How? A ton of grinding and some luck – but it taught us one key insights about activation that changed the way I think about growth.

Activation starts before onboarding.

When we figured this out – our monthly sign ups quadrupled, our activated user base jumped from hundreds to tens of thousands, and our self-serve revenue went from a side thought to doing half our overall revenue.

Let me walk you through it.

But first – some stats:

1. Our Youtube monthly views grew from a consistent 5k each month in Feb to over 50k by December. This translated to a 5x increase in monthly sign ups for Voiceflow.

2. Our product activation rate increased dramatically. Going from <3% to a peak of 17%. Driving more conversions than we had ever seen in the business and creating a thriving community outside of the core app.

3. Our Youtube subscriber count grew from 4000 with an average of 100 new subscribers each month to 10,000 with an average of 800 new subscribers each month. Allowing us to start compounding the traffic we were getting.

Lets walk you through how that happened.

Last year we had a pretty big challenge. We had been focused on enterprise for the last 2 years and no longer had a strong PLG motion, but needed to change that quickly.

Voiceflow is a platform to build mid-high complexity AI assistant. Previously this market was limited to enterprise, but with ChatGPT becoming more mainstream and stronger AI models (LLMs) becoming available, the market for AI assistants was expanding from something that only big enterprise companies had to a commodity that every SMB and Midmarket customer would want.

We needed to find a way to get more people launching production AI assistants on Voiceflow in a self-serve way. But Voiceflow is a fairly complex tool to use and learn.

On the enterprise side our activation motion was fairly high-touch (I built our Enterprise success team). We would spend a few weeks doing office hours and demos and guide customers through getting their first MVP stood up on the platform. It would usually take 2-3 months for a customer to fully get it and transition from beginner to expert.

This was okay for large enterprise deals with a lot of skin in the game but would not scale. We were already feeling the impacts of it on our self-serve user base.

To give some tangible numbers, our self-serve activation rate was really low. We had tons of sign ups but the number of people who were able to understand how to properly use Voiceflow and launch to production in 30 days was <3%. (note: I’m a disciple of Lauryn Isford at Airtable – that a truly meaningful activation metric is likely going to be in the single digits, and it should constantly be shifting down the funnel).

The main cause of this is that Voiceflow is a platform. Its the best tool available for building a mid-high complexity AI assistant with your team, but takes a while to master and has a steep learning curve. Similar to Webflow for websites or Figma for design. There are plenty of different ways to get started and plenty of different things you can build, but getting to your ideal end goal is often highly customized.

If we could move this metric even up to 5%, it would cause a pretty significant impact downstream.

Where did we start?

When I moved over to lead our Growth side in February to tackle this problem, there wasn’t really a clear point to start from.

The obvious one was to see how we could create a smoother onboarding experience. Replicate what we had created for our high-touch enterprise clients in a low-touch way.

But that didn’t really work out.

My team spent a ton of time tinkering with pop ups in the onboarding flow telling users how to get started, defined milestones, and aligned our email flows to them.

We looked at our data to figure out if there was some inflection point that we could drive people too and tried ways to optimize that in the app with notifications, re-activation loops, and all the things that you read about.

Some of these helped move it slightly upwards – but honestly nothing was really moving the needle for us. Our goal was to make big game changing improvements, not do micro-optimization to an existing flow.

Thats when one of our experiments showed us the one key learning that was holding us back from having a significant impact.

Activation starts before onboarding.

When you’re building a platform that can do many things, the hardest part is aligning your users and product with one end goal. People come to your product with an idea in mind but don’t have a roadmap to get there.

People saw Voiceflow somewhere online and had different ideas of what they wanted to build and no roadmap to get there. They wanted a discord assistant that could moderate their community, a personal assistant that could manage their day, a support assistant that managed all their customer communication. But all they had to figure it out were generic tutorials and tooltips focused on features not outcomes.

This resulted in most users coming to the product, playing around a little, and then leaving because they couldn’t figure out how to get to their end goal.

If we wanted to make a real impact on activation – that was where we needed to start. Align people on a use case, and provide them the step by step resources to get there quickly.

(Huge shoutout to Reforge, Tom Willerer, + Hannah & Curt from Openview for pointing me in the right direction here!)

Reforge has this excellent chart on the 4 fits of activation, where it looks at Audience Fit, Promise Fit, Intent Fit, and Knowledge Fit.

A key part of what we were missing is the promise fit. Its aligning a users expectation of what they can achieve with the core value of the product. Then giving them the resources to get there.

Thats where we started playing around with templates, demos, and tutorials.

We took our efforts to pre-sign up and tried to show people what they could build with Voiceflow and made a ton of different templates, long form step-by-step guides, and code repos to help them get started.

This was the first thing we did that made a significant difference and started compounding growth in our activation rate.

Here’s how we broke it down

The proposed experiment:

If we showcase really specific use cases with Voiceflow (ie. turn a PDF into a research assistant in 3 minutes, an AI discord bot for moderators in 5 minutes) and pair them with hyper specific resources – will that improve our activation rate for those customers?

We did the following:

Created short demos (a few minutes) and tutorials for specific Voiceflow use cases.

Distribute them on our personal LinkedIns, using our network to get some initial views.

Created templates showing exactly how to build it.

At first it felt a little gimmicky. These are pretty simple use cases and you can do so much more on the platform. I felt like we weren’t really showing off all the bells and whistles and were missing the more complex stuff that really separated us out (spoiler: no one cares about them at the beginning)

But once we were consistently pushing them out – these posts started getting some traction. First a handful or users, then 10s, then low hundreds.

It wasn’t anything dramatic – but the real impact was after sign up. Users who signed up from our tutorials had a 10x activation rate compared to others (yea you read that right – 10x).

So we doubled down on distribution. Lets find ways to get more people watching these videos and figure out where they get stuck. Then rinse and repeat.

Taking It to YouTube, Starting a Discord Community and Refining the Process:

Everything we made, we also hosted on YouTube so it could be discovered easier and over time.

We already had a decent following of 4000 subscribers from our OG days as an Alexa skill builder, but all of our content was geared at existing users with generic tutorials, feature demos, and webinar records.

Youtube was an educational channel for us, not a discovery channel and we were treating it that way (ie. just reposting videos on there from our docs, product releases, etc).

At first we were just letting the videos we posted be discovered organically. Getting a few hundred views max. But then we started being more intentional.

We did keyword research via SEMrush and Keywords Everywhere to see what were emerging topics that we could build tutorials on/competitive keywords that our Youtube videos could appear in the top search for.

(Shoutout to Steph Smith’s Doing Content Right ebook for bootstrapping my SEO knowledge – great resource for brand building and finding trends)

We saw our view count start to creep up close to the 1k mark and eventually pass it. But we didn’t stop there.

The green line represents views from new users vs subscribers. After we started optimizing our videos we had a steady increase in the number of new users watching our videos.

Whenever someone signed up from a tutorial, we targeted them with a personalized invite to our discord, and offered 15-minute calls with a ‘product expert’ (aka. me) to understand where gaps were and help them.

I think at the peak I was doing like 20-30 calls a week talking to users and figuring out what they wanted to build/where the gaps were. Didn’t matter who they were, we were talking to them. This helped us learn at an extremely fast rate where all the hurdles were.

We also started our discord community around that time as a way to talk to these users and get them started faster (our discord community also went from 100s to 1000s – but more on that in another article). That worked really well. It let us talk to users and answer their questions async with loom videos. We started to see the same questions pop up over and over again which made it very obvious where the gaps where.

So we worked double time to create and pump out new, longer form videos and tutorials. We thought we needed to do catchy 5 minute clips – but it turns out once someone decided to use Voiceflow for a specific person they actually wanted long form 30+ minute tutorials filled with how to debug and work through problems.

A big wave of Growth:

As we started activating more users something really unexpected happened – we started seeing videos of Voiceflow pop up all over YouTube (not created by us)! We actually started getting more videos created by community members than we were creating ourselves.

It turns out that some of our new users turned into champions. They loved what they built and started making their own Youtube tutorials to help other people learn how to use Voiceflow too.

It was surreal to see and triggered a growth loop. New users found us on Youtube, joined discord, got activated, and created content. Leading to more people finding us and so on!

Preparation meets timing

We started seeing this impact our in June/July. Not only did we start getting more sign ups, but our overall activation rate started going up and we had our first significant increase in self-serve MRR in 1.5 years.

Then we started to catch a trend that pushed us up dramatically. As GPT became more robust, more businesses started wanting AI chatbots. This expanded our market from primarily enterprise (you needed to have a big team to create a powerful assistant) to everyone from SMBs to MidMarket.

With this rise in demand came a number of brand new agencies that were looking to service these businesses. These were smaller existing agencies in the digital marketing space that started seeing demand for AI chatbots from their clients. Mostly for lead-gen and support use cases.

We started seeing a flood of them come in as ‘AI Automation Agencies’ started to become a trending search term.

We did as many interviews as we could with the new users joining our community in waves and kept hearing the same set of names and communities that were driving them to Voiceflow.

Two of our community members mentioning that they found us through the same discord community

So we doubled down.

Partnerships

One of the big factors driving this new growth of agencies were a set of Youtube influencers who had agencies and were walking others through tooling and how to adapt and serve this new market.

We started doing a bit of digging and found a few of them that were still starting out but had a serious vision for the future of AI assistants and really aligned with our values of building serious businesses for the long term.

We decided to take a big bet on Liam Ottley and struck a partnership agreement with him and his team. We shared the same vision for the future of AI assistants and found that Liam not only had his own agency servicing large clients, but had strong conviction and a unique perspective on the future of AI assistants for Businesses aswell as the skillset that agency owners would need to gain to adapt to this market.

This allowed us to double down on the momentum and inspired other Youtubers in this niche to start adopting Voicefow as the gold standard for building AI assistants.

The result? A 5x increase in our MRR, turning it from a dead channel to our newest revenue driver and a production user base that is compounding every month.

What now?

The last 8 months were a whirlwind but Its not all sunshine and rainbows, while we have way more people entering our top of funnel with the goal of launching a production assistant we still have similar challenges later down the funnel with this level of growth our churn rates went up and our community grew way past what we could handle.

While its clearer than it was before – users still come across the spectrum of use cases and technical skill. For us to continue to grow we need to adapt to new challenges.

