ranges aren’t fixed, but be aware of what size range you’re positioned in. Avoid the number one mistake I see: leaving a high-growth company too soon because you’re tired of something, thus leaving yourself positioned as a $10-30M operator when — if you’d held on for a bit longer — you might have been a $10-100M one.
• Expect to get hired below the top of your size range. No patient wants to hear, “this is the first time I’ve tried a brain surgery this complex, but I’m really confident I can do it.” Expect VCs and boards to think similarly. If you’re positioned as a $10-30M CPO, don’t expect to get hired by a $30M company growing to $100M. Expect to get hired by a $10M company growing to $30M. Per the prior point, ride existing success to increase the top of your range. And beware anyone willing to hire you above it.

I’ll use myself as an example of some of these principles:

• Despite having been CEO of two startups from $8-50M and $0-80M for over a decade, I am still widely positioned as a marketing guy. I embrace that both because there’s no point in fighting the tide and I think it’s true. I am a marketing guy. It’s my DNA. I ooze marketing. But I try to remind people of my overall experience with my 10/10/10 message: 10+ years a CMO, 10+ years a CEO, and 10+ boards.
• Despite having been CMO of a $1B company and SVP/GM of a $500M business at a $3B company, I get positioned as an early-stage guy, and get size-bucketed in the $0-100M range. While I am certainly strong in that range, I have both signficant experience and clients well beyond $100M. While I can find work beyond that range and enjoy doing it, as a marketer, I must accept that I can’t position at both ends of the scale. Positioning is all about choice and, as the saying goes, “if you try to be everything to everybody, you end up nothing to nobody.”

Or, as I like to say to marketers in career planning: marketer, position thyself!

The post You’re an Operator and Maybe Don’t Even Know It appeared first on Kellblog.

Many marketers are in such a hurry to talk about topical issues that they forget the duck test: if it walks like a duck, swims like a duck, and quacks like a duck, then most people will conclude it’s a duck. Philosophers teach that such abductive (or should we say, abducktive) reasoning can lead to incorrect conclusions — and it can.

But here in marketing, we draw a different conclusion from the duck test. It’s how most peoples’ minds work so we shouldn’t fight against it. There are two common ways that marketers fail the duck test and we’ll cover both of them — and what to do instead — in this post.

Deny Thy Father and Refuse Thy Name
Many marketers are eager to pretend that their product is the latest in-vogue thing (e.g., AI), and can get so busy dressing it up in the latest tech fashion, that they generate more confusion than sales opportunities.

It’s like a replay of the clichéd movie scene:
Man: Who are you?
Woman: Who do you want me to be, baby?

When someone asks your company the equivalent of “who are you?” [1], you need to answer the question and that answer needs to be clear.

Remember, the enemy for most startups isn’t the competition. It’s confusion. The easiest thing for a prospect to do is nothing. If we talk and I leave confused, then I’ll just write off the wasted half-hour and go on with my day.

Consider an answer like this [2] [3], to the question “what is MarkLogic?”

I mean great question. We ask ourselves that all the time. It’s actually hard to answer because there’s nothing else like it. Answering that is like trying to explain the difference between a Cessna and a 747 to someone who’s never seen an airplane. Our marketing people call it an XML Server, but that’s not a great description.

What is it really? Literally, it’s what you get when you lock two search engine PhDs in a garage for two years and tell them to build a database. You know, it looks like a database from the outside, but when you pop open the hood — surprise — you find that it’s built from search engine parts. Search engine style indexing. And it’s schema-free like a search engine so it can handle unstructured, semi-structured, and, of course, structured data as well. Let’s get into those exciting distinctions in a minute.

This thing — whatever you want to call it — it’s the Vegomatic of a data: it slices and dices and chops in every conceivable way. In the end, I think what makes it hard to understand is that it’s basically a hybrid: half search engine, half content application platform, and all database.

Is that clear?

As mud. What’s wrong with that answer?

