We have lived in the Search Engine Optimization (SEO) world for the longest time – it remains critically relevant. Many of us have embraced the new demands of Answer Engine Optimization (AEO). [I’m sorry, I’m not a fan of Generative Engine Optimization (GEO) because that honors the technology and tech changes all the time.]

Organic Answers Optimization, bringing the two worlds together… Because they really are one thing now.

Big brother SEO is still dominant – most traffic, most revenue. Little brother AEO has “stolen” “market share” in the consumer influencing game (no need to come to our sites ☹️), has started to drive modest scale in traffic and a surprising scale in profit.

Practitioners in each space fight surprisingly passionately to preserve their respective SEO and AEO domains. It. Is. Ok.

For me, the business person, it is now just one thing:

 
My SEO work drives AEO, and AEO drives SEO. The brothers (or sisters, if you prefer), united in strategy, execution, tactics, impact, and analytics.

Different “machines,” one quest: Organic Answers Optimization!

It makes it easier to make peace with this:

I want to bring these two together, from a business perspective, because “organic” deserves extra special attention. Even bigger declines in traffic are on their way.

Context for Change.

Around this time last year, I wrote a six-part (!) series on AEO, AEA (Analytics), and a three-part series on how to identify the upcoming losses, take actions for recovery, and compute the financial impact.

[Annual Premium Subscribers: I have converted all this into an updated 114-page Answer Engine Playbook! If you would like the PDF version of the mini-book, please email me.]

In a keynote I subsequently delivered, this picture captured the evolution:

Let’s say I have an upcoming trip to Prague.

Search

Old-school search would have me going to Big Google and searching for “lightweight carry-on luggage.” I’ve been patterned to use keywords to get back three paid links and ten organic links, and then head off on a research mission to find the right suitcase.

Most of us do this (a billion plus).

Answer.

Along with approx. 50% of GenZ who use an LLM every day, I stopped doing searching 18 months ago. I now pop open ChatGPT and type “ I need a light carry-on for a ten-day trip, with a small European airline connection, for under $250. I also need a backpack that I can stuff with extra clothes and a laptop, to go with the bag that the small EU airline will allow me to carry on.” Astra (my GPT model) is now off to the races, and 17 seconds later I have my answer. No research, no sweating.

A few hundred million of us, skewing young, are doing this.

[Bag: Travelpro Maxlite International Carry-on Spinner. Backpack: CabinZero Classic Tech 28L. Both look amazing. I’d never heard of CabinZero: the discovery power of an LLM.]

Complete.

Increasingly, I skip the buying part customers doent do it. Same context-rich query as above, but I might add “ …buy the first recommendation for each, confirm it will arrive by Oct 30th, from a site with a clear returns policy.” Often, the purchase shows up. Sometimes the site blocks my Agent from buying, so it adds to cart and sends me the link.

1% to 2% of us are doing this.

The world is moving right, because why would a human need to spend time hunting and gathering, the meal can arrive all caught, plucked, seasoned, and cooked?*

With Dots and Muse, the pace will pick up.

*Some people find joy in shopping; they will continue to have this option!

Why One OAO Scorecard.

Your customer does not care if they used Old Google or used an Answer Engine. For them, they were just seeking, and they found what they wanted.

[Per SparkToro, approx. two-thirds of US Google searches end without a click.]

Your CFO does not care about Old Google or Claude or Qwen. They care that we are chosen and get revenue.

[Per Adobe, with a trillion US retail visits analyzed, LLM-driven visits deliver 53% more revenue per visit than other channels. Per Shopify, LLM-driven visits convert approx. 50% better than Organic Search.]

Your Marketing team does care because you have an SEO team/agency and, if you read my mini-book, have a raft of AEO initiatives in your Content team. And these are silos today because the work used to be different. SEO, which you already know all about, had an emphasis on 1P and text plus images. AEO was built on that strong foundation, and added an emphasis on 3P and video plus audio.

As customers delegate more, a visit to your website becomes a less complete record of your influence. Hence, pulling it all into one scorecard will provide a fuller view of influence, and create incentives for your silos to break.

One more reason… For now, the first strong commercial results from your “AEO” efforts will flow in through your “SEO” traffic. Unification helps here too.

Meet Kettle & Loom.

Learning is better when we have an actual use case. Kettle & Loom is a made-up home goods retailer. K&L’s CMO was given a report with three numbers:

Is that good? Bad? Yes. 😊 Let’s learn.

OAO Analytics Methodologies.

For our unified scorecard we need metrics. BUT. Methodologies trump metrics!

Let’s do that first.

There is no one ring to rule them all. At this stage, we will have to pick and choose from a basket – and there is a lot of “variable” quality out there.

Your owned systems (GA, CRM, ERP) describe visits, behavior, sales and profit, mostly to entirely accurately. Search Console describes observed Google Search performance – often aggregated or hidden for privacy reasons. Competitive tools estimate things about other sites that your analytics can’t see – hence, they’re best for comparison, never for raw numbers.

It is important to reflect on methodologies carefully because it really is a case of GIGO. And, in our world of immense scale, it is more like garbage in, landfill out.

It is particularly critical for Answer Engine Analytics tools as they are so new. Investigate how they collect data. [Premium Subscribers please see: TMAI #472: AEA Methodologies & KPIs. If you can’t find it, please email me.]

There is a lot of snake oil out there. Ex: Some vendors claim, or STRONGLY imply, that they have full access to actual prompts typed by users of LLMs. That is simply a lie.

Panels of a few mil can be somewhat useful at extremely aggregated levels. Ex: Industry. Even by category in an industry, it falls apart. For a single brand in a category in an industry, it is just crap.

Even a large panel will see 0.01%, or significantly less, of ChatGPT’s July 2025 publicly stated 2.5 billion prompts. For every MILLION times a question is asked about your category, a vendor claiming “tens of millions of prompts captured” will catch about 100, and only single digits, at best, will be about you. Worse news? The monthly “search volume” of any one prompt is effectively one.

One day we might have “Claude Webmaster Tools,” or “OpenAI Seeking Console,” but until then… no dice.

SEO also has issues we have learned to be careful about. Marketers constantly mistake estimated traffic, authority scores, and visibility indexes – all proprietary vendor guesses – for Google’s actual ranking mechanics (falling victim to Goodhart’s Law).

K&L’s Agency Visibility Score of +9%? Their vendor’s model of rankings * estimated clicks. Some value in comparing against direct competition. It is not proof of any extra dollars.

Everything in AEO for now, at least, is a “long tail query,” but we also don’t get such queries from Google – these hidden rare queries add up to nearly half of all clicks (Ahrefs). 3P SEO tools that “model” this long tail data should be viewed with suspicion. Google now mixes AI Mode questions into the same report, unlabeled (and rare, hence, more likely than not, hidden).

K&L’s -14%? Real clicks. But brand and non-brand are blended. AI Mode sits inside, unlabeled, and nearly half the queries are hidden. That number is a mush of all that.

Just because a source is not perfect does not immediately mean it is useless. Understand. Use metrics from the methodology at the signal strength it offers.

Humility Note: Organic Answers Optimization is funneling demand that in large part was created by brand advertising, distribution, seasonality, word of mouth, yada, yada, yada.

K&L’s +2% organic revenue? 50% of it comes from brand searches, and most likely from non-S&A EO initiatives.

Organic Answers Optimization CxO KPIs.

KPIs are metrics that directly impact the bottom line. Typically, they are for people who fund things. They tend to move over months and quarters. They are most influential – or you are doing them right – when they have pre-set targets.