1. How can we learn which users and use cases are the stickiest and narrow our top of funnel to them
2. How can we attract the niche set of users that want/need a complex AI assistant that aligns strongly with our value prop
3. How can we better support our user journey from they 1st production interaction to their 10,000th.

We learned alot with the large number of new users we gained and our focus for the next 8months is to continue narrowing down our Audience Fit and Promise Fit for the core use case of medium-high complexity AI Customer Experience. This is where we’ve seen the strongest fit in customers that have come to Voiceflow and launched production assistants.

Leading a growth team is a bit like working in waves. You try a number of different experiments, double down on the ones that work, find ways to build momentum, then learn, slow down, and repeat the process with a bit more focus each time (and pray that luck/timing is on your side).

Always stressful, but never a dull moment.

If you want to learn more about this journey and the future of AI assistants – hit follow on LinkedIn and check out our Youtube channel @voiceflow!

Key Takeaways

1. Alignment: We showed what you could build on Voiceflow to align expectations.
2. Guidance: Videos + templates = step-by-step guide.
3. Optimization: We spent a ton of time talking to users and making it easier to go from sign-up to a live app.
4. Partnerships: Find out how your customers are finding out about you and double down on people or complimentary businesses that are sending them your way.

The post How Youtube 5x’d our Activation Rate appeared first on OpenView.

So, buckle up and let’s dive into what’s coming, what’s changing, and what it all means for you.

Review of last year’s predictions

Before we get into 2024, let’s give a report card to the predictions I made last year.

Efficiency as a necessity: Grade: A

Yes, 2023 was all about a potential recession and budget constraints​​. Gartner’s 2023 CMO Spend and Strategy Survey found 71% thought their budgets for 2023 weren’t enough to fully execute their plans and 75% were attempting the proverbial “do more with less”. This was also born out via data from OpenView’s recent SaaS Benchmarks report.

Follow-on prediction: Interest rates will remain high, cheap money will be scarce, and discretionary marketing budgets, headcount, and software investments will continue to be scrutinized heavily in 2024.

A new model for Sales and Marketing alignment: Grade: B

While there has been improvement – 53% of sales people say they’re aligned with marketing and 51% of marketers agreed that they’re aligned with sales, up from 42% and 44% respectively in 2022  – we’re still a long ways away from sales and marketing teams acting like an integrated soccer team.

Follow-on prediction: Alignment remains the number one priority to drive growth, according to the 2023 C-Suite Go-to-Market Benchmark Survey from DemandGen Report and Demandbase. But most companies will not achieve true integrated orchestration for years, if ever.

“Everyone-Sourced Pipeline” takes over from Marketing-Sourced Pipeline: Grade: C

While the concept of a unified revenue team is gaining traction, the shift from traditional metrics like marketing-sourced and marketing-influenced pipeline isn’t as pronounced or rapid as I’d hoped. The C-Suite Go-to-Market Benchmark Survey found the top marketing metrics in use today are total revenue (18%), MQLs (16%), marketing-influenced revenue (12%), and marketing-sourced revenue (10%).

Follow-on prediction: The move to shared metrics across sales and marketing is inevitable but difficult, given how entrenched the current models are. In 2024, we will see more companies adopt “team-based pipeline” but it will remain the minority.

The continued rise of the self-service buyer: Grade: A+

With Gartner reporting that 75% of B2B buyers prefer to buy online without interacting with a salesperson (Forrester reports 70%) and growing numbers of Millenials and Gen Z in the decision-maker seat, the shift towards frictionless self-service buying is only accelerating.

Follow-on prediction: In 2024 B2B marketers will invest in content and tools that enable buyers to make informed decisions independently. More – though still not most – companies will provide transparent pricing and personalized product demos without the initial involvement of sales representatives. (In fact, TrustRadius’s 2023 B2B Buying Disconnect report found 72% of buyers said access to transparent pricing made them more likely to purchase a product, while pricing not being available continues to be the number one reason buyers are less likely to purchase from you.)

The lines between ABM & Demand Gen get blurrier: Grade: B

The trend towards thinking of your go-to-market strategies as a spectrum, blending ABM and Demand Gen tactics, is happening – but not as fast as I expected. On the technology side, I don’t see the ABM or marketing automation vendors implementing blurred capabilities (yet).

Follow-on musing: I wonder if Malachi Threadgill’s departure from Forrester will delay their proposed merging of the ABM Platform and B2B Marketing Automation Platform Wave reports? That would have been one of the biggest drivers of this trend.

Privacy will still be consequential: Grade: A-

Not only is this true, but as I wrote, “privacy will always be consequential”. And the importance of proper use of first-party data is even more critical given the voracious appetite for data from AI systems.

Follow-on prediction: I also wrote “[cookies will] be sticking around for a while longer still”; as noted below, 2024 is the year we’ll finally see the end of third-party cookies.

Delayed embracing of the metaverse and Web3: Grade: A

There’s still skepticism and slow adoption in the B2B sector regarding the metaverse and Web3, reflecting a cautious approach as I predicted.

Follow-on prediction: These trends remain buried under a mountain of AI.

Dark communities’ place in the buying process will keep growing: Grade: B-

This isn’t growing as fast as I predicted. The TrustRadius 2023 B2B Buying Disconnect report found that buyers consulting communities/forums to inform their purchasing decisions decreased from 37% in 2022 to 24% in 2023.

Follow-on prediction: Despite the decline in 2023, buyers do want to research off your website and consult with peers. Whether it’s a formal community or informal networking with peers, so-called “dark social” will remain part of the equation. A related prediction: these touches can’t be tracked by attribution software, which will accelerate the decline of traditional attribution.

Artificial Intelligence, the prediction I missed completely: Grade: F

I wrote my 2023 predictions in October, 2022, about a month before the release of ChatGPT. Who knew? I failed to even mention what would be the most important trend of the year, likely the decade. Oops. (Read on for more about AI.)

My Predictions for B2B Marketing in 2024

OK, here we go.

1. AI in B2B marketing is overhyped in the short run, and underestimated in the long run
2. Email marketing isn’t dead or dying — but it is evolving (quality over quantity)
3. The traditional B2B demand gen playbook will continue to decline in effectiveness
4. We will see more content based on original research and data
5. Leading companies will build brand by investing in owned media and engaged communities
6. New “XLG” go-to-market motions will continue even as companies embrace blended approaches
7. B2B marketers will not be prepared for the end of third-party cookies
8. Innovative marketers will start tracking Qualified Buying Groups (QBGs) instead of MQLs

1. AI in B2B marketing is overhyped in the short run, and underestimated in the long run

Amara’s Law states that “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run,” and I think that’s exactly what’s happening with AI.

In 2024 we’re going to see a rising backlash against the “first-generation” uses of AI. We’re already seeing a surge of startups offering a light layer of content personalization on top of existing tools. This will just lead to more mediocre, inauthentic content, even as B2B buyers are already overwhelmed with too much irrelevant content. In fact, Forrester predicts that thinly customized generative AI content will further degrade the purchase experience for 70% of B2B buyers. Google’s concern over AI-generated content quality, as indicated by its November 2023 update targeting unhelpful content, is a further testament to this issue.

In 2024, our social feeds will be full of people posting a range of AI mishaps, from hallucinated facts to poor personalization to ill-timed offers. While sometimes amusing, these will remind us that AI for now is better suited as a “co-pilot” than “autopilot”, augmenting human skills and working in tandem with our creativity and strategic thinking.

All that said, if we look past 2024, most marketers are underestimating the absolutely transformational impact AI will have on go-to-market strategies and platforms. In the future, text-based conversational user interfaces will automatically identify the right audience for any campaign, eliminating the need for marketing operations expertise in data and complex segmentation rules. AI will also enhance the effectiveness of existing campaigns by streamlining A/B testing and segmentation, and will transform broadcast-like campaigns into engaging conversations, analyzing and reacting to user responses appropriately.

Looking further ahead, we’ll see a shift from human-in-the-loop systems to more automated, agent-based systems. These systems will have the autonomy to solve problems and access external tools, leading to a network of interconnected agents rather than monolithic platforms.

The culmination of these advancements will be the emergence of true self-driving marketing capabilities. These will function like a large language model, but instead of predicting the next word they will predict the next optimal interaction for each buyer. In this future, the role of marketers will pivot to focus on training and tuning the models and setting strategic goals and constraints. This will be a fundamental departure from current marketing automation platforms and will radically transform how marketers use technology.

In sum, the long-term impact of AI on B2B marketing is not just an enhancement of existing practices but a complete reinvention of how marketing works. This will create new jobs, eliminate others, and transform the day to day of every marketer. It will perhaps even transform what we think marketing even is.

What do you think?

2. Email marketing isn’t dead or dying – but it is evolving (quality over quantity)

Email has been used as a marketing tool for over 40 years – and people have been predicting its death for nearly half that time. Yet, somehow, email has remained not only relevant but also imperative for B2B marketing. Sure, we might use Slack or Teams for internal communications, and instant messaging for our friends and family, but email remains the preferred way to receive offers: in fact, Adobe reports 56% of buyers prefer to receive B2B offers over email, significantly more than direct mail, social, and other marketing channels.

Email has remained critical in marketing in part because it has capabilities not found in text messages or messaging. When you need clickable links, URLs with UTM parameters, open tracking, easy replies, attachments or images, and so on, you have email.

At the same time, email is far from perfect. Buyers complain about too many emails, too much junk. The important messages get lost in the clutter, and the sheer volume leads to disengagement. Additionally, marketers and sellers too often take the easy way out and don’t personalize or use personalization that doesn’t actually add value to the recipient (“I see your company announced an award last week, congrats!”). It’s no surprise that Adobe finds buyers say only 25% of emails from brands are interesting or compelling enough to even be worth opening.

The goal in 2024 is to have people receive fewer emails, but for each email to be more relevant, more engaging, and thus, more valuable. This is being enforced by the email clients: starting February 1st, Google and Yahoo are imposing stringent requirements to curb unwanted mass emails, especially from sellers using sales engagement platforms.

Personalization will be essential. This is not “Dear {{lead.First Name:default= }}” or even “I saw you went to school at Duke” personalization; it’s about being relevant to a recipient’s actual interests. We’ll also see an increased use of the human touch in email marketing. Emails from real people have higher open rates, especially when they come authentically from people the recipient knows or executives at your company. And as buyers become sensitized to poorly customized, AI-written emails, we will see the pendulum swing towards genuine human interactions.