• It’s confusing
• It’s long
• It’s navel-gazing (let’s talk about me)
• It’s bleeding on the customer (sharing internal troubles)

It’s a horrible, horrible answer.

Now before you stop reading, perhaps thinking that this is one specific, dated case study, let me say that I could easily write such a parody for about a quarter of the few score of startups I work with today. This is not some ancient example from another world. This is a current problem for many startups, but I’m not going to parody any of them here [4]. Might you suffer from this problem? Go listen to some Gong or Chorus recordings, particularly high funnel (e.g., SDR) and/or discovery calls, and see if anything resonates.

Now, let’s contrast the previous answer with this one:

It’s an XML database system, meaning it’s a database that uses XML documents as its native data modeling element. Now, what did you want to do with it again?

What’s nice about this answer?

• It’s short
• It’s clear
• It’s correct
• It leaves an opportunity for follow-up questions [5]

But the really nice part of this answer is that it puts focus back on the customer. The direct cost of all the previous blather is confusion. The opportunity cost of all that blather is you waste precious time you could have spent listening to the customer, learning more about their problem, and trying to decide if you can solve it.

So why didn’t some of our sellers want to give the second answer? They didn’t want to say the X word. XML was cool for a while, but that quickly passed and XML databases were always distinctly uncool. So, some sellers would rather spend five minutes tap dancing around the question rather than directly answering it.

What followed was almost always a difficult conversation [6]. But the flaw in tap-dancing was simple: the customer is going to figure it out anyway [7]. Customers are smart. If it:

• Stores data like a database
• Builds indexes like a database
• And has a query language like a database

Then — quack, quack — it’s a database.

That’s the first way marketers fail the duck test. They’re afraid to say what the product is for fear of scaring people off. But there’s another way to fail the duck test.

Confusing Products and Solutions
The second way to fail the duck test is to rotate so hard to solutions that you basically refuse to say what the product is. You end up dodging the question entirely.

Customer: So, what is it?
Vendor: You can use it to build things, like a deck.
Customer: That’s great, but what is it?
Vendor: You can use it to assemble things, too, like a bed.
Customer: Sure, but what is it?
Vendor: And you can use it for disassembling things too.
Customer: Wait, it’s a drill isn’t it?

Here we have the prospect playing twenty questions to figure out what the product is. Yes, we all know that customers buy solutions to problems [8] and Theodore Levitt’s classic example of customers buying 1/4″ holes, not 1/4″ bits.

But don’t take that in a fundamentalist way. If the customer asks, “what is it?” the answer is not, “a thing that makes holes” but, “a power drill with a 1/4-inch bit.” If they ask why ours is better, we say that our bits are titanium and don’t break. “Feature” need not be a four-letter word to remember that the purpose of the drill is to make a hole and, transitively, that the purpose of the hole is to build a new deck with the ultimate benefit of quality family time.

The point is: knowing what solutions (or use-cases) we want to target does not eliminate the requirement to have strong product messaging. Particularly in unexciting categories, we will need to lead with use-cases, not product superiority, category formation, or market leadership. But, inevitably, even when you lead with use-cases, you will get the question: what is it?

And a short, clear answer – as we discussed above – not only gets the customer what they want, but it lets us have more time for listening and discovery. I see many companies where they rotate so hard to use-case marketing that their product messaging is so weak it actually interferes with discussions of the use-case.

For example, say the product is a data streaming platform (DSP) and the use-case is industrial monitoring for manufacturing facilities. Let’s assume that data streaming platforms are not a hot category, so there aren’t a lot of people out shopping for them. That means we’re not going to target DSP shoppers with a product-oriented superiority message, instead, we are going to target people who have a problem with industrial monitoring.

But when one of those people asks what it is, we’re not going to say, “a thingy that helps you do industrial monitoring.” Instead, we’re going to say, “it’s a data streaming platform, many of our customers use it for industrial monitoring, and here’s why it’s such a great fit for that use-case.”