For organic traffic, across all machines, from an ocean of metrics and fuddy counts, I recommend three KPIs (you should rarely have more than three):

KPI #1: Non-Brand Organic Search Profit.

Measures profit from orders (or leads) that began with an unpaid search visit from someone who did not search for your brand/product/service name.

Best source of this data: GA4 by landing page + Search Console branded filter + GA4 AI Assistant channel. It is imperfect, but as good as it gets.

[Premium Bonus: Please see TMAI #521: GA 2026: Unlocking New Value re how to improve GA’s ability to report LLM traffic more accurately using my AI True Traffic Total Kit.]

Why insightful: Following my humility note above, the real job of OAO is to help find and deliver people who didn’t know you – brand demand has extremely low incrementality.

Action recommended: Set targets for this number, then fund SEO initiatives against it (to mostly slow down declines in Organic Search traffic – using, among other things, ideas in my Loss/Recovery spreadsheet model).

Pro-tip: You can translate KPI #1 into Rental Cost. Non-brand organic clicks * what you’d pay per click for the same queries = What it would cost you to rent an asset you own!

KPI #2: Answer Engine Profit.

What it says on the tin.

Best source of this data: Site analytics tools, AI Assistant channel. This is incomplete (mid-Oct ChatGPT decided to change referral strings), and a bunch of it lands in Direct. But. We have to start somewhere.

Why insightful: Is the trend Adobe and Shopify have identified, higher conversion rates and AOV, happening in your case? Profit numbers bring focus.

Action recommended: Use it to gain investments in content (especially video, audio, especially brand, especially 3P). This data can help you persuade your sometimes stubborn, old-school Tech teams to focus on technical AEO, unblock robots and Agents.

Pro-tip: It is really hard to quantify, but most AEO content investments will initially return as a reduction in SEO declines (hence more SEO revenue).

KPI #3: AI Brand Score.

An approximation for how often AEs recommend you, and how prominently, when the question doesn’t include your name. It combines Visibility Score with Average Position.

Best source of this data: From tools like Evertune*, a probabilistic answer gained from repeated prompts to identify a distribution of answers the machines provide. 100 = your brand named first, every time. What’s yours?

What it isn’t: It doesn’t claim to know what your customers type. Nobody knows that.

Why insightful: The number closest to the Most Important Marketing KPI ever: Baseline for Unaided Brand Awareness. You can also think of it as your share of an LLM’s shelf.

Action recommended: I believe the insurance against all the changes coming is my strategy of more content, more places, more often (details in my AEO mini-book). AI Brand Score measures progress against this faith in future influence and revenue. Hence, proof for your CFO that content investments are worthy.

Pro-tip: Tag every major model release on your AI Brand Score chart. A new model can swing you ten points instantly – without changing anything fundamental. Don’t take credit, or blame, for the AI’s homework.

*A long time ago, I made a tiny, tiny, tiny angel investment in Evertune because the team’s awesome.

Let us reflect on K&L’s measurement and apply the lessons above…

The Story Continues in Part 2.

K&L’s CFO will ask the obvious next question: why is non-brand profit down? Did K&L lose rankings? Did Google keep the click? Or did customers take their questions to LLMs and never click at all?

TMAI Premium #532 opened with a simple triage, built entirely from data K&L already own. That quickly helps us identify challenges/opportunities each month. The next step is to drill down to diagnostic metrics and influencing variables that K&L’s agency should be acting on. For larger brands, TMAI #532 also pulled out of SEO the contribution AEO makes to reducing declines or increasing growth.

And we’ll try to reduce the faith required in my OAO strategy of more content, more places, more often.

Premium Subscribers also received an Excel workbook to use across VPs, Directors and agency peers to activate SEO+AEO=OAO revolution. To get a copy sign up here, and reply to the welcome email.

Bottom line.

As humans move from Search to Answer to Complete, the cascading impact on businesses is immense. It’s hard to appreciate for now, because Search remains dominant – especially for older demographics (who have all the money!).

My hope with my little new acronym, OAO, is to encourage a focus on change to ensure change does not catch you by surprise – delivering a business shock.

Faith + Data will prevent surprises.

Carpe diem.

Some of these capabilities are forward only. They collect/process data from the day you turn them on, and never backfill. So… As soon as you are done reading, get to activating!

I’ve picked four (plus three) features that will speed up your conversion from a Reporting Squirrel to an Analysis Ninja. This will get you into more meetings with Extremely Senior Leaders (ESLs). That should translate into more influence [and higher compensation for you!].

1. AEO: The AI Assistant Channel.

You’ve read my mini-book on AEO [TMAI Premium editions #468 > 473]. You’ve taken my Excel based AEO Loss > Recovery > Growth Model [TMAI, 482 > 484]. You have seen my prediction of approx. 20% to 30% loss in SEO traffic come true.

But, until recently the only way to answer is ChatGPT sending us traffic, conversions required a regex. Not anymore, GA answers by default. And, there’s more.

Location: Reports > Acquisition > Traffic Acquisition. Look for AI Assistant in the Default Channel Group. Medium is set to ai-assistant, campaign to (ai-assistant). No setup required on your part.

Unless your business is exceptional, your Answer Engine (AE) traffic will be small. 1% – 5%. Reassuring to those who have read my L > R > G AEO series above. Disappointing to those who have not, because they might be expecting SEO down equals AEO up. Sadly, no. Now. You have data.

You are not getting back a big chunk of the lost Google SEO traffic, hence focus on AE driven engagement rate, session key event rate, conversion rate. Unless your business is unique, you will discover that the quality of traffic is much better. Compare it to the Organic Search row in that same table.

Here’s something I find helpful… Where is the AEO traffic entering my site?

It sheds light on what we are ranking for in Answer Engines, what persuasion or influence is there that is bringing people to my site, so on and so forth.

Adobe’s March 2026 U.S. retail data showed AI-referred traffic converting 42% better than non-AI traffic, spending 48% longer on site, and generating 37% more revenue per visit. Shopify published similar data in May 2026 [nearly 50% higher conversion rates for Q1 sessions beginning on PDPs, soundly beating Organic Search, and 14% higher AOV for AI-referred orders].

AEO traffic is more valuable than the count of sessions would indicate.

Higher Order Bit: Answer engine traffic is a couple percent of the sessions, and converts twenty-five percent better. This is not a traffic story. It is a margin story. It is an argument for serious AEO. For referring traffic AND influencing those who might never land on the site.

Real life is in nuances… Cautions:

Data only starts mid-May 2026. No past in “AI Assistant Channel.”

The list GA is using is a moving target. Claude was in and then out by June. Deepseek, CoPilot, and Grok showed up later. Check the definitions monthly before you send your reports out.

The issue with no referrers from mobile apps, desktop apps, in-app browsers etc. also haunts us here. There’s been massive growth in LLM apps. In GA traffic will end up in Direct.

Heartbreakingly, Google categorizes its own AI Overviews and AI Mode traffic as Organic Search – though clearly, we would call it AE traffic. Perhaps Google wants to make old Organic Search look better than it might be. ☹️☹️

So… The AI-Assistant row is deeply welcome, and bigly underrepresented. Use it actively, and tag your reports with “Understates LLM Traffic.”

Bonus Gift: Use my Toolkit below to address above issues and get True AI Traffic & Revenue.

2. Conversion Attribution Analysis.

Assisted Conversions are back! Reason #2 for centralizing your MTA in Google Analytics.

TMAI Premium Subscribers are aware of my consistent advocacy of Assisted Conversions KPI as critical context for your Last Click conversions “reality.” [Please see #448 (Smart Attribution), #434 (Attribution to MMMs).]