So, as we step into 2024, it’s not time for email to die, but for its transformation into a more relevant, personal, and authentic tool. This evolution is an exciting opportunity for marketers to innovate, connect more meaningfully with our audiences, and create truly engaging experiences.

3. The traditional B2B demand gen playbook will continue to decline in effectiveness

I’ve previously written that the traditional marketing playbook (you know, the one I helped create at Marketo) doesn’t work anymore. Years of eBook promotions, nurturing emails, social posts, and SDR outreach have numbed buyers to the traditional tactics, and our buyers are staying unknown and anonymous for longer. At the same time, the traditional playbook’s focus on highly measurable tactics has left us over-rotated towards short-term pipeline creation and significantly underinvested in brand and other parts of the revenue cycle, specifically pipeline closing and retention.

The new B2B playbook calls for marketers and their companies to evolve:

• Invest in brand creation that speaks to buyers’ emotions before they are in-market.
• Use intent and AI to identify when buyers are in-market.
• Focus on all aspects of revenue including retention and expansion efforts, not just acquiring net-new pipeline.
• Deeply align with sales every step of the way.

Of course, all this is easier said than done. Too many boards and CFOs still insist on making every marketing dollar measurable and tracking outdated metrics like MQLs and marketing-sourced pipeline. Short-term thinking pervades the annual budgeting processes, and many CMOs haven’t built the credibility or conviction needed to budget for longer-term, harder to measure initiatives that build brand but not immediate pipeline.

The result? In 2024, only a fraction of companies will fully embrace the new B2B playbook, and the ones that don’t will continue to feel the declining effectiveness of the traditional tactics. Nonetheless, the new playbook is inevitable, and over the next few years we’ll see more and more companies adopting its principles and evolving their thinking and metrics.

4. We will see more content based on original research and data

Part of the decline in the traditional B2B playbook comes from the dramatic changes happening in content marketing. It’s no longer about gated eBooks or Definitive Guides. For better or worse, people don’t want long-form content anymore. (I appreciate the irony of writing that in a 4,000+ word article.) And as discussed, GenAI is making this problem worse by simultaneously increasing the volume and decreasing the value of written content.

Nonetheless, building a brand with quality content and thought leadership remains vital, so what’s a company to do? One strategy is more content grounded in original research and data – think in-depth reports, industry surveys, and data-driven content – which will become the gold-standard for thought leadership. This type of content relies on unique data, often owned and/or gathered by the company itself, to provide fresh insights and perspectives. According to the Content to Conversion Report from Pavillion and Kickstand, executives in particular appreciate this kind of content.

Gong Labs is the best example of this trend. They analyze the sales interactions captured by their product, which includes transcripts of web meetings and phone calls, as well as emails from across their customer base. This wealth of proprietary information is then analyzed in aggregate form to create content on various sales topics, such as the effectiveness of different cold email strategies or when you should, or should not, swear on a sales call. This is branding gold for Gong for two reasons: first, it’s full of really useful and insightful insights that their target audience truly values, and second, it can only come from Gong and their unique data set.

In essence, companies that invest in generating content backed by analyzing proprietary data, similar to Gong Labs, will enhance their brands and better position themselves to influence their target audience, particularly with executive buyers.

5. Leading companies will build brand by investing in owned media and engaged communities

Anyone involved in B2B content marketing knows that getting the content distributed and seen by the right audience is usually a bigger challenge than getting it created. For years, we’ve been stuck relying on social media and SEO to get our content found. But these are fickle friends. They’re governed by algorithms beyond our control, often pushing us towards paid promotions to gain visibility.

The solution? As evangelized by Anthony Kennada of AudiencePlus, traditional B2B content marketing needs to evolve to owned audiences and communities. This means creating and publishing content on your own platform and directly distributing the content to your audience.

Owned media encompasses channels entirely under a company’s control, including websites, blogs, and email newsletters; new content formats such as short and long-form videos, podcasts, live streams, online courses, certification programs, and branded apps; and live experiences such as events and conferences. An intriguing addition to this mix is “merch.” As my daughter’s Taylor Swift obsession shows, merch is different from SWAG. SWAG (“stuff we all get”) is given away for free to promote a brand, such as pens and water bottles with the company logo. Merch is focused on the desires of the audience and extends to items and designs that have an intrinsic value or desirability beyond the logo or brand; as such, the audience is willing to pay for it with money – or attention.

The primary advantage of owned media is cost-effectiveness. When you control the distribution, the cost drops significantly, often to zero. This not only reduces expenditure but also ensures unfettered reach to your audience. It’s about nurturing direct relationships, where communications are not mediated by third-party platforms or algorithms.

Central to this strategy is building subscribers, not just opt-ins. Subscribers actively indicate a desire for ongoing engagement and value from your content. The key is to 1) offer publicly accessible, high-quality content to showcase value, and then 2) provide exclusive content, experiences, and merch to subscribers, thereby nurturing a sense of exclusivity and community.

Ultimately, the goal transcends mere audience building; it’s about creating a community of fans, and advocates that help you create brand recognition and grow the business. We’ll also continue to see influencer marketing grow in popularity as part of this trend. A key reason is trust: buyers are more likely to trust people than companies, and in B2B they especially trust experts.

This is a far cry from traditional content marketing, but it’s where the industry is headed, and the best content marketers will evolve and adapt accordingly.

6. New “XLG” go-to-market motions will continue even as companies embrace blended approaches

Over the last year or so, we’ve seen an explosion of giving names to what John Common of Intelligent Demand calls the various XLG go-to-market motions (where X = the driving function). GTM Partners names some of these approaches, including:

• Product-Led Growth (PLG): Centers around the product itself driving customer acquisition, expansion, and retention. (Slack, Canva, Zoom are all good examples.)
• Ecosystem-Led Growth (ELG): Leverages partnerships for market expansion and product distribution. Also called Partner-Led Growth, but PLG is taken. (Example: 95% of Microsoft revenue comes from its reseller network.)
• Inbound-Led Growth (ILG): Focuses on attracting customers through content, SEO, and digital strategies. Also can be called Marketing-Led Growth or MLG. (Hubspot and the early days of Marketo were good examples of this.)
• Sales-Led Growth (SLG): Involves proactive outreach to potential customers through channels like outbound email and working existing relationships; more growth comes from more sales reps. (Salesforce and Oracle are examples.)
• Community-Led Growth (CLG): Builds and engages with communities to foster brand loyalty, advocacy, and growth. (GitHub and Figma both benefit from their communities.)

Do a simple search and you’ll see that each motion has multiple websites, self-proclaimed experts, and even industry events dedicated to them.

You’ll also see that most of these concepts are not new. But that’s OK. What’s happening now is more about giving a formal name and structure to well-established practices, which is a key element of codifying structured frameworks into best practices. So if using PLG or ELG or CLG or any other XLG works for your business, then I say embrace it.

First, I predict we’ll continue to see all of these approaches gain traction in 2024. Companies will leverage partnerships more strategically as human trust and connections gain importance in business relationships. As discussed above, communities will become an increasingly important part of building brand and awareness, and marketers will invest in PLG-like tools to enable self-service buyers. And so on.

However, there’s a caveat. Overemphasizing one GTM approach can cause companies to neglect other equally vital strategies and lead to an imbalance. The real challenge lies in not getting tunnel-visioned by one approach while overlooking the rest.

Therefore I also predict that companies will increasingly recognize the need for using a blend of these GTM approaches, tailored to their unique market conditions, deal sizes, and product offerings. The blending of inbound and sales-led motions, including blurring of ABM and demand generation, will continue. Product-led companies will keep building enterprise sales teams to complement their motion. Companies of all sorts will embrace partners and communities. And through all of this, we’ll come to understand that no matter what we call it, it’s all just go-to-market.

7. B2B marketers will not be prepared for the end of third-party cookies

Against the backdrop of privacy concerns and regulations, after two years of delays Google Chrome will finally phase out third-party cookies in 2024.

A great deal has been written about what cookie deprecation means for companies (including this and this), but despite the delay and lots of advice, I predict most B2B marketers will not be prepared.

Common approaches for dealing with the end of third-party cookies include:

• First-Party Data: Take advantage of data directly obtained from your own systems to gain deeper insights and drive personalized marketing efforts.
• Consent Managers: Implement consent management platforms to ensure transparent user consent and compliance with data privacy regulations for any data collection and processing activities.
• Contextual Advertising: Focus on aligning your advertising content with the context of the web page the user is engaged with.
• Google Topics (Cohorts): Use Google’s privacy-centric Topics API for audience targeting, which groups users into cohorts based on interests while preserving individual anonymity (formerly called FLoC).
• Unified ID 2.0: Utilize Unified ID 2.0 (or other alternative IDs), which will rely on encrypted and hashed user identifiers (like email addresses) to maintain targeted advertising with a focus on privacy and user consent.
• Sites Where Users Are Logged In: Leverage the consented user data available within social media platforms and other walled gardens to target specific audience segments effectively.
• Lookalike Audiences: Build audiences with your first-party data and use AI to identify anonymized third-party audiences with similar characteristics.
• Google Protected Audiences: Obtain user consent to add visitors on your site to specific interest groups, enabling you to engage in targeted remarketing to these users when they visit other sites (formerly called FLEDGE).

On the plus side, most B2B companies I work with have solid first-party data strategies, with well-managed CRM, ABM, and marketing automation platforms and consent management in place. (If you don’t, it’s time to get on this!)

Also, B2B advertising vendors (including Demandbase) are working on contextual advertising, in which ads are placed based on the content of the web page instead of user behavior. So, for example, if you want to target companies showing intent for “digital security” topics, you’ll place your ads on web pages that talk about it. This approach respects privacy and doesn’t rely on third-party cookies.

However, the other solutions raise more questions than answers for B2B companies.

Regarding Google Topics, there are 470 proposed interests (documented here), but the vast majority are consumer-focused such as Arts & Entertainment, Autos & Vehicles, Beauty & Fitness, Food & Drink, Games, Shopping, Sports, and so on. There are a few Business & Industrial topics, but it’s unlikely they will be granular enough for most B2B companies.

Similarly, it seems like Unified ID 2.0 and other options for targeting logged-in users (such as social media and walled gardens) have a B2C-bias, where the email used as an identifier will primarily be consumer emails, not work emails. Given the low match rates for mapping B2B and B2C identities (typically 30%), it’s unclear how B2B companies, whose first-party data is full of professional emails, will be able to take advantage of these solutions. I wonder if a similar B2C bias will exist for lookalike audiences; in a B2B setting, lookalike audiences would want to focus more on firmographic and technographic data points, but it’s not clear those will be available as options.