That is, we map to the use-case. We don’t redefine the product around the use-case. We don’t try to use the use-case to avoid talking about the product. Doing so only confuses people because eventually they figure out it’s not an industrial monitoring application, but a data streaming platform that can be used for industrial monitoring. Unless we are clear that it’s a platform being used for a use-case, then we fail the duck test.

In the end, you will get the right answer if you always remember three things:

• Customers are smart
• Time spent in hazy product explanations confuses customers and robs time from discovery
• If it walks like a duck, swims like a duck, and quacks like a duck — then, for marketing purposes at least — it’s a duck.

# # #

Notes

[1] That is, “what is it?”

[2] I swear this is only partially dramatized, and only because I’ve assembled all the fragments into one single response.

[3] This is circa 2008. Presumably much has changed in the intervening 15 years.

[4] I obviously don’t use more recent examples as a matter of both confidentiality and discretion.

[5] An obvious one might be, “so if it’s a database, does it speak SQL?” (To which the answer was “no, it speaks XQuery,” which could lead to another loop of hopefully tight question/answer follow-ups.)

[6] Because, simply put, nobody wanted to buy an XML database. Gartner had declared the category stillborn around 2002 with a note entitled XML DBMS, The Market That Never Was. The way we sold nearly $200M worth of them (cumulatively) during my tenure was not to sell the product (that nobody wanted) but to sell the problems it could solve.

[7] And when they do, they’re not going to be happy that you seemingly tried to deceive them.

[8] Or hire them to do jobs for them, if you prefer the Jobs To Be Done framework.

The post Does Your Marketing Pass the Duck Test? appeared first on Kellblog.

• My aborted attempt to write an AI GTM guide, after I realized just how huge the space was and how fast it was changing. I quickly understood it’d take too long to write and it would be out of date the second it was published. But the exercise nevertheless got me started researching AI and GTM.
• The following slide from Battery Ventures that I discussed in my 2024 predictions post. This slide argues that, thanks to AI-driven productivity improvement, you should be able to drive the same quota with a 75-person organization that previously required a 110-person organization. This got me thinking: boards are going to start asking about that 30% productivity improvement in 2H24 and what are we going to say?

When the market is in a state of confusion and things are moving fast, it’s better to have a conversation than to write a guide. So I found two of the smartest people I know and asked them to join me on a panel:

• Alice de Courcy, CMO of Cognism, an amazing company that’s doing some of the best solutions-oriented and thought-leadership (aka “demand generation“) marketing in Europe. Alice is also the author of Diary of First-Time CMO.
• Firaas Rasheed, founder and CEO of Hook, a company that’s re-inventing customer success software. Firaas argues that CS software lost the plot and ended up more focused on process (e.g., QBRs and NPS surveys) than on results (e.g., churn prediction and prevention). The company’s origin story is quite compelling and told here.

After a I did brief introduction to set the stage, we focused on four high-level questions that GTM leaders are pondering:

• What should I make of all the AI tools flooding the market?
• What should my strategy be?
• What are my higher-ups expecting?
• Where should I start?

Thanks again to Alice and Firaas for joining me, and thanks to everyone who attended. The slides are available in PDF here and are embedded below. Balderton is writing up a summary of the event that, once available, I’ll link to here.

Note: both Cognism and Hook at Balderton portfolio companies.

The post The Impact of AI on Go-To-Market: Slides from my Balderton Event appeared first on Kellblog.

The material, which should be familar to long-time Kellblog readers, is largely based on posts that I’ve written over the years and the last slide of the deck has links to specific posts. The six principles are:

• Answer the effing question (ATFQ). Not answering questions wastes time, frustrates coworkers and executives, and can stall your career.
• Know your in-memory analytics. Know what numbers you should know in your sleep, why, and then know them. Executives will often use this as a basic form of competency testing.
• Understand the three fundamental layers of management (manager, director, VP). Learn how to think like the next level. It’s not that easy.
• Write actionable emails. Write messages that are written to be responded to, quickly and tersely. Have empathy for the recipient.
• Be a simplifier. The fastest way to get stuck as a project manager (or equivalent) is to be seen as someone who complexifies simple things instead of simplifying complex things.
• Follow the three golden rules of feedack. It has to honest. It has to be timely. And, the tough one, it has to be kind.