Feel the difference.

A means reduce focus on TikTok. B means a big reassessment of TikTok across acquisition, behavior and outcomes.

Location: Advertising > Conversion Attribution Analysis. For Assists, set the attribution model to Last click and the attribution timing to interaction time.

Recommended Steps:

1. Pick the model context first, click, choose Assisted Conversions / Last Click.

2. Compare value added by assisting (blue bar above) and total conversions attributed to the channel by last click (green). I anticipated Paid Social, but was completely surprised by Organic Search’s assisting role. Like, really surprised.

3. I love sorting by this column, Assists, to remove blinders from Marketer eyes.

4. You will of course pair Assists with Outcomes (revenue) and Inputs (ads cost).

Super cool, no?

Reason #1 to ditch your MTA tool and switch to GA? Much better understanding of each channel’s role in the consumer journey!

GA uses a refined “funnel analysis” to sort channel touch points (owned, earned, AND paid) into Early/Mid/Late, along with splitting out single-touchpoint paths.

1. Channels that are lone rangers. Notice the placement of Direct, and the busting of the myth that “Direct” is stealing credit from other channels (in case of this biz).

2. Some people use phrases like prospecting, education, consideration interests. Of particular interest here are “late touchpoints,” channels that close out complex journeys. (Notice Paid Social here, surprising.)

3. Quantify the share of data-driven conversion credit assigned – at each journey stage!

Identify the undervalued “upper-funnel” channels and STOP cutting them. 😊

For my acquisition conversations I start with this report. It is such a simple way to identify clear actionable paths…

One more super actionable bit: Single-point or Multi-point location should immediately change your Call To Action in the Ad. With a cascading impact on the creative (see: Human Made Machine!).

Play with time periods. Apply “brand” and “performance” segments – normal Analysis Ninja stuff. If your boss asks “are you using AI”? If you follow my recommendations above, say YES!

Higher Order Bit: Assisted Conversions (ACs) are not the destination, Incremental Conversions are. Still, the fact that ACs are available all day, every day, for every channel gives you an intelligent measure that is immensely actionable. Use it.

No more throwing spaghetti at the wall. No more spending on Pinterest or Snap because you have a feeling.

Real life is in nuances… Cautions:

Reminder: Attribution is not incrementality.

You will notice that the total of assists sums to well over 100%. That is by design. Use this report to reorder your channels, please do not inflate your total or imply it.

The Advertising section requires you link at least one Google Ads or GMP account.

I’ve advocated for Macro ($$$) and Micro (“assisting”) Outcomes. Still do that. But, make it a super focused and valuable list. One Macro, that’s your ecom. Four or Five Micro (newsletter signups, software downloads, leads, etc). If you don’t, a conversions set stuffed with micro-events will tell you everything assists everything. GIGO.

3. CMO Convos: Cross-Channel Budgeting.

The gap between email converts at 4.5% and email converts at 4.5%, go from six sends to nine, it will deliver $3m of incremental revenue is the gap between an insight and a decision. It is also the difference between a Reporting Squirrel and an Analysis Ninja.

I’m a big fan of predictive analytics (TMAI #250), and I’m so excited that Google has built a version of this into the product.

Location: Advertising > Cross-Channel Budgeting.

Tool One: Project Plans. They answer pacing. Is spend on track, and what will it deliver at this rate?

Tool Two: Scenario Plans. They answer allocation questions. What happens if I move $250k out of Paid Social into Paid Search?

These models are trained on your history, they incorporate seasonality, and additional factors.

In my Smart Clusters framework, you learn the criticality of win before you spend. Now, instead of just being in meetings where you present results after the spend, you can start going to meetings with scenarios that help plan the execution strategy WITH predictions of business outcomes.

Or, better still, go with three – one for each team pushing their agenda – and help the business leader make the most informed decision vs. being swayed by the charms of one team. 😊

Recommended Steps:

1. Define planning period, target KPI, budget, eligible conversion.

2. Inspect diminishing returns, and relationship between spend and selected outcome.

3. Compare the current plan with the modeled optimized point.

4. Assess optimized vs. projected cost, revenue, and ROAS.

[Premium Subscribers: See my advice in TMAI #463 on why POAS is 10x more powerful than ROAS. Email me if you can’t find it.]

Make cool discoveries like shifting 20% of your budget barely moves projected revenue – welcome to the surprise that our channels are far more substitutable than Google, Meta, TikTok and others might imply. Such discoveries are immensely more valuable than recommended allocations, and they also change how you brief your Agency.

Higher Order Bit: I ran four allocations. Moving a fifth of the budget between them barely moves the revenue line. Which means we are optimizing a split that doesn’t matter, and the real lever is somewhere else entirely.

Cross-channel views are honest when you have cost data for all your spend. Google has made a ton of progress in making this easy. Here’s how to import campaign data from Meta ads, and TikTok ads. Without real cost data, my recommendations above will not be as life-altering as they should be.

Real life is in nuances… Cautions:

Remember: All models are wrong; some happen to be useful.

Any model trained on your own past will be wrong to a certain degree, because your present is not your past (or, I hope it is not!). Each time, present your analysis as a prediction, a projection, or a best estimate, but not a forecast.

Over time, you’ll be less wrong. And your predictions will gain more confidence.

If you don’t see Advertising > Cross-Channel Budgeting, you will see what you need to be eligible:

4. “Conversational Analysis”: Ask Advisor.

Will you miss reports if they die?

I already don’t, because Claude can go do better analysis for me and find quality answers from Excel or Google Analytics in the time it takes me to navigate to the report I need (and then I need more time to figure out what the data is saying).

You can imagine my joy at seeing “Ask Advisor” – as uninspiring as that name is.

Now you can just ask a question, have the advisor do the complicated analysis, and get an answer that would otherwise take a bunch of effort:

Location: A magnifying glass icon on the top right of your window.

Try your simple queries, try your complex queries.

Your day-to-day experience of GA will be much better if you just start with Advisor. Default to it, because see how hard this would be to answer via your normal reports vs. getting an answer in a few seconds:

It is a struggle to get ESLs to use GA. But, tell them they have “AI” at their disposal and they might get excited to skip emailing you! They get answers quickly, and you get time back to focus on the hard questions. Bonus: The data pukes can slow down (or become self-serve!).

Have them try this query: What changed for my top two revenue-generating products over the last 30 days — traffic, channel mix, price, promo?

Show them a complex query you are using: Compare the last click credit attributed to top ten channels vs. the DDA credit attributed to each channel, and identify the top two that are being undervalued by last click, and by how much?

Ask Advisor is limited by what’s in GA. #doh

Even simple questions from your CMO require you to go outside of GA. Look at your Media Plan. Your Promotions calendar. Your internal offline customer sales data, and more.

Worry not. The team at Google has generously built out a first-party GA MCP server!

It connects your GA data to your fav LLM. Takes half an hour with a developer, I strongly recommend hooking up with ChatGPT or Claude. Now you have all the data in GA, and the ability to extend your analysis across a ton of new possibilities, as the MCP server is bounded by nothing.

Outcome: Your Analysis Ninja powers expand well beyond just web analytics and go so much further in activating decisions. If you give your ESLs access they can answer the questions they actually have (which will naturally span the entire business vs. just the site).

Hook up your company’s approved LLM to the GA MCP, it’ll be a concrete sign of your expanded influence.

Real life is in nuances… Cautions:

If you ask causal questions, AI Advisor might start inventing. Be careful.