Protected Audiences holds the most promise for B2B since advertisers and publishers have control over the interest groups. But it will take time for B2B marketers to figure this out and publishers to adopt B2B topics.

As a final consideration, account identification relies on both third-party cookies and IP addresses to match an anonymous web visitor to the correct company where they work. This is a key capability of ABM platforms, and it remains to be seen what impact the end of third-party cookies will have on account match rates and accuracy.

I’ve seen almost no discussion of these topics with a B2B bent, which leads me to conclude that many B2B marketers will be caught somewhat unprepared by this important change. Figuring out the new way will require testing and tuning, so the sooner marketers start figuring it out, the better.

8. Innovative marketers will start tracking Qualified Buying Groups (QBGs) instead of MQLs

As we look towards 2024, buying groups will increasingly become the core of modern go-to-market strategies. As I’ve written, we’ve seen the limitations of lead-based marketing, particularly in how it disconnects marketing and sales efforts and inefficiently targets individuals who may not be decision-makers or even linked to target accounts. Account-based marketing, while addressing some of these challenges, also misses the mark by treating accounts too broadly, overlooking the diversity of buying scenarios within a single organization.

This is where buying groups shine. They represent a collection of individuals within an organization who are involved in the purchasing process. This approach acknowledges that B2B purchases are rarely made by individuals in isolation but by groups with varying roles and influences. While leads are too narrow and accounts are too broad, buying groups are just right.

Therefore, I predict companies will start talking about Qualified Buying Groups (QBGs) to replace Marketing Qualified Leads (MQLs) as the new benchmark in B2B marketing. (Perhaps there will be a different three-letter acronym, we’ll see.) Operationalizing buying groups remains challenging, however, requiring AI that can sift through millions of data points to automatically identify the right members. As a result, only the most cutting edge, innovative companies will fully embrace QBGs in 2024, with more companies adopting the strategy in 2025 and beyond.

Closing Thoughts

As we step back into the present from our journey to B2B marketing in 2024, it’s clear that the future holds both challenges and exciting opportunities. From the evolution of AI and the demise of third-party cookies to the rise of self-service buyers and the blurring lines between all the various “XLG” GTM motions, the landscape is shifting beneath our feet.

But remember, with great change comes great opportunity. B2B marketing isn’t just about adapting; it’s about pioneering new strategies, embracing innovation, and leading the charge into the future. The time to act is now. As you stand at the forefront of this transformative era, ask yourself: Are you ready to embrace these changes, or will you watch from the sidelines?

The future is in your hands – let’s make it remarkable!

The post B2B Marketing in 2024: 8 Trends That Are Changing the Game and What They Mean for Your Business appeared first on OpenView.

For someone who had spent seven years as a Product Manager (PM), confidently stepping into the world of entrepreneurship, this turn of events was nothing short of a rude awakening. I had assumed that my extensive background in product management at companies like Intercom would seamlessly translate into success for my startup.

I was wrong.

In my first three months building June, I discarded not one, not two, but 10 roadmaps. The same happened with user stories. Features were being developed at a good speed, often before I could even finish drafting user stories.

It was during this time that I began to question the applicability of traditional product management principles in the volatile and unpredictable early-stage startup environment. The notion that I had nurtured for seven years seemed to crumble in the face of these new challenges. So I embarked on a mission to understand what truly works in the world of early-stage product management.

In this article, I’m sharing with you the six commandments of product management in the pre-Product-Market-Fit (pre-PMF) phase. Looking back, I can say with confidence this adapting product management to pre-PMF is what helped us reach our PMF.

First, it helped us develop a muscle to ship fast and increase our output. We started to ship insanely fast and never missed publishing a weekly changelog since then. We also understood how to prioritize better and build what people wanted. A few rewards acknowledged that: we won the Golden Kitty Award in Data and became the fastest growing app on Twilio Segment in 2022. All of that with a team of 8. A year and a half later we reached our PMF (we wrote more here).

Throughout this journey, pre-PMF product management was our secret. It’s what accelerated us, but it would not have been without these six principles:

1. Unlearn traditional practices
2. Start with the right MVP
3. Prioritize insanely well
4. Move insanely fast
5. Set the right milestones
6. Collect learnings

Step 1. Unlearn traditional practices

Traditional product management doesn’t fit early-stage startups. These methods are designed for late-stage corporations, ensuring user-centricity as they expand. Yet, for startups, these approaches introduce unnecessary friction, slowing delivery and reducing impact.

When you’re starting your product and not yet hitting big success, concentrate on getting things done. Don’t spend too much time on complicated ideas. Concentrate on finding answers to problems. This is fundamentally different from the post-PMF phase, where the biggest risk is losing sight of your customers and misalignment between teams.

Let me be even more specific with some DOs and DON’Ts for effective product management in the pre-PMF:

DON’Ts

• Build a roadmap: Especially > 3 months. Your product changes too fast.
• Write user stories & epics: Abstraction will slow you down
• Do too much user research: Chances are you’ll get lost. Instead, get real.
• “Influence” people: If you have an opinion, say it loud and act on it.
• Set & forget: Building and releasing something is only the beginning.
• Listen to everyone. Find out who loves you and pick a niche.
• Don’t get stuck on strategy docs. Have a clear positioning and execute.

DOs

• Write problem statements. Make a prioritized list and only work on key customer problems.
• Get real and prototype live: Do everything you can to increase the delivery pace.
• Ship it: Just ship it, you’ll learn faster
• Pick big swings: “What’s the highest impact work we can do this week”?
• Empower people: Centralized decisions slow you down.
• Get feedback on releases: Most things require an iteration.
• Start a community: Before your product is ready, you can start a community.
• Talk to users every day: Keep sharing these learnings with your team.

Step 2. Start with the right MVP

The common idea about the MVP is that you create a small solution, give it to people, and see if they find it useful. But in most cases, this first version doesn’t quite match what people need. So, you start to wonder:

• Are you solving a real problem?
• Is your solution the right one?
• Are you aiming at the right group of people?

If you’re in this situation, you’re in a tough spot, and it’s hard to get out of it. Each of these questions has 10 different possible answers. That’s 10 x 10 x 10 = 1000 combinations you need to test to find PMF. This would take a lifetime!

To get a chance to succeed you need to ship an MVP that helps you collect some learnings. And for that, you need to be accurate. You need to ship an MVP as close as possible to what your product will eventually look like.

Successful startups often get it right from the beginning:

Notion and Uber MVPs look very familiar no?

Here are a couple of tips to launch the right MVP from the get-go:

• Be organized when you launch your first version.
• Trust your gut feeling.
• Solve a problem that you’ve personally experienced.
• Keep learning from your launches and be ready to change if necessary.

You’ve probably heard that you should launch fast and improve quickly. That’s good advice, but don’t rush through your first version. It’s really important.

Step 3. Prioritize insanely well

For any small business trying to create a new product, picking the right things to work on is super important. However, the regular methods for doing this don’t work well for startups. Those methods involve gathering feedback from people, sorting it into groups, and then using fancy systems like “RICE.” These ways are good for bigger companies but not for startups that are just starting out.

Startups in the early stages need to be quick. They have to find the most valuable things to work on and break through barriers, or they might fail. To do that, startups need to choose what’s most important in a different way.

Here are six excellent ways to pick the most crucial things to work on pre-PMF.

• Set one priority: Your resources are limited. Set one goal and align your initiatives to increase your chance of success
• Start with stones: Your planning is like a jar. Fill it with stones first (big bets), then add sands (low-hanging fruits). Avoid starting with sand.
• Aim for big swings. Pick ambitious bets. To do so you should ask yourself: “What’s the highest impact work we can do this week”?
• Reduce deadlines: Challenge your product priorities frequently. Adopt the shortest delivery cadence you can. Plan every 6 weeks instead of every quarter. Adopt one-week sprints, instead of two-week ones.
• Just in time: Define and estimate engineering work just in time. Estimate too much ahead – or too late – and you’ll waste resources. To solve that challenge sync your estimation and planning exercises.
• 10%-30% debt: Bugs and tech debt pile faster than you think. Dedicate a consistent part of your resources to them. Start with 10% of your resources, then grow this number over time.

Step 4. Move insanely fast

Doing things slowly in startups doesn’t make startups any better. It just makes everything take more time. Paul Graham, co-founder of Y Combinator, explains it well:

But going fast doesn’t mean making products in a hurry or putting out things that don’t matter. It means being smart and efficient when creating and releasing your product. It also means taking risks and working differently from the masses. You can’t be better and catch up if you use the same recipe.

Below are unique ways to give your users what they need as quickly as a flash of lightning.

• No staging: Any steps between the dev and production environment are a way to hold back (and here is how to survive without staging).
• Adopt feature flags. A feature flag is great to add users to a beta and iterate with them. You can do it yourself for QA, and you’re one click away from going live for everyone.
• Scope small. Think big, start small. It’s faster to break down a project into parts and ship them than to ship a project all at once.
• Write regular product releases. Publishing a changelog at a regular cadence is a forcing function to ship fast. A weekly changelog is like a superpower.
• QA immediately. When engineers wait for QA, they have to switch contexts. Context switching is a huge waste of time. QA right away.
• Create engineering ownership. Make it clear to engineers that they are responsible for solving the users’ problems. End to end. Avoid dragging.

Step 5. Set the right milestones

PMF isn’t binary, it’s a scale.

First, you have nothing, then some level of PMF, then a strong PMF. Many success stories will tell you how a startup moved from nothing to having a strong PMF. But that’s usually for the sake of making the story snappy and remarkable. Most of the time, it took years for a startup to reach its PMF.

Once you understand PMF is a spectrum, you understand the value of setting gradual goals, and progressively hitting them, until you reach a strong PMF.