I’ve embedded the slides below and you can download a PDF version here.

The post Six Principles to Optimize Your Results and Your Career (Presentation Slides) appeared first on Kellblog.

Focus is like baseball, hot dogs, apple pie, and Chevrolet. Needed. Timeless. And everyone’s in favor.

But, alas, when you drill in, the conversation often goes something like this:

DK: So what are you focused on?
CEO: Mid-market (MM).
DK: Do you sell anywhere else?
CEO: Yes, for sure. A lot of today’s SMB companies will be mid-market tomorrow, so we want to catch them on the way up.
CEO: Oh, and today’s MMs will be enterprises (ENT) one day, so we sell to them too — so we can learn their product requirements –and our customers can grow without fear. I mean, we’d never want a customer to outgrow our platform.
DK: OK, but what about our marketing, do we have any focus there?
CEO: Well, you know, you can’t really control who clicks on a Google ad, so we can get inbound from SMB, MM, and ENT. We can control geography though. We don’t run ads in Florin or Genovia.
DK: But those are fictional countries from princess movies. How about LinkedIn, you get a lot more control there?
CEO: We’re not doing as much on LinkedIn. Leads are more expensive.
DK: (Thinking that maybe they’re higher quality and more aligned to strategy.) How about industries?
CEO: No, we’re a horizontal play. We have customers in banking, healthcare, pharma, insurance, energy, and retail/CPG. And many others.
DK: Any focus on use-cases?
CEO: Yes. But no. No, we’re a platform play. That’s the beauty. We have an integrated platform that can support dozens of strategic use-cases. So, really, yes and no.
DK: So you’re focused on everything. All size segments, all geos, all industries, and all use-cases?
CEO: Yes. We love focus so much that we have a whole bunch of them. Go big or go home.

At this point, I’m thinking three things:

• My Latin teacher, Mr. Maddaloni, taught me that focus was singular.
• The founder of Southwest Airlines always said that to get big you should think small.
• This is why Geoffrey Moore wrote Crossing the Chasm and Inside the Tornado.

As it turns out, a quick nod to the chasm gods is a lot easier than embracing them. In the rest of this post, I’ll share some tools I use to detect real vs. faux focus and that you can use to sharpen focus in general.

• An ideal customer customer profile (ICP) with concentric circles. Sometimes it’s too binary to have ICP and non-ICP customers, with the result that everything gets equal treatment. Instead, treat your ICP like a bulls eye. Ring zero is credit unions of size X with use-case 1. Ring one is banks of size X with use-case 1. Ring two is insurance companies of size X with use-case 1. Ring three is financial institutions of size X with use-case 2. Ring four is everyone else. I find this increases focus, especially when the inner rings are variations on a core.
• Define the idea of strategic vs. opportunistic revenue. Look, I’ve run startups. Cash is king. You want to give me money, I’ll take it. As long as there are no strings attached. Startups get in trouble when they draw-and-quarter themselves by selling roadmap (i.e., non-existing) features to a diverse set of customers. That’s why you should define strategic revenue (e.g., in the first three ICP rings) vs. opportunistic revenue and then religiously enforce this rule: if it’s oportunistic revenue you have to sell what’s on the truck. Don’t even bother asking for roadmap commitments. Maybe give those sellers lower quotas in return. But don’t let them ruin your future by selling your scarcest resource, R&D capacity, for non-strategic purposes.
• Segmented metrics. Let’s say you’re strong in SMB and your growth strategy is a big up-market push into MM. All of your reported metrics quickly become a variably weighted blend of two different businesses. You’ll find yourself in board meetings saying things like, “well the average sales price isn’t that meaningful because it’s a blend of SMB deals at $10K and MM deals at $40K.” For that matter, neither are average sales cycle, close rate, win rate, loss-to, and other metrics. So, segment these metrics: present SMB, MM, and total (aka, “blended”) figures. The same goes for industries and use-cases. Sometimes you’re doing great on the new strategy but the core business is collapsing faster than you thought. Sometimes, the core business is going gangbusters and you’ve made no progress on the new strategy. Without segmented metrics, you can’t easily tell.
• Not-on-list lists. Planning is an additive process at most startups. “Let’s do this and this and this. Forget anything? OK, let’s add that, too!” To sharpen your focus, add a subtractive element. When you discuss something and decide not to do it, capture that in a not-on-list list. Think: here’s the list of things we decided to do, and here’s a list of things we considered and decided not to do. It will both help your current focus and shorten subsequent debate (think of the asked and answered objection in court).
• Split business units. If you’re constantly arguing it’s actually two different businesses that happen to share a go-to-market (GTM) team, then consider splitting the GTM team. Back in the day at MarkLogic, we had two unlikely bedfellows as businesses: intelligence and media (aka spies and publishers). It helped that our staff literally couldn’t attend meetings in the other segment (e.g., security clearances). So we split our business in two: media and federal. We didn’t have SCs, we had media SCs. We didn’t have consultants, we had federal consultants. We didn’t have a CRO, we had a VP of media and a VP of federal. While this is a pretty extreme approach, in certain situations — particularly when the businesses are pretty far apart — it might make sense. We had two different distribution businesses atop a shared product foundation.