Prompts matter a lot. Please see my basket of 19 Analytics & Marketing prompts in TMAI #514 & #515 and use cases to improve yours for Advisor.

For more complex queries, examine the chain of thought reasoning. Understand the detours and biases.

Premium Subscriber Bonus.

For Premium Subscribers, I’ve created two helpful items – please email me for them.

GA Feature Activation Audit. It outlines 22 checks across the four main features (and two bonus ones below). What to check, where it lives, what good looks like. My Readiness Dashboard tells you which features you can use today and which you’re still earning.

AI True Traffic Total Kit. Recall the issues I’d outlined in item #1 that cause understating of your AE/AEO/LLM traffic and revenue. And, the lack of history prior to May 2026. Worry not. My toolkit outlines how to build a custom channel group via regex, the channel ordering rules, and includes a reconciliation worksheet that converts your AI floor into a defensible range (including “dark traffic”!). Replace my number with yours when you’ve collected evidence.

Bottom line.

We’ve used GA thus far primarily to explain what happened. In 2026, we have the capabilities to shift to driving what happens next – with concrete proposals.

The transformative shift to win before you spend… To Analysis Ninjas.

Carpe diem.

PS:

Two additional notable features I’ve activated:

A. Source Group Consolidation.

It fixes the mess of situations like facebook, fb, meta-facebook, %instagram%, ig, etc. into a cohesive stream, say, Meta. Google’s own platforms have had this benefit in GA, now it can be applied to everyone. And, retroactively. End of regex hell.

After you activate, your first stop should be analyzing your cross-channel ROAS – be ready for surprises.

B. High-Value Purchasers with LTV Percentile.

Without the torture of BigQuery SQL, you can use a field called LTV Percentile to select top percentile of LTV users for your business. Your dearest, most-cherished, nicest to be, customers.

Why obsess with just “Purchasers”? They are not all worth the same. Find the 5% responsible for 40% of your profit! Then go back into Google Ads Audience Manager and have it find more of them. 😊

Let’s start with a phrase that causes me to cringe every time I hear it: “directional results.”

It sounds sophisticated. Nuanced. Even reasonable. But what does it actually mean?

Answer: Just about anything you want it to mean!

I was reminded of this while reading Google’s official guidance on how to interpret Google/YouTube’s Brand Lift, Search Lift, and Conversion Lift studies.

It made me pause. Then sad. Read the last one carefully.

Google says: ≥90% is “very good chance.” 70%-90% is “good chance.” 50%-70% is “moderate chance.” <50% is "no lift." Across three layers the sentiment emphasized is, paraphrasing: Your ads probably did something good.

The last layer is, paraphrasing: Hey, the ads might not have sucked, spend some more and let’s see what happens.

Google also says in the article: Studies at 50%+ can provide valuable, directional insights, and explicitly tells advertisers in the 50%-70% band to use the results directionally.

If you pause and reflect on this carefully caveated article, you’ll see this is a case of: Heads Google wins, tails you lose.

Whose Success Matters More?

To bring this lesson home, let’s assume I ran a campaign on YouTube.

 
Google calls 70%-90% Certainty a “good chance.” It also says studies at 50%+ can provide “valuable, directional insights.”

What. The. Heck. Does. Directionally mean?

 
From Google’s view, the interpretation is that there is a “good chance the results were caused by your ads.” But as a Senior Director of Brand Media, is a “good chance” good enough for me to report to my CFO and ask for $5 mil more?

Two Parties. Two VERY Different Consequences.

Party One: Google: It sells advertising. If a study contains weak evidence that advertising could possibly have worked, Google has every incentive not to throw it away. Perhaps, and that’s the operative word, perhaps there is something useful to learn. Fair enough.

Party Two: You: You own the Marketing budget. Your job is not to find something interesting in the data. Your job is to answer: IS the evidence in YouTube’s BLS strong enough that I should risk another $5 million of my company’s money on the conclusion that the first $5 million worked?

Here, I made a little table to outline incentives and implications:

It is impossible to know if Google’s wording on its sites, in its client decks, is intentionally self-serving. It does not matter. The outcome is self-serving.

Google does not bear the consequences of an optimistic interpretation. Your company does. Google also, to their credit, has a drive-by line that says: Guys, interpret results based on your business needs and risk tolerance.

Since I am bearing the risk, for consequential business decisions I use 90% Certainty as the floor. It isn’t magic; it is simply the minimum level of evidence I’m willing to accept before telling a CMO or CFO: “This worked. Spend again.”

[Premium Subscribers: Refer to TMAI #298: Smarter Statistical Significance Reporting. It includes a one-pager to standardize stat sig reporting across your team, agency, vendors. If you can’t find it, please email me.]

Google’s emphasis is on “moderate chance,” “good chance,” “directionally.” These are comforting, decision-sounding words that obfuscate evidence more than illuminate it. The language is for sure carefully caveated, but it ignores the immense risk and cost it intentionally or unintentionally shifts onto Google’s clients.

Let me prove it to you.

Google Brand Lift and Conversion Lift: Certainty vs. Replication Risk

Rather than discuss p-values associated with “good chance,” “moderate chance,” “use these results directionally,” let’s focus on the question your CMO and CFO care about:

If we ran this campaign again with another $5m budget, how likely is it that we will get a result strong enough to confidently say: “Round two worked. Spend again.”

You’ll recall YouTube reported a +3-point lift in Consideration at 70% Certainty.

While they might not use the technical phrase, your CMO and CFO are essentially asking: What’s the Replication Risk?

[For this thought experiment, we’ll assume, the second $5m will face the same underlying conditions as the first $5m. Same platform (doh!), audience, seasonal factors, promotions, creative, sample size, etc.]

To compute Replication Risk, I’ll use a simple Bayesian replication model inspired by biostatistician Steven Goodman. It helps translate Google’s abstract “Certainty” into a much more practical question: What might happen if we measure the same thing again?

YouTube’s BLS estimated a +3-point lift in Consideration. But +3 is an estimate; rather than pretending it is precise, the Bayesian approach carries that uncertainty forward into the next measurement.

As business owners, it is our money on the line. We are applying this simple logic:

(Uncertainty from what the true effect might be)
combined with
(Uncertainty from measuring it again)
= How confident should we be that we will see similar results?

Let’s do some math!

Three computations for the Business Owner lens:

Here’s the model using a simple Bayesian replication model inspired by Mr. Goodman:

[Pedantic Note: Google says displayed Certainty is rounded down in 5-point increments. So a displayed 70% can represent 70% to just under 75% and hence the exact replication % can move a few points. For simplicity, I treat Google’s displayed Certainty as exact.]

YouTube had reported 70% “Certainty,” follow the row above… That “good chance” Google label translates into:

As a business owner, would you feel confident saying yes, let’s spend on YT again?

Forget Google for a second, take me: I recommend a minimum of 90% Certainty for a business decision. That comes with a Replication Risk of 50% (!!) and Repeat Shows Positive Lift of just 82%. Too uncomfortable in many cases (what if it was a $10m question, would you take 50% and 82%?)

Now Alex and Juliette understand why I so often push for 95% Certainty. When you spend as much money as we do on YouTube… At least get the Replication Risk down to 40%, increase the Repeat Shows Positive Lift to 88% and increase the odds more in the company’s favor.

So… Are results below 90% Certainty useless?

If you have real money riding on it, look at column 3, look at column 5 in the table, see if you want those odds. If you have little to no money riding on it, sure – go try a new hypothesis, design a different/better test, give additional money to Google/YT/Meta/TT, remeasure.