Here are the milestones you should set yourself pre-revenue:

Qualitative milestones

Nothing

• You get < 10% acceptance rate in your user research requests

Some sort of PMF

• You get > 20% acceptance rate in your user research requests
• People give you money for using your early product
• Customers share a list with you of what would it take to adopt your product on the company level
• Customers react sensitively when you have a sudden downtime

Strong PMF

• 40% of your users would be very disappointed if they couldn’t use your product anymore
• Organic inbound (word of mouth) picks up

Tools suggested: Gmail, Webflow, Tally

Quantitative milestones (more benchmarks here)

Nothing

• Your website conversion rate is < 1%
• Less than 50 active users
• User retention below 20% at 6 months

Some sort of PMF

• Your website conversion rate is > 2%
• B2B: 50 active accounts; B2C 500 WAU or DAU (depending on your desired frequency)
• Active users growing 3% week over week (WoW)
• User retention between 20% and 40% at 6 months

Strong PMF

• Active users growing 5-10% WoW
• User retention > 40% at 6 months
• 10k+ MRR

Tools suggested: June, Metabase

Step 6. Collect learnings

Pre-PMF, the smallest unit of product value that product management can generate is not signup, a piece of customer feedback, or a non-repeatable revenue. It’s a learning. 

A learning can be anything that has a meaningful impact on your business, and how you run it. A learning can be:

• A value proposition that resonates more than others.
• A persona who couldn’t care less about your product.
• An acquisition channel where it seems most of your sign-ups came from.
• The technology you previously used is going to be discontinued.
• New technology that you may be leveraging in public beta.

Over time, whoever generates the most learnings and is able to act upon them, wins. Product management should help you collect as many learnings as possible, and then circulate them to make wise decisions.

Here are a couple of learnings that drastically impacted businesses you may know:

Conclusion

Product Management pre-PMF is broken, but it does not have to be. If you’re working at an early-stage startup, you can fix that.

Unlearn traditional practices, focus on solving real problems, and start with a precise MVP.

After that prioritize effectively, move swiftly, and set achievable goals through your journey. To make this journey efficient, collect insights and act accordingly.

Altogether these practices will make you move faster than anyone and more importantly, in the right direction. They will help you leverage product management to increase your chance of reaching PMF.

Last but not least: enjoy the ride. Product management in the early-stage is an incredible experience to live.

Hope this helps

Enzo

 pssss! I’m writing a book called “The first Product Manager”

It’s about product management in early-stage startups. To be on the VIP list and receive the first chapter for free when it’s out please drop your email here.

The post The Pre-PMF Guide to Product Management: How to Move Faster and Stop Throwing Away Your Roadmaps appeared first on OpenView.

Here’s what we found:

1. Folks are changing both pricing and packaging.

Most of those changes – even in this market – involve price increases. According to our data, half of all SaaS companies surveyed changed pricing and packaging, while a quarter changed pricing only, and just three percent changed packaging only.

A mere 22 percent of companies surveyed changed neither pricing nor packaging. But with persistent inflation, I’d bet we continue to see more price increases well into the new year.

Several notable SaaS companies also changed up pricing this year, including:

And even non-traditional software products are getting in on the action. Mercedes-Benz added a subscription fee for faster acceleration (topping out at up to $1,200 a year) and BMW added, then dropped, a monthly charge for heated seats – $18 a month.

If you’re mulling a pricing change in the next year, we recommend reading the following pricing resources first:

• SaaS Packaging 201: 9 Advanced Lessons for Better SaaS Packaging
• The 5 SaaS Pricing Mistakes You’re Probably Making (And How to Avoid Them)

2. SaaS companies continue to embrace more complex, hybrid pricing models.

This year, we’re continuing to see more companies embrace usage-based pricing. If you’re not familiar, usage-based pricing (UBP), also known as consumption-based pricing, enables customers to pay for a product according to how much they use it. Usage corresponds to how the customer extracts value from your product. As seen in this year’s survey data, UBP is becoming increasingly prevalent within SaaS, even replacing more traditional subscription- and seat-based pricing models.

But usage-based pricing models exist on a spectrum. Increasingly, there are more hybrid models that combine usage-based pricing with traditional subscriptions in novel and creative ways.

• Autodesk introduced Flex, a pay-as-you-go offering for occasional product use. This is a great way to get folks to try out the broad suite of Autodesk products with minimal commitment.
• GitHub charges on a per-user and subscription basis. But they bake in usage of newer products like Actions, Packages, and Codespaces. Customers pay more as they use more.

According to this year’s survey respondents:

• 23% have usage-based subscription tiers
• 18% have a largely usage-based model
• 17% have tested usage-based pricing
• 42% have no usage-based pricing

3. Folks are bringing pricing in-house.

In addition to the pricing changes mentioned above, companies are bringing pricing in-house as a function owned by internal team members. Just six percent of respondents this year said they hired a third party to help with pricing changes.

Early stage companies typically changed pricing based on a quick internal analysis lasting less than four weeks. Later stage businesses, meanwhile, did a more robust internal analysis to account for greater pricing complexity and a larger install base of customers.

4. PLG companies are starting to localize pricing for different markets.

Nearly 60 percent of respondents to this year’s survey said they make some effort to localize their pricing. This is true more so of Europe-based respondents relative to their American peers. Looking at pricing specifically, best-in-class SaaS companies adapt pricing geographically based on local willingness-to-pay, foreign exchange, and operating costs.

5. We’re in the early innings of AI monetization.

As with most things AI-related, it’s still early innings. And that’s true for AI monetization as well. While more companies are developing standalone or sidecar AI products, fewer have figured out how to monetize those additions.

• 76% of SaaS companies either launched or plan to launch AI features
• Only 15% have monetized AI (think: Notion, Intercom, Microsoft)
• Given the costs of enabling AI, expect to see more monetization testing in 2024

It will be fascinating to see how pricing evolves next year – what pricing or packaging changes do you have in store for 2024?

For more data from this year’s SaaS Benchmarks report, you can access the full findings here.

The post What’s going on with pricing this year? A deep dive into 2023 pricing data. appeared first on OpenView.

That said, achieving true product-led growth takes a winning combination of free parts of your product, virality, paying users, and more. Startups spend years (and thousands of dollars) trying to figure out the right model for viral growth – and many never do.

That’s why we’re using this post to share tested tenants for product-led growth and the charts, reports, and analysis you can use to refine your growth strategy. If you’ve ever wanted to answer: can we adopt a PLG strategy, what would we have to do to make it work, or why isn’t our PLG strategy growing our business, this is it.

Overview

At June we’ve been lucky enough to see a bunch of amazing PLG startups scale and grow their product usage too. Seeing them gave us some unique insights into some of the patterns that made them successful.

Let’s dig in. First, let’s get on the same page about what makes a successful PLG business. These are our strong opinions, take them for what you will.

We believe that to be effective at PLG:

1. Your product must have a free component that effectively acquires users.
2. Your free plan must attract other users to it.
3. Your free plan must attract users who will pay you.
4. Your paid plan needs a gross margin of 80-90%.

These strategies are an expansion of Christoph Janz’s Three Rules of Freemium with our own additions around why a PLG company needs a free plan and what has to happen for it to work effectively.

Here’s a quick look at what we’ll work through in this post:

Your product must have a free component that effectively acquires users

Product-led growth is defined by (literally) a product’s ability to drive growth. When your product is doing your marketing, you grow when more people experience it. It follows then that a free component is the most effective way to get many people into your product. Whether it’s a free trial, reverse trial, or free plan, a PLG company needs a free component.

If your product can’t acquire customers and convert them on its own, you’ll have to rely on a marketing team. That’s okay, you’re just not suited for pure PLG. Instead, consider hiring a sales team and creating inbound marketing channels.

The free component of your product must also effectively bring in new people and those people must actually use your product; otherwise, they’re just sign-ups on an email list.

Free Sign Up

It’s quite straightforward to measure acquisition. You’ll look at your website to sign up conversion rate. We’ll also flag that it can be worth defining sign ups as someone who actually creates an account, not just enters their email, since there’s generally a larger drop off than expected.

Your website to sign up conversion rate should be between or above 3 to 6%. Anything below 2% is a problem. There are a few key players in the space that convert 18% of visitors to sign ups which is likely a function of stunning products, good pricing, and a viral component.

You’ll also want to look at the raw number of sign ups each month to get a sense of basic business growth.

This can help you predict how you’ll grow down funnel.

Activation

We’ve noted that to be an effective free plan, users must actually use and engage with your product. This means that your free plan must effectively activate and retain new sign ups. Put simply, it must turn sign ups into users.

(Note that we didn’t say “customers” as that would imply someone who pays you).

It’s our opinion that conversion from sign up to actual user happens when your free component provides value and is also self-serve.

Creating Value

Often, startups put their most valuable features behind a paywall because, hey, they just spent two quarters and half a million dollars on those shiny new features. But if you’re PLG you don’t have strong marketing channels. Your free plan is your marketing. And if your free plan doesn’t expose your value, it doesn’t exist to your users. If your users don’t see value, they don’t buy.

Make it Self-Serve

Not many people will tell you that your PLG product needs to be self-serve, but it’s our stance. Here’s why: If your product needs multiple people for set up and implementation before it creates value (for example, it’s a marketing tool that needs engineering implementation, or it’s an analytics tool that needs multiple team approvals) it’s pretty hard to create organic growth because the people you market to aren’t the only people who need to believe and/or pay. That internal collaboration also creates a longer sales cycle here that hinders a viral spark.

Activation is not the same as engagement. Free users might engage with your features, but if they’re not activating through your core functionality, they won’t get your product, why it’s valuable, or – most importantly – why they should pay. That’s why activation is a measure of people who hit your Aha! moment.

So how do you define the Aha! moment?

You’ll start with a hypothesis. For example, sometimes your Aha! moment is tied to a magic number. The most famous magic number is probably Facebook’s “7 friends in 10 days” idea. Facebook realized that if their users added 7 friends within their first 10 days on the platform, they were significantly more likely to retain (aka activate) that person.

You can start to explore your data by comparing user activities across cohorts. For example, here’s a simplified version of how Facebook could have done it:

We also measure activation as a percentage. Activation is a threshold, it’s the percentage of users that signed up to your product. Thus you have your Activation Rate. When you understand your activation, you understand if your product is in a healthy state to turn sign ups into long-lasting users of your product. If your free plan doesn’t activate sign ups, you’re going to have a hard time growing.

So what’s a good activation rate? According to Lenny’s Newsletter:

• The average activation rate is 34%, and the median activation rate is 25%.
• For just SaaS products (removing marketplaces, eCommerce, and DTC), the average activation rate is 36%, and the median is 30%.

We believe that activation below 20% makes it hard to build a business. If you’re B2B, your conversion rate should be even higher since you likely have fewer people entering your funnel.