I hope this post has given you a few ideas on how to test your own focus, how to sharpen it, and how to report on it.

The post How to Detect if Your Startup Has a Faux Focus appeared first on Kellblog.

Thanks to Balderton for hosting, to my colleague Claudia Rowe for emceeing, and to everyone for attending this event.

The post Video of Balderton Webinar on Efficient Growth via Entering New Markets appeared first on Kellblog.

Thanks to everyone who attended and/or submitted questions at the event. And thanks to the Balderton team for hosting it.

The slides are embedded below as a slideshow. You can download a PDF version here. A video of the presentation at the event is available in the immediately following post on Kellblog.

The post Slides from Balderton Webinar on Entering New Markets, The Key to Efficient Growth appeared first on Kellblog.

The registration link is here. Since I’ve heard of a few problems with it, let me know if you get stuck: ping me on LinkedIn, Twitter, or leave a blog comment here.

Since 2024 is the year of efficient growth, I have already talked a lot about how to improve efficiency through driving expansion ARR, doubling down on campaigns and segments that work, relentlessly optimizing the go-to-market (GTM) machine, and experimenting with new AI GTM tools [1] that can increase sales and marketing productivity.

After quickly reviewing that material, we’ll switch gears to talk about growth, which almost invariably means not just tuning and optimization, but doing new things. We’ll discuss what I call the strategic expansion hypercube [2], which covers the five (arguably six) dimensions of growth:

• Product
• Use-case
• Industry
• Geography
• Channel
• And arguably, customer [3]

I’ll make some important general observations about the best ways to traverse this strategic epansion cube, providing some real-life examples of where startups fell into quicksand along the way. I’ll then discuss each growth dimension in more detail before wrapping up and doing a brief Q&A session at the end.

I hope to see you there. As always, I’ll be B2B-focused, fast paced, and hopefully fun. Register here.

I’ll post the slides after the event and a recording if Balderton makes one available.

# # #

Notes

[1] See prediction number five.

[2] A fancy name for an N-dimensional matrix. This terminology is actually fairly common in finance orgs because most financial planning solutions are multidimensional in nature and built atop hypercubes as the data abstraction and/or underlying database model.

[3] In one sense, it’s a somewhat less interesting dimension because it has only two members, new and existing. In another, it’s critical and captured in age-old strategy tools like the Ansoff Matrix.

The post See You Wednesday for a Webinar on Efficient Growth Through New Market Expansion appeared first on Kellblog.