A bonus relevant lesson from “Beyond Power Calculations: Assessing Type S (sign) and Type M (magnitude) Errors”: Weak evidence can hurt twice: The evidence may not survive (70% in our case), and the apparent impact might be overstated (+3 in our case).

You’ve been warned.

Premium Subscriber Bonus: A Working Model.

I want you to be able to see all the formulas and math I’ve used in my computation of Replication Risk.

I want you to be able to test lift’s Certainty AND Magnitude (Repeat Shows Positive).

I want you to be able to type in the results of your Brand Lift, Search Lift, and Conversion Lift studies and calculate your own truth (before you put your career on the line).

To enable all this, I’ve built a working model with Read Me + Methodology, Calculator, Risk Table, and Calculate Your Truth tabs. It is a bonus for TMAI Premium subscribers; please just email me for lift_replication_risk_model.xlsx.

Bottom line.

Google and you are sitting on opposite sides of a consequential decision.

Google sells advertising. You are spending money.

Google might reasonably find value in evidence that is merely suggestive. See the first table. You have to decide whether that evidence is sufficient and measured credibly enough to risk the next tranche of your budget.

Don’t let an advertising platform’s definition of useful information become your company’s definition of sufficient evidence.

Stop outsourcing your tolerance for risk to the company selling you ads or the Agency whose fees are tied to the size of your spend.

Carpe diem.

PS: We have used Google’s specific table and specific guidance today. Replication risk applies just as much to Meta, TikTok, and confident calcs your Agency is sending you. Please check just as carefully if they are solving for themselves or you.

This transformation applies across Media, Creative, Performance, Brand, Measurement, CRM/Lifecycle, Advocacy, Auditing, AI, and combinations of them all.

All types: Media. Creative. Performance. Brand. Measurement. CRM/”Lifecycle.” Advocacy. Auditing. AI. Combo of all.

Three strategic drivers behind the ‘we’re not in Kansas anymore’ realization:

1. AI achieved broader, more general intelligence.
2. Ad platforms happen to own foundational AI models. Translation: Intelligence became available, automated, and actually useful FAST.
3. Interconnectedness of systems. Data moves in real-time across your stack, platforms, partners, everyone. That doesn’t make AI smarter in general. It makes AI smarter about you.

These delicious drivers of behind the scenes changes at Agencies are now ready for a cascade of benefits for Clients.

More, For Less. Really!

Immediate and medium-term implications:

[Premium Subscribers: Please dive back into TMAI #496: What The Heck is Brand Marketing – the implications will power the next 10 years of your career. Email me if you can’t find it.]

Change will happen on sliding scale. Ex: Everyone can get A-driven 25% to 35% savings from work elimination today. Achieve about the same reduction next year – at that time you might also add 10% B-driven. So on, and so forth.

Cut to Grow. Really?

My objective is not to save on Agency fees. It is to create incentives to fully embrace the present and be ready for an unknown future.

If your Agency is working off an old-world Statement of Work (SOW), all the incentives, legally, aligned to keeping the past as the future.

Current contract has no incentive to shift work to platform intelligence and automation – thus reducing Agency hours. The SOW rewards manual work: micro-optimization, reporting, complex campaign setup, etc. – all harmful to AI-led efforts. Your contract’s Percent of Media Spend rewards spending. More and more and more activity – easier via non-AI! Paying using KPI Percent of Outcome creates AI-Impact alignment – it is not in the contract.

Renegotiate your Agency contract not to save money.

Do it to:

Critical lens on Agency fees is simply a mechanism to accomplish A, B, C.

When you renegotiate, you are not buying fewer hours of the same old work. You are buying a different operating model.

A Gigantic Caution.

Nothing I’m recommending will actually work while the Agency fees are a Percent of Media Spend.

That model literally pays the Agency to touch more, spend more, and be anti-AI.

To make the present the future, your Agency contract structure needs to migrate to:

The Agency will now offer genuine specialist depth, cross-client learnings that power speed of innovation, cross-platform OEP consumer behavior informed strategy creation, and new outside-in scale… Or additional fees reductions become possible.

Everybody Wins, YES!

If you lead an Agency, I’ve likely added to your discomfort. Let me make the case for why this change is incredibly exciting for Agencies.

A. You are no longer a hamster furiously rotating a wheel connected to nothing.

Agency gets to work on really cool, smart, high-value, unquestionably outcome-impacting initiatives. This extends the life of the Agency. This enriches the careers of every Agency employee.

B. You don’t need to be doing $50/hr or $100/hr work.

Your new SOW contains more expensive $500/hr and $1,000/hr work streams, along with $yyy,yyy per week costing strategic projects!

Glorious impact: This new and exciting work allows you to hire large numbers of entirely different types of experienced individuals – while paying them well (vs. the current: charge the client a lot, hire the most junior employees you can get away with, and pay them below market wages in exchange for “experience”).

C. (As with every revolution…) Better Agencies will emerge.

Agencies have died and were born each day last decade. I anticipate both accelerating in the next two years.

Births will be so exciting. Modern Agencies attuned to persuasion in an AI world, responsive to shifting consumer experience, embracing/leading radical evolution of platforms, structurally built to be outcomes-centered strategic partners! Mamma mia!!

The Core Shift.

The old Agency fee was largely rent on an execution army: campaign builds, keyword lists, audience segmentation, manual bidding, painful upfront contract negotiations for the next 18 months, pacing, trafficking, QA, reporting, and constant “micro-optimization” on ad platform to feed Clients with the illusion of “work” – and have something to show in the 2x/wk meetings Client employees demanded to “prove value.”

Except for the illusion need, most levers above are pulled by the platform’s own AI for Performance Marketing and many for Brand Marketing. You’ve heard the names: Smart+, ASC, PMax, AI Max, yada, yada, yada.

[Premium Subscribers: Calculate your sophistication score: TMAI #508: Google Ads Maturity Model. +Discover what your Agency should have been doing mid-2025 onwards.]

An Agency’s value has migrated upstream: Cross-platform OEP consumer behavior informed strategy creation. Value signals hunting and automation. Creative concept ideation and pre-testing (execution and variations are AI helped or solved). Super advanced holistic full-anywhere-outcomes analytics. Governance (guardrails, Agentic wrangling, brand safety). Some or all of these create new client impact and new client fees.

The Agency should not be paid primarily for touching the account.* In fact, over-touching now actively degrades algorithmic performance by messing up AI learning!

The Agency should be paid for automation, more senior judgment, governance, scaling intelligence, and bleeding edge innovation risk reduction (not “A/B Experimentation”).

Agencies are not becoming worthless. The basis of their value is materially changing.

Exactly as, Analysts / Creative Directors / Content Creators are not becoming worthless. The basis of their value is materially changing.

* If in July 2026 your Agency is touching your account more than once every dozen or so days, do not blame them… Fix the source by firing your Marketing Manager & Director.

Subtractions & Additions to Power Your Shift.

As someone with leadership roles at Clients (15+ years), Ad Platforms (15+ years), working closely with Agencies (10+) and leading one (2+), I say with confidence that the future is super bright for Agencies with good CEOs.

To support that transformation, presenting my… Client < > Agency Operating Model.

This week: Subtractions. Everything transitioned to Platform AI or reduced or eliminated entirely, resulting in changed Agency scope and resulting contract savings.

Next week’s Premium edition: Additions. A. Everything your team should own now. B. Streams of higher value work the Agency needs to grow into and be paid more.

Subtractions: What You Stop Paying For.

Extracting from a typical past Agency contract, the subtractions contain twelve dimensions to focus on. For each, I’ve identified the typical cost weight, reduction in Agency effort, and contract savings.