You’ll also want to look at how quickly someone activates.

When it comes to activation, timing is also an important consideration. For SaaS you’ll want to make sure someone is activating in 1 to 3 days. For B2B businesses that might look more like 2 to 3 weeks.

*A note on retention and active users

There are two other agreed-upon ways to measure the success of your product activation: active users and user retention. Simply put, when a user activates, they are active. If that user stays over time, they are considered retained. For that reason measuring the number of active users (whether it’s daily, weekly or monthly) as well as user retention over time is yet another good measure of product activation.

Lenny also provides a few benchmarks on standard retention rates. But, in general, if you’re not a consumer social app, retention should be above 50%.

Virality

You also need a free plan that can acquire other users. Again, PLG uses product, not marketing, to drive acquisition so you need a component of your free product that people will share and one that will convince others that they need your product too. Whether this is through calendar invites (Calendly), branded shortlinks (Buffer), or internal company expansion (Slack), you must make it easy for your users to share your product (because you’re not building any other paid avenues).

Luckily you’ve got options. Your free plan can be shared in two ways: virality or network effects.

“A truly viral product has intrinsic virality, meaning that it’s designed around something that naturally involves other people, things that create two-sided benefit…The network effect, on the other hand, is about value; specifically compounding value. When a customer who has already joined, gains incremental benefit from the latest person who became a customer, you have network effects.”

– Oji Udezue, former CPO of Calendly as told to OpenView.

For example, Slack isn’t necessarily built for virality because it can’t intrinsically jump from organization to organization. Calendly is viral because calendar invites by their very nature include more than one person.

Both can work for PLG as long as your K-Factor is high enough. A K-Factor measures how many people are told about your product by one user, and therefore acquired for free.

K = i * c
i= The number of app invites sent per customer
c= The average conversion rate of each invite

To be honest, not many software companies sit down to determine their K-Factor, but it’s important to consider what part of your product can be shared on the free plan – especially as you gate and keep certain features free. Think critically about how exactly you want to enable your free users to share your product with others.

Can you create virality or are you relying on network effects? How can you incentivize both of these actions?

Authory, an automatic multimedia portfolio builder, ignites the network effect by offering two additional weeks on a free trial if someone shares their app on social media. And if you search for Authory on X/Twitter, you can see just how well this incentive is working.

Another measure of shareability is measuring your top-of-funnel growth. This might look at measuring growth in active users per week.

Growth in active users is often key to a startup’s success. Typically,  10% week-over-week growth is world-class growth and 5-7% is great.

From our end, we believe that active user growth rate is a reliable North Star metric for PLG companies. It tells you if you’re bringing in enough users and if you’re bringing them in effectively. Think of this as the PQL (product-qualified lead) of PLG.

Free to paid conversion

Of course, sign ups and activation might be leading indicators, but if these customers don’t pay you, you’ll fail.

This is a fairly straightforward metric in that you want to track the number and ratio of people who are converting to paid plans. That conversion, however, can happen many ways: in a trial, a reverse trial, or freemium experience.

Generally, 5% of all freemium sign ups convert to paid on average based on OpenView’s benchmarks.

Of course, it’s nice to beat averages, but the key is that you’re seeing conversion from free to paid.

Here’s a clear chart on what’s standard depending on your model.

What’s right for your business is highly specific but do consider your conversion rates and full funnel as you choose.

So what if your conversion rate isn’t great? Your free plan must attract an audience that will convert. In a non-PLG company, if leads aren’t converting, there’s often a positioning problem. There’s a dissonance between who comes in and what happens in the product. In a PLG company, this means looking at what part of the product is primed for sharing and who it’s shared to. Make sure your product can penetrate the communities where your audience hangs out to make sure you have well-qualified sign ups coming in.

Retention

And finally, your paid plan needs a large gross margin. As Christoph Janz says:

“If you have a lower gross margin – for example, because your product is not fully self-service, requires extensive customer support, or is extremely costly in terms of tech infrastructure – freemium will probably not work for you.”

We’d say your paid plan must cover the cost of marketing and product development. We’d highly push for a self-serve component of your product to reduce these costs and make pricing and conversion to pay more accessible. If your paid plan doesn’t meet these criteria, it’s likely that your free users are going to cost you too much, and your business won’t be viable.

This number is a financial metric more than a product metric so we won’t go into detail on it, but it’s expected that as a business you know your costs, revenue, and growth over time.

We’ll also bring back our retention chart from above.

According to Casey Winters,

“Retention is not only the primary measure of product value and product/market fit for most businesses; it is also the biggest driver of monetization and acquisition as well.”

Not only is retention the flip side of activation but it’s tied up with LTV. If you’re retaining people longer, all of a sudden each customer is worth more and your revenue projections go up.

Summary of Analysis

To recap, we’ve just walked through five core reports we see as essential to product-led growth:

1. Sign up to free
2. Activation
3. Virality
4. Free to paid conversion
5. Retention

If you’re looking at all of these, your dashboard might look something like:

Why these reports?

It’s our opinion that to thrive as a PLG business you need to get three out of five of these core pieces right.

For example, if you sign up to conversion rates are down, you can make up for this in your funnel by converting many of your free users. This is key if you have a small audience, a technical product, or a complicated one.

Or you might have a wide top-of-funnel and only convert a few customers. Looking at your total addressable product and simply understanding how your product fits within the market is going to help you determine what’s right for you.

These reports also represent leading, not lagging, indicators for growth. Notice that we’re not looking at revenue, monthly recurring revenue, or churn. By the time you see the impact of strategic decisions in these financially-focused numbers, it’s quite late. Instead, we’re empirically looking at what happens at the start of your customer lifecycle. This helps to incentivize activation over quick revenue, which centers your team on better experiences not just short-term revenue gains.

Final Thoughts

In summary, for an effective PLG strategy your product must have a free plan that acquires new sign ups and activates them, a free plan that acquires other users at scale, an ability to convert free users to paid, and the ability to retain these customers for growth.

This is best measured through your sign up to free conversion rates, activation rates, your ability to drive viral growth, your free to paid conversion rates, and your retention rates.

You can learn more about product-led growth here.

The post The Definitive Guide: Product Analytics for Product-Led Growth appeared first on OpenView.

Companies looking to execute an ABM campaign have their priorities upside down. The promise of ABM has always been better sales alignment and innovation to drive insights about who marketers should target, in turn arming sales teams with the information they need to close deals.

Instead, we got a lot of display targeting technology that goes after accounts and not even the right people in those accounts. Around 70% of companies I meet with to discuss ABM start their process backwards. It usually goes something like this: They gather a list of target accounts from their sales team, buy an ABM software platform, and target those accounts and then high-five themselves any time an account that’s seen an impression ends up converting.

But here’s where these companies have gone wrong. They’re starting with sales NOT marketing. Marketing has the data and insights to know which accounts ABM should be targeting – not sales. Sales might know who they’d like to convert, but marketing has the data to best understand which accounts will actually convert and how quickly as they make their way through the funnel.

Sales & Marketing Integration

Given the nature of ABM and its focus on driving high ACV accounts, marketing’s role shifts from being a sales support team that just provides enablement material to a strategic partner that is accountable for pipeline creation. Marketing is now focused on informing sales about what accounts they should be going after as well as providing intelligence on those accounts to enable better conversations.

Marketing has the technical skills to assess the ICP, define the digital profile of that ICP, and build a model to identify target accounts. In other words, marketing is best qualified to lead this effort.

Still, most companies end up missing their ABM-related goals because:

• Their ICP is too broad. Instead, companies should focus on slimming this down as much as possible. In ABM focus wins.
• They lack a repeatable model for identifying target accounts.

Marketing and operations teams should work together to analyze the data and identify the proper accounts. The goal here is to take emotion out of it. A good ABM program relies on objectivity. In other words, focus on demographic, firmographic, and technographic data and then events that signal different levels of intent, and not on who you “think” might convert.

Demand Creation

It’s marketing’s job to drive interest in what you’re selling by driving engagement on your site. Actions like a demo request or free trial start may signal high intent while events including a visit to the pricing page or product pages may signal interest. In ABM, marketers want to create a better pathway for these potential buyers to talk to sales. The ultimate goal shouldn’t be ad impressions, it should be hand-raisers. When the goal is to generate hand-raisers, it’s likely that marketing will create programs that generate higher rates of recall. Smarter goals mean better outcomes.

But marketers shouldn’t just stop at a hand raise. While many companies think driving demo requests is an inbound motion, we reject this notion. Marketers should look to drive the accounts as far down the funnel as possible.

Once target accounts have been identified and your CRM is set up to inform sales, it’s time to measure the impact of your work. You’ll need to report on engagement, qualified accounts, pipeline generated and revenue driven.

Marketing also needs to pass along relevant account information to sales including company size, company revenue, as well as characteristics that will help set your reps up for a good first call – that’s how you’ll identify the proverbial low hanging fruit. It’s here that many companies skip to display ads. Direct outreach is going to drive more meetings and is a better bang for your buck in the beginning.

Also keep these lower cost options in mind:

• Co-marketing: Co-marketing with complementary companies – does their customer page feature logos that would be a good fit for your product? I like co-marketing because it’s a high engagement channel, but also, it’s an opportunity to align your brand with another strong, potentially more established business.
• Network: Leverage your company’s network – it’s likely your sales team has sold to similar audiences, and it’s also likely people at your company know people in your ICP. This includes leveraging organic LinkedIn engagement.
• Leverage your database: Many companies overlook the already existing accounts in their database in favor of bringing in new accounts. Identify ways to move existing accounts through the funnel while also bringing in new accounts.
• Drive accounts to high engagement content from paid social: Don’t advertise to a new audience with a demo offer. Instead, introduce yourself first. Start to build demand by presenting the problem and how you solve it.

Awareness

The cautionary tale here is about targeting. Yes, it’s easy to find programmatic display options that are great at targeting accounts with impressions, but are they targeting the right people? Focusing on impressions to drive “awareness” won’t create the desired effect. Someone who clicks on your ad, or views your content over and over is much more likely to remember you than if they’ve simply seen your ads.

Also, you need to ensure display impressions are targeting the right audience. Just because an “account” gets an impression doesn’t mean that those impressions are getting in front of the right buyer. This is the challenge with many display vendors.