I won’t attempt to summarize the episode, but my God I love Costco. From its complex and intertwined origins to its values-driven culture to its relentless — and I mean relentless — focus on execution, it’s an operations dream. Set a margin target and then operate as efficiently as humanly possible in order to deliver the lowest prices to your customers. When you get savings, don’t increase the margins, decrease the prices. A small margin on a huge volume is still a huge amount of profit. Goosebumps.

And all of this without even mentioning the hot dog. Go take a long drive and listen to the episode. You’ll love it.

My favorite part of the episode is where they discuss what makes Costco work. In business, we love stories with magic wands. Strategists loves cases where, with enough analysis and thought, you identify one key lever that changes everything. Heck, I think I’m a strategist and I love those stories, too. But the answer to every question in business isn’t always about one thing.

Searching for Costco’s one thing is as fruitful as searching for El Dorado. There isn’t one. It doesn’t exist. Instead, there are 50 little things. That all work together. Excerpt:

I want to say a Jim Sinegal quote, “This isn’t a tricky business. We just tried to sell high quality merchandise at a lower cost than everybody else.” I think it’s hilariously farcical. He’s both right and so cheeky. This is an extremely tricky business. […] We’ve talked about a bunch of it in this episode. It’s the 50 little things that they do that all synchronize with each other that makes it work. You don’t do one of those and it falls apart.

That was a few weeks ago. Yesterday, I was in the car again, listening to the Revenue Vitals episode with Alice de Courcy from Cognism discussing how to scale a demand engine. And a light bulb goes off. The trigger was this conversation with host Chris Walker around minute 25. Quoting Chris:

I seriously want to double click on the point that these non-sexy, quote-unquote boring things are typically the things sitting right in front of your face that will drive the highest impact without a lot of money. I collect feedback after we complete engagements and customers will sometimes say, “I wish you brought a lot more innovative, sexy ideas,” and I say, “well, I brought you the three ideas that are sitting right in front of your face that are going to double your pipeline.”

So I think there’s a balance here between new, sexy things […] and how are we going to convert our website demos by 2% more each year so we can get $6M in additional revenue. The things that are sexy [to me] are the things that drive results.

Great marketing machines are like Costco. There is no magic wand. There is no secret lever. It’s about 50 little things, all working together. And that’s one reason why people have trouble understanding them. This may be obvious, but I’d never previously seen it so clearly. CMOs show the funnel slide in board meetings with stages and conversion rates. But no one really understands the machine. They ask a few random questions, usually about channels. The inevitable attribution conversation follows. You can almost feel them searching for the one thing. (Or the one cock up.) But in this case, there isn’t one.

Alice calls this concept loops. I think of it as concentric circles. You build marketing programs out from a core of ongoing, proven programs to the edges of new, experimental campaigns. That’s why when I present marketing, I like to discuss high-level concepts that people can easily understand. Here are the three big themes for the quarter. Here’s our demandgen machine, shown in concentric circles of provenness. And here’s what it’s producing, ending always with the number of opportunities it produced and at what cost for each.

You want to dive into the machine? We can do that — I love the machine — but that’s like asking what makes Costco work. Let’s schedule a three-hour meeting to discuss it — and I’m not stonewalling here. But if you want to jump into the machine to try and help optimize cost/oppty (which is usually the intent), then you need to understand how it works. Or more cheekily, that’s why you pay me. But if you’re interested, really interested, then let’s jump in.

For more on the notion of a marketing machine and how to build one, see this deck or watch this video. And a quick thank you and shout out to former dbt Labs CMO Janessa Lantz for her kind words on that deck and on Kellblog in general.

The post Great Marketing Machines Are Like Costco appeared first on Kellblog.

It is always, always a pleasure to speak with Harry. He’s such an effective interviewer that you quickly get into detail and stories that others miss. So you end up with very rich content, which in this case lasts significantly longer than 20 minutes. (More like 72, but who’s counting?)