For each of the 12 dimensions, in a robust spreadsheet, I’ve identified: Area of Work. Old Model (what an agency did). New Model (powered by platform AI). New Company Role. New Agency Role. Change in Agency Scope. Estimated Contract Savings.

Since my experience along all of these dimensions would take a mini-book to detail, I’ll compress 12 dimensions into 5 clusters and summarize your actions.

[Note: TMAI Premium subscribers can reach out for the robust spreadsheet to direct the conversation with their Senior Leadership, Procurement, and CFO.]

1. Agency Activity Army.

Account and campaign architecture, “keyword” research and match-type sculpting, audience segmentation and targeting, radio & tv ad tactic structures. This was the beautiful old world of a hundred thinly sliced campaigns “for control.”

The machines devoured this work. PMax, Advantage+ (A+), similar collapse structure into a few asset groups that the algorithm allocates internally. Intelligence reads intent, using additional tens of thousands of data points beyond the keyword or post content. A+ audiences treat your segments as hints, and then finds converts you never listed.

The Agency’s job is not building, it is deciding. Advertiser (you) sets the reward function hierarchy, brand/non-brand priorities, brand safety red lines, margin protection bands. The Agency’s real remaining role: One-time architecture design, and occasional restructure IF strategy changes. It is NOT monthly rebuild billed as progress.

Approx. 22% contract cost weight, work that can be reduced by approx. 78%.

2. The Bid & Pace Dancers.

Manual bids, budget adjustments, day parting, device modifiers, daily pacing, spend checks, disapproval fixes, “anomaly” checks, “hygiene.” Hours and hours and hours.

AI can do narrow intelligence at a scale and impact that surpassed us in late 2024.

Smart bidding to your reward function (see: TMAI #432: AI Unlock: Value Based Bidding), campaign budget optimization, automated alerts, auto-recovery, now all this work continuously, and better. You just need to let the AI learn. When it starts, results will dip a bit. Your Agency jumps in to rescue by cutting bids/something. The AI learning resets. The dip returns. Agency… You know the rest. No stable learning. Company keeps sucking.

Reminder: Over-touching does immense damage in an AI world. Your Agency is causing this harm and it is your fault – for not understanding that every “rescue” is a sabotage.

The Agency job shrinks to setting the true reward function (aka kpi, target), occasional configuration of guardrails IF business strategy changes, and, hardest of all, operate at the rhythm of the AI’s learning cycle.

Approx. 14% contract cost weight, work that can be reduced by approx. 73%.

3. The Assembly Line.

Trafficking, ad builds, combination making, ad formats \/ creatives \/ variations, torture of tagging, QA, shopping-feed babysitting.

Ad platforms – except TV, Radio, press – can do this at a scale AND relevance that will shock you. Assembling bits from your ads, text, site, building creative, generating variants with GenAI (try this on paid Amazon!) mixing image video audio in the best version per person. All they need: often just your site link (or core assets) and a reward function.

Note: One reason they can do this spectacularly well is that your creative is judged by your VP of Creative/HiPPO, while the creative Ad Platforms deliver is judged by the business outcome delivered. 🤯

The Agency’s role narrows to the genuinely useful: Asset preparation, clean taxonomy, and spot-QA. Feed management is the one line that remains meaningfully skilled for now – sadly, data quality is still human.

Approx. 12% contract cost weight, work that can be reduced by approx. 45%.

4. The Optimization Theater.

Daily rituals of pausing “losers,” shifting budgets by feel, endless small A/B tweaks, the deeply irritating unfocused “learning agenda” items that will never amount to much.

Often the single biggest cost, and now the most destructive thing your agency is doing.

Modern AI-led platforms run their own continuous explore-exploit; they are already testing combos of creatives, bids, targeting to find high-value customers – at scale and cray cray speeds. They can also account for learning cycles that can span weeks/months, try that with a human.

The Agency’s role is cut down to: Few, really big (as in 15%+ increase in revenue), clean experiments, with the right altitude (creative concept – not execution -, audience attribute).

Approx. 16% contract cost weight, work that can be reduced by approx. 75%.

[Premium Subscribers: The role of what an Analyst does is quickly changing. Your current role will be gone by Jan 2028, replaced by a better one. If you are willing to learn and change, here’s how to save your career: TMAI #495: Analyst 2028: S.H.I.F.T For Relevance.]

5. The Reporting & Servicing Factory.

Manually pulled weekly decks/spreadsheets/update emails. 2x Weekly “update” “check-ins” with 6-36 Agency attendees. Exhaustive reports on placements and “brand safety.” Agency “account management” meetings with other vendors and partners. Detailed hand written commentary of the data already in dashboards. Urgent need it by EOD cuts of existing decks/reports. “Client Training Sessions,” with content already easily available to all anyway. Dinners, outings, offsites, to ensure client happiness.

AI cannot solve for all these items, certainly not dinners and outings.

But, dashboards and reports are disappearing with Claude-fronted data lakes that can do both what and why to such a great extent that you don’t have to be held hostage by the Agency’s 17-tab spreadsheet. Platforms for automated placement filters, and brand safety tooling that gets better by the hour. Since there is ever increasing automation in Meta and Google, you need drastically less “account management,” and certainly not as many meetings.

The Agency’s work is limited to: Aforementioned dinners. Automating in-flight optimization. Shifting focus from Care (99% today) to Do & Impact (TMAI #391: Data Storytelling Framework: Care, Do, Impact).

Approx. 30% contract cost weight, work that can be reduced by approx. 60%.

Punchline: Contract Cost Weight * Reduction in Work and in my case equals 65% savings. For you, it might be a little less or a little more. Use the table and the math above to discover.

The work is not going away, a lot of it is moving to machines. What’s left is more useful, higher impact work. Agencies don’t sell motion anymore. They sell judgement.

The Conversation Continues in Part 2.

Achieving this transformation in our Agentic AI-era requires you add new roles to your company to thrive in this new universe.

You also need to add/increase SEVEN threads of new work to your Agency contract.

In Part 2, I’ve covered both these critical elements in detail. Additionally, it also contains the complete Operating Model in Excel, to simplify transformation conversations with your CMO, CFO, VP Procurement… And then, your Agency.

If you are a TMAI Premium subscriber, please reach out for Part 2 if you are unable to locate it in your inbox.

Bottom line.

I come to Agencies from a place of respect. The best ones are extraordinary – taste, courage, and pattern memory across dozens of clients that no in-house team can match.

I come to AI from a place of trust. A vast majority of performance execution is AI-led, and all of it will be soon enough.

That respect and trust sit with this pragmatic truth: You can pay for the past or you can embrace and extend the present.

A new tomorrow starts with knowledge, and now you have it.

Carpe diem.

PS: A little in the weeds but…

A. With the extraordinary criticality of data and automation, it is important that you own your ad accounts, pixels, and any data Agency is piping from sources beyond you. Avoid becoming a hostage or victim. Get immediate and complete ownership.

B. When you kill Percent of Media Spend, you are putting a kibosh on toxicity of undisclosed markups, principal media, and rebates. Awesome. Be aware: There may be people inside your own company for whom that toxicity is a positive incentive. Don’t forget to fix it.

I’m of course talking about the Google Ads platform. To help you assess whether your company is actually AI-ready, I built a Google Ads maturity model that scores both your capability and how widely that capability is deployed.

Like it or not, Google has been AIfying their platform for some time. [Meta as well.] They are making it harder and harder for us to stay with how it used to be. Not such a bad thing, because to achieve better outcomes we can do less and the AI can do more.