Getting Started with ABM

Marketing’s job in an ABM GTM strategy is to surface the accounts that are most worthy of investment based on real data and intent signals. Let’s apply it now to your own business:

1. Narrow Your Target: Identify the characteristics of accounts that are most likely to produce by analyzing your company’s sales data. We use a number of metrics to build a model including:

• Customer lifetime value
• Average deal size
• Conversion rates
• Deal velocity

2. Define Your Digital ICP: Next, enrich and prospect data against these predetermined criteria to build a list of named accounts to deploy your programmatic ABM efforts against.

3. Streamline the Buying Process: Each programmatic campaign is then segmented by persona and buyer journey stage – nurturing the entire buying committee at enterprise-level accounts toward purchase.

4. Drive Engagement: Account engagement with programmatic efforts serves as a barometer for intent – they’re a great fit AND show readiness to purchase. Surface these accounts from marketing to sales.

5. Drive Pipeline: Finally, sales and marketing bring it over the finish line together – increase your investment and switch to 1:1 and 1:few ABM tactics for the win.

Measuring Success

First, let’s be honest, marketing’s role is to build interest and demand in your product. So the easiest KPI is looking at hand-raisers. How many people are proactively coming to the site to say they would like to talk to the sales team or sign up for a trial within your target account list?

When you set this as the bar, the goal from marketing moves from impression to high-quality lead. Inevitably, you’ll build marketing campaigns that drive high recall even for the leads that don’t come in and fill out a demo form.

While we don’t believe an impression should constitute a KPI for ABM, we do think engagement matters to drive recall. Recall will make it that much more likely for target accounts to engage with outbound email, InMail, meeting requests and other outbound efforts. You should also take into account brand touchpoints along the way, with a focus on high intent engagement like case study engagement, pricing page visits, event attendance, and other similar actions.

ABM: Strategy Not Tactic

It’s not easy to know where to start with ABM, and maybe that’s because ABM itself is nebulous and going to look somewhat different at every company and for every product. But to make it work, teams need to look at it as a strategy, not just a tactic. Taking a holistic approach, with marketing leading and sales following, is the key to ABM success.

The post You’re Doing ABM Backwards. Here’s Why & How to Fix It. appeared first on OpenView.

Eraser — a documents and diagrams tool for engineering teams that I founded in November 2020 — has grown rapidly, with sign-ups scaling by 20x in the last year.

We focus on serving developers with targeted features like diagram-as-code and GitHub syncing being central to the product. And we have a track record of trying less-explored growth strategies such as virtual office partnerships and an ungated product experience.

In May 2023, we released a sidecar product called DiagramGPT, an AI tool that can generate diagrams from natural language or code snippets. I wrote this post because I wanted to share our experience with other software teams that have built internal LLM-powered prototypes but are struggling to figure out next steps. It’s a case study reflecting on what our team did and learned, and ultimately why your team may also want to build an AI sidecar product.

According to OpenView’s forthcoming 2023 SaaS benchmarks report, 46% of SaaS companies launched AI features over the last year and another 31% are actively building or testing AI features. In other words, more than 3 out of 4 SaaS companies have had or currently have AI R&D initiatives.

However, the bar to integrate anything into an existing mature product is usually very high. Two resulting failure modes are:

• The AI prototype loses steam going through endless iterations
• A significantly scoped down version gets shipped to test the waters but fails to generate excitement from customers

As we show in the example of DiagramGPT, there is another path forward. A sidecar product can be a mechanism to deliver a bold, undiluted vision of an AI feature to users quickly. It will generate qualitative and quantitative feedback that can help bring the AI feature into the main product. And of course, if it grows legs, the sidecar product can become a reliable organic user acquisition loop. DiagramGPT accounts for 30% of sign-ups for Eraser today.

Building DiagramGPT

When we got our hands on GPT-4 like the rest of the world in the Spring 2023, it was soon clear that LLMs were finally smart enough to generate diagrams. We were able to input natural language or code snippets and output beautiful diagrams. The early results were compelling enough that I decided to fly to Boston for a week to collaborate with our Founding Engineer, Yoel Tadmor, and kick off a new AI project.

On the first day, we discussed whether the generative diagramming functionality should be a product feature or its own sidecar product. We quickly settled on the sidecar product because we did not want to burden it with the craftsmanship that goes into building features in our core product. We wanted to find a footing in the quickly shifting LLM landscape, and the best way would be to ship something as soon as possible.

Early mockup of DiagramGPT, drawn using Eraser

Despite focusing on speed, we still made sure to create moments of delight for the user. For example, instead of just showing a spinner while the diagram is generated, we created a streaming experience where interim outputs are rendered frame by frame. This made the waiting less dull for the user and the product experience more dynamic.

Streamed diagram generation makes the waiting fun!

Another explicit decision was to create a separate brand identity called DiagramGPT for the sidecar product. By not calling it Eraser AI, we gave the sidecar product enough distance from our main brand such that we’d have no problem moving on from it if it flopped. We also built DiagramGPT in dark mode despite our then light mode website and even crafted a slightly different logo.

DiagramGPT with a different brand identity than the rest of Eraser

Launching DiagramGPT

We treated launching DiagramGPT like a product launch. We put a date on the calendar and coordinated engineering and marketing prep against that date. We invited friendly users to play around with it in advance and recorded a scripted demo video. As the launch neared, we had enough internal conviction in DiagramGPT that we knew that we wanted to spend some more time polishing the UX. As a result, we ended up pushing back the launch a few days to improve the streaming experience and diagram layouts.

One of the final decisions was what the call-to-action (CTA) would be. DiagramGPT was an un-gated sidecar product that lived on the marketing website. If DiagramGPT was successful, what business impact did we want it to drive?

The obvious option would be to nudge users to create an Eraser account. However, that didn’t feel right because the bigger opportunity seemed to be in user discovery as we explored the nascent problem space of AI diagramming. Learning first, and growth second. So instead, we made “Let’s chat” / “Request early access” the main CTA.

On launch day, we posted our demo video to all social channels. It was well received on Twitter, especially considering our small following of <1k followers. Leveraging that social proof, we pitched DiagramGPT to the growing number of popular AI newsletters. Several large newsletters picked up DiagramGPT on their next issue, which ended up creating a bigger impact than the original social posts.

Our Twitter post created the initial splash but the newsletter features had a bigger impact

After the Launch

We were glad to have gone with call scheduling as the CTA as we conducted over 40 user discovery calls in the following month scheduled from DiagramGPT. Not every call was packed with insights of course. But, it allowed us to survey a broad cross-section of users interested in AI diagramming ranging from PhD students to software architects at large enterprises. The conversations also gave us conviction to build and ship an API version of DiagramGPT.

DiagramGPT also provided usage data with a large enough sample size that allowed us to build a better AI diagramming feature into the core product. For example, we observed that about half of the generation attempts were actually re-tries or slight tweaks of the original prompts. This told us that iterating on diagrams with “AI edits” would be important and made sure to ship that feature in our core product.

The AI diagram feature that ultimately was shipped to the core Eraser product

DiagramGPT ultimately delivered on sign-ups as well. After the initial spike from the launch and newsletter features, it wasn’t immediately obvious that the incoming traffic would be sustained. But to our surprise, traffic actually steadily grew mainly via word of mouth. Unlike text or image generation, diagram generation is a less crowded space. Simply being the first with a compelling demo was actually enough to draw sustained interest.

In order to encourage visitors to sign up for Eraser, we gated diagram editing behind a sign-up wall. You’d be able to generate a diagram, but not edit it without signing up. Since AI generations are rarely perfect as is, we observed that serious users with work use cases would often sign up.

DiagramGPT grew to eventually account for ~30% of Eraser sign-ups. As usage scaled, so did cost to serve and we ultimately introduced a limit on how many diagrams could be generated before creating an Eraser account.

DiagramGPT gradually compounding sign-ups

Reasons to build an AI sidecar product

#1. Create a sustainable and organic user acquisition channel.

If successful, your sidecar can become a reliable, zero-to-low-cost user acquisition channel that compounds on its own. It’s a way to place your shiniest feature outside of the sign-up wall or paywall so that even low intent users can experience your product with minimal friction.

In our case, the launch marketing and the newsletter mentions seeded initial interest in DiagramGPT. Afterwards, it grew via word-of-mouth without much additional effort from our team. The growing public interest to find and adopt AI tools likely worked in our favor. Paid marketing for the sidecar product did not make sense for us and likely doesn’t for most companies unless conversion is highly efficient.

#2. Validate demand for AI features.

Your AI feature could either be a game changer or a massive waste of time and it’s hard to know which it will be without trying. AI sidecar products are a way to quickly test demand without committing to invasive changes to the core product. You can run bold experiments because they are easy to discard if they don’t wok.

#3. Leverage usage data for product development.

Building AI features today is hard in part because there aren’t established design patterns to rely on. For example, should your specialized AI feature have a chat interface or a non-chat interface? Referencing peers or competitors won’t help because they are likely still figuring it out too. However, with sufficient traction, and if you have time to wait, the sidecar product can generate usage data that will steer your decisions.

Reasons not to build an AI sidecar product

While our experience at Eraser has been positive, an AI sidecar product isn’t for everyone. A few areas to consider.

#1. It could be a source of distraction.

Unlike other growth projects, it will require real engineering and design time, probably equivalent to building a medium-sized feature. Would you rather ship a core product feature or ship the sidecar product? That said, AI sidecar products are often more lightweight since most software teams have already spent the time in the last 12 months (see OpenView survey data mentioned above) to build internal R&D prototypes. For those teams, the marginal investment to ship the sidecar will be minimal.

#2. It could cannibalize usage of your core product.

If you give too much value away in the un-gated sidecar product, users will have less reason to sign up for your core product. In Eraser’s case, if DiagramGPT were generating perfect diagrams every single time, we could end up with a lot of DiagramGPT users who don’t bother to sign up for Eraser. The ideal sidecar product experience should deliver concrete value but leave the user craving for more.

#3. LLM usage costs could balloon depending on your LLM stack (OSS vs. OpenAI, GPT-3.5 vs. GPT-4, etc.).

This is a gotcha since software teams usually don’t need to think about the infra cost of a marketing site. But it will be worthwhile to confirm that the unit economics pencil out for your AI sidecar product. Does your sign-up conversion, paid conversion, and LTV justify the LLM infra cost involved?