In this episode we hit on a wide range of topics including:

• The metrics that matter in SaaS today
• Why CAC Payback is flawed and CAC ratio is better
• Why you need to hire sales reps three-at-a-time (aka, modulo 3)
• How to forecast in 2024 and in general (keyword: triangulate)
• The biggest mistakes made in forecasting, and how sales management practices can confound the forecasting process
• Why renewals are harder than ever to get (but alas easier to forecast)
• What all this means for Customer Success (both the disicpline and the department)

I’ve embedded the video of the episode below. I hope you can make time to watch or listen to it. And thanks again to Harry for having me.

The post My Most Recent Appearance on 20VC with Harry Stebbings appeared first on Kellblog.

The event came as a result of Nick reading this Kellblog post, The One Question to Ask Before You Blow Up Your Customer Success Team, which led to a conversation about it. At some point Nick asked, wouldn’t this be an interesting conversation for everyone? And thus, the event was born.

Expect it to be informal (no slides), conversational, and interactive because I know Nick wants to get questions from the crowd. More than anything, expect us to simultaneously address a serious and timely topic for C-level and Customer Success leaders, and to nevertheless have some fun while doing it.

I hope to see you there!

The post Join Me for a Chat with Nick Mehta: How To Prevent Your Customer Success Team from Getting the Axe appeared first on Kellblog.

This is neither a how-to book on building startups nor self-interested VC propaganda designed to foster more startup activity. More than anything, I’d say it’s a public policy book that includes a strong dose of technology history. The book is designed to help us first extrapolate possible future scenarios, and then select policies that drive us towards the more positive outcomes on the spectrum. The book doesn’t argue that society should trend towards entrepreneurialism, it presents a matter-of-fact case that it is doing so inexorably, for both better and worse. It then asks a series of “so what should we do about that” questions that take us into public policy.

Everyday-Everyone Entrepreneurship

The world envisioned is one of everyday-everyone entrepreneurship, which James defines as a state where people:

• Have meaningful ownership of what they produce.
• Earn in a proportional way to their (or their product’s) success.
• Can be self-directed, at least most of the time.
• Can choose how to solve a problem, and with whom to work to solve it.

What I like best about the book is that it doesn’t tap dance around difficult questions of societal structure and power, both past and future. You cannot discuss this kind of material without considering winners and losers. While the book certainly has an optimistic bent, it also provokes the reader to consider the alternatives. Excerpt:

“[Bloodworth’s] conclusion [in Hired] was that many of the rights and benefits the labor movements and trade unions had won, over several generations — from sick pay and holidays to maternity and paternity leave, pensions, and safe, civilized, working conditions — had been undermined, not by political opposition but by technological innovation and and ruthlessly demanding business models.”

For example, income security seems out the window in the eat-what-you-kill world of individual entrepreneurship. But was it already out the window anyway with the steady erosion of the social contract between employer and employee? And to the public policy angle, what should we, as a society, do about it? 

Or, more topically, will AI create more jobs than it displaces — as James suggests and has been the historical pattern with new, disruptive technologies? Or will we eventually find ourselves in some more dystopian Logan’s Run type scenario?

After an in-depth review of several policy areas, the book concludes with An Entrepreneur’s Manifesto that offers sixteen specific policy suggestions grouped into three areas: find work, fair work, and fulfilling work. You can learn more about the manifesto by watching this lecture.

For more information on the book, you can read this Publisher’s Weekly review, peruse this Q&A with James, or even chat with the book.

My Overall Take

Overall, Startup Century is a worthy read — the history lessons alone are worth the price of admission. The public policy is less my passion, but the book nevertheless poses important policy questions and considers them in depth and with thoughtfulness that I have not previously encountered. 

As James notes, “in his writings on capitalism, […] Schumpeter both championed capitalism and predicted its demise, […] warning that capitalism would inevitably morph into cronyism and give rise to oligopolies.” 

Let us hope that everyday-everyone entrepreneurship can help prevent that demise and that we can collectively develop answers to James’ questions that lead us together, and successfully, into the startup century.

The post Startup Century: A New Book on Technology Policy by Balderton’s James Wise appeared first on Kellblog.