Hence, I admit to immense frustration when in meetings and online discussions, “Search marketing” is still framed as brand, non-brand. Search “Marketers” are still monkeying with match types, hours, geos, audiences, negatives, and all the rest. Search “Agencies” are still proving their worth by making changes multiple times a day. All this is going backwards, profit pulverizing.

☹️

I want to share two biases:

You might come to Google and AI from a different place. I respect that. In a business context, consider this pragmatic perspective: The choice to stay in the past is not available.

So… Why lose our employer cash every single hour? Why put your career in reverse gear? Let’s embrace the possibilities today and extend our winning streak!

Current AI Offerings in Google Ads.

Google’s “let us do everything for you everywhere” campaign is called PMax. Give Google your goals, a budget, a bunch of assets (text, images, videos, logos), and Google’s AI then mixes and matches these assets to deliver ads across Search, YouTube, Display sites, Gmail & Maps.

Google’s AI-powered Search-only campaign is called AI Max. No concept of brand, non-brand, match type, etc. AI Max assesses intent in real time and promises to deliver your ads to highly relevant search queries. The key bit: It looks beyond search keywords typed, and analyzes intent to expand your audience.

Demand Gen is a visual option (aka ex-Discovery ads) that uses AI to assess intent and deliver your video ads, Shorts, and other ads.

Two things are happening with all three: A. You control the “reward function.” AKA: What winning means for you. B. AI assesses intent across a significantly wider set of signals than you and I can imagine.

PMax, AI Max, and Demand Gen are not perfect yet. Still, as applications of narrow AI, they are already better than us humans, and getting better every day. Losing three or four times while winning thirty or forty times is a decent tradeoff.

Maturity Assessment Killer Parameters.

Are you truly living in the AI present, or are you a tourist in the present?

To help you do a genuine assessment of horse riding or motorcycle riding, I’ve built a simple maturity model. The self-reflection is powered by two dimensions.

Capability Scoring: How sophisticated are we?

You’ll score your company on a 0–4 scale in each dimension.

Depth Scoring: How widespread is our sophistication?

I’ve included this to help prevent self-deception. We will use relevant spend / conversions / campaigns to assess how widespread.

To prevent self-deception, I recommend:

My model will score your maturity in two steps:

Your destination? A score of 85 or higher.

Google Ads Maturity Model | Six Dimensions.

Our objective: Nearly all business outcomes are optimized by Google, using automated modern intent <> value signals.

The Google Ads ecosystem is complex enough, diverse enough, that you can take many different approaches. Mine is clustered and prioritized based on my experience and what I see a couple years out. Here are my six dimensions:

1. Measurement & Value Architecture.

Weight: 30 Points!

Because: If you don’t know where you are going, any road will take you there – and you’ll be miserable at the destination.

Google’s intelligence and automation can only optimize to the signals (reward function details) it receives. Enhanced conversions improve measurement and unlock stronger bidding; data-driven attribution is the default for most conversion actions; value-based bidding is explicitly about maximizing conversion value such as revenue, profit, or lead score; and Data Manager is now Google’s central layer for activating 1P and offline data.

The one thing you still control is the reward function – what the AI should deliver that’s of value to you. Hence, if your measurement and value architecture is weak, everything above it is built on sand.

> > How to score Capability?

Horse rider tell: “We trust automation, but we still optimize for traffic.”

Motorcycle rider tell: “We let automation optimize to our reward function of what a good customer is.”

> > How to score Depth?

Truth Question: What percentage of business outcomes from Google are measured by modern value signals?

Evaluate depth using share of conversion value or lead volume covered by modern measurement:

0 to 4, see legend above.

Great News: In TMAI #508, I’ve shared my maturity assessment as a working Excel model. Once you punch in your Capability and Depth scores, it will provide a personalized set of actions to bring your company into the Ads intelligence and automation age! If you are a TMAI Premium subscriber, please email me for it.

> > Actions to arrive at the present?

For the measurement and value architecture my model will select from:

A company can have good creative, tidy campaigns, PMax, Demand Gen, and pretty reporting, but still be immature if it is optimizing for the wrong outcome.

2. Search Operating Model.

Weight: 20 points.

The present, riding motorcycles, is about smart bidding, broad match, responsive search ads, and your coverage depth at 100% for AI Max! It is giving up the need to segment by match type, manual bid adjustments (god no!), and human text customization. It includes automated search term matching, final url expansion, and some new controls like brand controls (use them!) and location-of-interest.

> > How to score Capability?

Horse rider tell: “We still believe performance comes from sculpting the account harder.”

Motorcycle rider tell: “We’ve simplified the structure and given the system room to learn. We use controls sparingly, primarily to protect the brand.”

> > How to score Depth?

Truth Question: What percent of our non-brand search budget is still being run with an AdWords-era control mindset?

The search operating model is critical because the most desirable biz outcome: SCALE!

Evaluate depth using share of non-brand search spend.

To simplify, you can use AI Max row, because it covers broad customer intent matching (beyond KWs), creative optimization, and final URL expansion.

0 to 4, see legend above.

> > Actions to arrive at the present?

Based on your score above… To win, my model will identify your personalized actions from:

At the end of this stage, we erase the fantasy that humans can out-manage auction-by-auction intent better than Google’s AI.

The Journey Continues!

The Google Ads maturity model contains four additional dimensions:

These dimensions help you out-smart your competition – while your competition tries out-spend you! TMAI Premium #508 contains the complete scoring logic and automated model for all six dimensions.

TMAI Premium subscribers can simply email me if they can’t find TMAI #508, or would like the automated Excel model to identify if you are a Legacy AdWords Operator (score: 0 – 29) or a Modern Google Ads Advertiser (70 – 84), or… are you the unicorn at 85 – 100!

Tips to Avoid Self-Deception Scoring.

This is not an exercise to convince your CMO that you are an 86. Fooling is not progress. Do a real assessment, even if you keep the scores to yourself.

To ensure zero self-deception I recommend:

The truth will set you free. 😍

Bottom line.

The old game was: Can the human out-manage the account?

The new game is: Can the company feed the machine better truth, better creative assets, better customer value signals, and give it enough room to learn?

The cultural pain of learning the new game is worth it because, depending on your current maturity score…. 3x, 5x, 20x Revenue and Profits await.

Not to mention how much more fun it is to give up the soul-sucking work of the AdWords world. Scores above 69 make work meaningful, even joyous.

Carpe diem.

In an early meeting with a sub team, they shared that the primary success of their Marketing campaign was the metric Cost Per Session.

I’d never heard of it. In. My. Life.

And, I had a couple of decades of experience. I’d worked with the largest companies on the planet. I’d authored two bestselling books on Analytics, in multiple languages. I’d helped invent entirely new Analytics tools!!

The only CPS I knew was Cost Per Sale.

Now, there are plenty of poor metrics. Take Impressions & Views. They are so value deficient, I would call them “things” and not metrics.

[TMAI Premium Subscribers: Please review the invaluable guidance in TMAI #459, #460: Impressions Suck! If you can’t find them, please email me.]

Still, Cost Per Session surprised me by its existence because I could not believe anyone would consider that as the end point of what their job was in Marketing. Just shovel traffic, and do it as cheaply as possible?

☹️

Pause.

Deep breath.

I’m going to come back to Cost Per Session. If you’re in a gun to your head type situation, I’ll share what you can measure instead to suck less. Even more valuable, I’ll share why as Google heads to AI Mode, a focus on Cost Per Session will harm your company exponentially more.

Outcomes Over Activity.