The post How an AI sidecar product drove 30% of sign-ups: Eraser’s founder on building and growing DiagramGPT appeared first on OpenView.

In this guide we’ll focus on the nuances of initiating paid advertising for early-stage B2B startups. We’ll look at setting an adequate budget and the need for cross-functional support encompassing design, content, engineering, security, and legal considerations. We’ll explore the essential tools, including web analytics, UTM parameters, platform integrations, and approaches like remarketing and lookalike audiences. For goal setting, we’ll touch on the importance of experimentation and using industry benchmarks as guiding metrics. We’ll also touch on understanding and calculating Return on Ad Spend (ROAS) as a metric for scaling the advertising program.

My experience with paid advertising is diverse, ranging from managing the initial campaigns for early-stage startups on a tight budget to overseeing mature demand generation teams with annual budgets in the millions. Those two scenarios are significantly different. In this article, I’ll concentrate on the early stages of paid advertising, highlighting essential components to ensure efficient use of limited resources.

If you plan on using an external agency for your digital advertising efforts, this article has plenty of insights that will be helpful. While an agency can do a lot of the heavy lifting for you, they are not a turn-key solution. I would view an agency as a partner in an effort that requires ongoing input and feedback to be effective.

Lastly, a disclaimer: If you have any reservations about running paid ads to developers, get over it! A common misconception is that developers hate ads and advertising to them doesn’t work. Simply put: EVERYONE hates useless/deceptive/abusive ads. EVERYONE hates being bombarded with spam. The quality of your campaigns will be the primary gauge to the success of your efforts. With that out of the way, let’s dive right in!

Dependencies

Budget

If you’re running ads for the first time, your goal is to learn, iterate, and scale up where the opportunity is the greatest. While you shouldn’t splurge and waste money, you will need a sufficient budget to see results in a timely manner.

$5-10k per month across 2-3 channels is a reasonable budget at the start. This should give you enough room to try different audience targeting options and ad content while providing the volume of traffic for statistically significant results. This budget range is also in line with the monthly minimums that some smaller agencies will require to support your paid campaigns.

Cross-functional support

Like a lot of efforts in marketing, paid campaigns are a team sport. Design and content are at the core of your ads, and in most cases these are the responsibilities of two different people. Assuming you’re doing the content, you’ll need to either leverage existing assets or require design support to create new assets.

In order to monitor and optimize your campaigns, you’ll likely need engineering support to build new landing pages, integrate analytics and send key events from your website or product to the advertising platforms (more on that later).

Don’t overlook the security and legal considerations. Any new tracking tool installed on your website or in your product creates a potential security vulnerability, albeit in most cases the risk is very low. Conducting a security review gives you one less thing to worry about.

Some platforms allow you to upload first-party PII data so you can target your existing customers or find look-a-like audiences. While this is a very effective approach, you’ll want a legal review of the platform’s data sharing policies to make sure you are operating within the bounds of regional privacy regulations and your own terms of service.

Tools of the Trade

Web analytics

All major advertising platforms provide analytics to monitor campaign performance on their platforms. Since your goal is to bring visitors to your website or product, you’ll need to be able to analyze user behavior off of these platforms as well. Most early stage companies have analytics tools instrumented within the product early on to help improve product usability. Often this translates to the same analytics tool being used on the website.

Unlike most product analytics tools, web-specific tools primarily focus on how visitors find and interact with your website, highlighting overall trends and include demographics data about your audience; all critical information to help you monitor and improve your paid campaigns. If you don’t have a web-analytics tool installed on your website, use Google Analytics; it’s free, it’s standard, and it will give you all of the insights you need to monitor and improve your campaigns.

This example shows the default Landing Pages report in the latest version of Google Analytics (GA4). By default, GA4 provides detailed information on user behavior based on page location and where they came from (source/medium). Since Conversions and Total revenue metrics are unique to each business, these metrics need to be manually configured within your account.

UTM parameters

Almost all analytics platforms will capture the source and medium of your traffic. For example, if you’re running an ad on LinkedIn, by default, the source/medium will show up as linkedin/cpc. If you’re running multiple ads on the same platform, you’ll want to use UTM parameters on the links in your ads. Here’s an example of a link with UTM parameters (the part in bold):

https://your-website.com?utm_source=linkedin&utm_medium=cpc&utm_campaign=winter-hackathon

With the UTM parameters above, you’d be able to separate the traffic by the campaign name, while still being able to group all of your paid LinkedIn efforts under linkedin/cpc. This will be very helpful for reporting. While UTM parameters allow for additional values beyond source, medium and campaign name, these three parameters are required and, in most cases, is all that you need. Google URL Builder provides a simple interface for adding UTM parameters to your links.

Maximizing ROI

Remarketing is a paid advertising strategy that targets visitors who have visited your website, and serves them your ads on other sites. Remarketing is less expensive than display ads, making it a cost effective strategy for companies of all sizes and stages.

Generally, remarketing is enabled by embedding a third-party tracking script or pixel provided by the advertising platform. Each advertising platform will have detailed instructions on how to implement this.

In general, embedding third-party tracking scripts requires code changes. While trivial for an engineer, for non-engineers this introduces an external dependency. In many cases, leveraging a tag management solution like Google Tag Manager simplifies the process of embedding and managing multiple tracking scripts on a website without requiring code changes for each implementation, meaning you won’t need an engineer’s help everytime you want to add a new third-party tracking script. Initially, adding Google Tag Manager to your website will require engineering support, but it’s a one time effort that is well worth it in the long run.

To further optimize your advertising spend, you’ll want to send conversion events to the associated platform once a visitor has taken a desired action. This is worth some additional detail on how advertising platforms target users.

When you define the audience you want to target with your ads, the platform will find an audience segment that matches your selected criteria and will serve them up your ads. Assuming someone from the group signs up for your product (aka converts), you wouldn’t want to waste money continuing to serve them the same ads. By sending a conversion event to the platform, you are instructing the targeting algorithm to pull the converted user out of your campaign and replace them with someone else with matching criteria.

For one-off integrations, sending conversion events will require engineering support to make code changes, but, again, this is worth the effort as it is a very effective way to optimize your campaign spend.

As you start to scale and have various data sources you need to pull and send data from, you’ll want to implement a tool like Segment. Be warned, while these types of tools make managing multiple data sources easier, they can be complicated to implement and will require ongoing configuration and maintenance.

Systemic approach

With the right tools in place, you’re ready to start experimenting! Your ad content will be your control, and the various platforms are the variables you are testing. To get the most accurate results, keep your ads as consistent as possible across each platform. Since each platform has its own requirements, aim for as close as possible to minimize any variance. Don’t let perfect be the enemy of the good.

The next variable you want to control is audience targeting. The goal here is to match the audience criteria as closely as possible across platforms. Be as specific as you can be (e.g. demographics, region, interests, etc.) without making the audience so small that you don’t get results in a timely manner.

Remarketing is a good strategy for finding the same audience across platforms. Remarketing will anonymously target visitors that have previously visited your website. Keep in mind that remarketing only works if the platform that you use can actually “find” the people that you want to retarget. This will require a certain volume of traffic on the properties you own.

Lookalike audiences and customer match targeting are also effective strategies for finding the same audience across platforms. This approach requires you to upload a list of your existing users so the platform can either target the specific customers or find other users with matching criteria. Similar to remarketing, there are certain volume thresholds you need to reach in order to make this approach effective. You’ll also need to consider legal and privacy issues since you’re uploading PII data.

Finally, since each platform has its own data set, you’ll want to add all of the data into a centralized report. Start with a spreadsheet, manually adding the data for each platform you’re using. In time, you can automate the data collection process with a tool like Zapier, and create more visually compelling reports with a data visualization tool like Looker Studio.

This is an example report from Looker Studio. Reports are highly customizable allowing you to pull in multiple data sources for a single report all for free (forever).

Goal Setting

A successful paid advertising program relies on setting realistic and revenue-aligned goals against your advertising budget. But in the beginning, when you have little to no historic data to work off of, this can be a recipe for failure and disappointment. So where do you start?

First, you want to set the right expectations. Your first few tests should be considered experiments, and the process is about discovery and learning. Take the time to communicate your approach, review the assets with your stakeholders, and get everyone aligned with the initiative.

With limited or no historical data to reference, use industry benchmarks as your KPIs. Finding the appropriate benchmarks will take a little digging as you need to factor in the platform, your industry, and other factors like the ad type as well.

Wordstream is a quality source of information, and they are referenced across multiple credible sources. In their 2023 report on B2B Paid Advertising Benchmarks, they suggest Google Search Ads to be a top-performing paid advertising channel with click-through rates (CTR) ranging from 6-7% and average cost-per click (CPC) between $2-$4. Compare that to X (formerly known as Twitter ) with an average CTR of 0.86% and CPC of $0.58 and you see the significant difference.

Ultimately, you want to measure your performance based on the benchmarks you realize over time. So your next step is to aggregate all of the data into a spreadsheet and compare results.

The example above shows actual results from the very first paid advertising experiment I ran for a B2B SaaS client. The Budget column shows the planned spend (what we defined at the beginning), and the Actual column keeps track of how much was spent so far. The CTR is derived from Clicks/Impressions and CPC from Actual/Clicks.

These results only tell one side of the story: the quantity of users that came through your ads. Since your goal is to convert visitors outside of the platform, you’ll want to take into consideration the conversion rates on the pages you are sending traffic to to start forecasting the number of new sign-ups or leads you think you can generate. Ultimately you want to understand how much revenue you’re generating from each channel to define your Return on Ad Spend (ROAS).

ROAS refers to the amount of revenue that is earned for every dollar spent on a campaign and is a critical KPI for scaling your program. Similar to other benchmarks, ROAS is specific to the platform, campaign, industry, etc. ROAS can also be impacted by internal factors such as your business development team’s ability to qualify leads or the product’s ability to monetize new users. Calculating and improving your ROAS can be complex. If you want more information on the subject, Neil Patel’s A Complete Guide to Improving ROAS is a great place to start.

Final Thoughts

This guide outlined a strategic approach to getting started with paid advertising. With a focus on maximizing outcomes while on a budget, we covered the right mix of tools, processes and cross-functional support needed to effectively run your paid campaigns. As you continue to scale up your paid advertising program, a methodical approach, as described in this article, coupled with continuous learning, is the key to maximizing return on investment!

The post How to Maximize ROI on Your Early Paid Advertising Efforts appeared first on OpenView.