My objective is to protect the CMO from the CFO. As in, ensure that the client is executing a marketing strategy aligned with the awesome vision of the CMO, AND that all that activity is producing results that can withstand a CFO’s strict scrutiny.

This turns out to be a bit of a pain for the CMO, as it forces a bit more discipline than they might prefer and a bit more accountability on the marketing organization than they prefer.

After a period of adaptation, this pays off handsomely by identifying the incremental business impact of marketing to the CFO. Quickly followed by ever-increasing marketing budgets.

A real-life case study of that shift in emphasis.

The most common reports floating around your company likely look something like this:

Sorry. What I mean is Google Advantage+ does a better job of eliciting a higher response rate.
We celebrate this.

We ship more budget to Mountain Park.

If you have an engaged CFO, or legacy-minded CMO, she/he would ask what the outcomes were from the activity above.

No biggie, we add our normal favorites like Revenue and Conversion Rate and share:

More Orders. More Revenue.

Joy!

When you are assessing the impact of your Owned, Earned, and Paid marketing efforts, at the very minimum, go all the way to Outcomes.

I know that sometimes this is hard because you are a B2B company, or you are a B2C company with a longer sales cycle, or you are a pharma company where the Outcome is a doctor writing a prescription.

I still encourage you to go to Outcomes. In all of these cases you can either track the direct site Outcome or you can measure a Micro-Conversion and times it by an Average Lead to Offline Conversion Rate and Average Outcome Value and get a working set of numbers. These might only be 85% accurate, but they are a heck of a lot better than just looking at the Activity!

Now… If you have a CFO who truly cares about Marketing, they will want to fund Marketing to the max. But, they have to look across all company opportunities (Stores, Support, Product, Eng etc.).

Hence, she will ask you for one more thing.

Accountability Over Outcomes.

All of that Marketing was not free.

Google Advantage+ costs you money. Email costs you money.

Start by collecting those inputs. I recommend Campaign Cost and Cost of Goods Sold (what it cost you to make the products your marketing sold).

Here’s that lovely picture.

We want to understand COGS – you can do actual or apply an average percentage across products, none of this will be reported to the SEC and hence good enough is good enough.

Once you have the two inputs into Marketing, you are ready for the Accountability view.

$17k in Revenue minus $7k in Campaign Costs minus the Cost of Goods Sold gives you a Profit from Google Advantage+ AI-powered campaigns of $5k.

You can choose the level of accountability you want to demonstrate to your Marketing Loving CFO.

If you want less CFO love/budget, you can use Return on Ad Spend (ROAS).

[Note: TMAI Premium Members, review TMAI #455: Minimum Acceptable ROAS? 8. It will change your Performance Marketing strategy forever.]

The tables now flip. Google A+ at 2.4 looks significantly worse than Email at 9.6 ROAS.
A different view from both Activity AND Outcomes.

Depending on your company culture: Earning you instant gratitude and thanks from the Paid Media team OR the Paid Media team becoming defensive and…

My recommendation: At least aim for CFO like (if not love).

You can do that by computing Return on Investment (ROI). It subtracts from the Marketing’s claimed Revenue the Campaign Cost.

The tables continue to flip. Google A+ has a big percentage drop to 1.4 and Email has a smaller percentage drop to 8.6.

Don’t stop the accountability train. Aim for CFO love!

Compute Profit on Ad Spend (POAS). You can see the formula above.

With a POAS of of 1.7 Google A+ is significantly worse than Email.

You are not going to believe it; this is not the end of the story.

That comes by holding Marketing to account for the budget, and calculating Profit On Investment (POI).

Time to cry.

For every $1 you are sending to Google, your campaign is sending back $0.70 in Profit. :( If this was your data, you exist to provide employment to Google’s Advertising Sales team.

Email delivers a profit of $5.7 for every $1 in spend.

Does it matter that your Revenue from Google A+ was 12x higher than Email?

This is why every CMO who wants to grow Marketing budgets YOY, every CFO who truly loves Marketing demands their team measure Accountability over Activity.

Here’s the complete picture:

You’d expect your Agency to use the above picture, and more (!), to make decisions on your behalf to drive ever higher POI.

Your VPs of Global Marketing, your CFO, receives something much simpler:

If the culture rebels against Profit or there is simply no way you can calculate it, go one step higher to ROI (P3).

Don’t lower your standards and measure ROAS (but, if you do, the formula is above).

Action: Should You Cut Google Advantage+ Budget?

Yes.

The results clearly illuminate that the way you are spending money on Google (or Meta) you need to immediately stop. In fact, with such poor POI, you should have never started.

Yet.

Recognize that Email only brought you 14 Orders, vs. 173 from Google Advantage+. Sure. You lost a lot of Profit on every one of those 173 orders. But there is the promise of scale.

Google and Meta are successful businesses. They are built primarily on Advertising Sales. That means countless companies are getting high green POI. In this instance, chances are the problem is you, and not the platform.

My recommendation:

[How high is high? That’s the job of your CFO to identify. Oh, and also what’s too low – something clearly missed in the case study above.]

Cost Per Session | Bye, Bye.

At this point, you recognize the utter futility of letting any Marketing team use Cost Per Sale as a Success KPI or a Metric or, dare I say, even an Influencing Variable.

Right?

[Note: Premium members please see: TMAI #448: KPIs, Metrics, Influencing Variables.]

The Cost Per Session of Google Advantage+ was $14.

Stopping your success measurement at the Cost Per Sale KPI might be delivering job promotions to Marketers, ever higher fees/rewards to your Agency, with the Ad Platform laughing all the way to the bank. You saw the POI of a $14 Cost Per Sale above.

If company culture, leadership issues, or a mass hypnosis prevents you from stopping this unprofitable behavior, my advice is to suck less.

Instead of using Cost Per Session, shift to measuring Cost Per Non-Bounced Session.

You received 510 Sessions from Google Advantage+.

A spend of $7,200 translates Cost Per Session: $14.

The Bounce Rate was 52%. 245 Sessions were: I came, I puked, I left (my definition of Bounce Rate). Completely unproductive (especially since you Paid for each of these people to come!). Take them out.

Cost Per Non-Bounced Session: $27.

With these new, more reality reflecting numbers, there is a higher potential that your internal team and your external Agency (if they are aligned with your interests) will look at $27 and say wait, that sounds crazy high for just a Session, maybe we should dig in further and revisit our tactics.

Perfect.

Sucking less.

There is one more reason you should dramatically deprioritize Cost Per Session focus: SEO implications in an AI Search world.

Google recently shared this collection of guidance for how to do SEO for AI Search. Specifically for AI Mode – a ChatGPT or Perplexity type experience from Google. You should read and start to activate Google’s guidance immediately.

[Note: Premium Subscribers, it is crucial to understand the underlying changes to Search – for both Paid and Organic. Please review: TMAI #412, #413: AI Search: The Sky Is Falling!?]

Google’s guidance for better AI SEO has this important, relevant bit:

Google’s saying: Don’t use Cost Per Session. Don’t incentivize Cost Per Session. Don’t focus on one-night stands.

If I did not persuade you to disincentivize Cost Per Session, I hope Google did.

Bottom line.

If you want to support your CMO’s career – because that is also very good for you -, prioritize a focus on Outcomes over Activity.

If you want to really do that, you need to ensure Marketing’s impact is resilient and incremental, prioritize a focus on Accountability over Outcomes over Activity.

It is not lost on me that this is difficult, it demands a ton of smart thinking, a lot of hard work, some uncomfortable conversations. In exchange… You get an AI-disruption-proof career.

Carpe diem.