When a software company files to go public, the coverage writes itself. Someone runs the valuation. Someone counts the retention. Someone always finds the dividend the sponsor paid themselves on the way out, and that one gets the most attention because it is the most fun to write about.

All of that happened to Entrata in June. It filed to list on the NYSE, it is a 23-year-old property management software company out of Lehi, Utah, and Silver Lake owns most of it. The breakdowns were good. Jason Lemkin’s is the one most people in B2B will have read, and it is worth your time.

It runs about 4,000 words and never once mentions the sales and marketing line. Neither did anything else I read.

Quite evidently, i want to talk about the sales and marketing line.

The number nobody quoted to you

Here it is, straight off the income statement. You can check every figure yourself, because it is an audited document sitting on EDGAR for free.

In 2024, Entrata spent 74 million dollars on sales and marketing against 412 million dollars of revenue. That is 18%.

In 2025, it spent 85.7 million against 509.3 million. That is 16.8%, in a year revenue grew 24%.

In the first quarter of 2026, it spent 19.1 million against 143.5 million. That is 13.3%.

Revenue in the quarter grew 23% year on year. Sales and marketing spend grew 5.6%.

Read those numbers again, because it is the whole gist of this piece. Revenue up 23%. The budget that is supposed to produce revenue, up 5.6%.

I have sat in planning rooms where the model ran the exact opposite way, and i suspect you have too. You want 25% revenue uptake next year, you work back from the pipeline you need, divide by your conversion rates, and arrive at a number for media and headcount that climbs faster than the revenue target does. That is the usual path. Growth costs money, and it needs that money in advance.

Entrata is running the other way, and it did not get there by cutting costs. It got there by building a revenue base that mostly does not need an acquisition event.

How they built it

Before i talk about the mechanisms, let me tell you what makes them absolutely necessary.

Entrata discloses its sales cycles in the risk factors. Six to twelve months for enterprise customers, three to six for mid-market. It describes high costs and unpredictability, and says it is often required to spend significant time and resources educating operators whose existing systems are deeply embedded in how they work.

Which means the falling sales and marketing line is not a company that got good at acquisition. It is a company that knows exactly what the cost of acquisition is and structured itself to need less of it.

Now the three mechanisms, and not one of them is a marketing tactic.

They chose customers whose portfolios grow on their own. The filing’s industry section carries the two numbers that matter. New multifamily construction added roughly 2 to 3% a year to the total US unit base across 2023 and 2024. Over a longer window, the top 50 operators on the National Multifamily Housing Council’s list grew the units they manage by 48% since 2020, an 8% compound rate. The market barely grew. The big operators grew a lot, by taking units off smaller ones.

Entrata sells to those operators. 233 of its customers were above 500,000 dollars of annualised recurring revenue at the end of 2025, up from 183 a year earlier, and those customers were 84% of total ARR. When one of them buys a portfolio, the units land on a platform they already run, under a contract that already exists. Nobody had to sell anything.

They made the money arrive with the rent. Every customer on the Operating System is contractually required to use Entrata’s payment solution for payments processed through it. The filing states this twice, once in the glossary and again in the risk factors. Contracts generally run three to five years. So the revenue is not waiting on a renewal conversation or an upsell meeting. It arrives monthly, when residents pay rent, whether or not anyone at Entrata did anything that month.

And it counts. The glossary defines annualised recurring revenue to include those payment processing fees, on the basis that rent reoccurs by nature, and it is careful to exclude the usage fees it judges episodic. Subscription revenue goes up when somebody sells something. This goes up when a customer wins a portfolio.

The fee is also flat per transaction, with credit cards the only exception, so it tracks the number of payments rather than their size. The filing reports rents down 1.1% year on year and 5.2% off their 2022 peak, and almost none of that reaches this line. Vacancy would, and vacancy is the risk the filing actually names.

They split the motion and said so out loud. The company describes a scaled inside-sales team covering mid-market operators, with dedicated teams on the large enterprise ones, and says the point of the split is keeping customer acquisition costs disciplined. It also says expansion inside existing relationships happens at near-zero incremental acquisition cost. That is not marketing language. It is a company telling its future shareholders precisely which revenue it pays to get and which revenue it does not.

I argued a few weeks ago that expansion revenue was never something your GTM team built, that top-quartile retention was mostly your customers hiring people and your contracts raising prices on schedule. Entrata is the version where somebody chose it deliberately. Same mechanism. Opposite intent.

The part I think should bother you

So far this is a flattering read, and i do not want to leave it there, because the filing contains a number that undercuts the story being told about this company.

The attractive statistic in the coverage was the ARPU ceiling. Average revenue per unit is 216 dollars a year. Entrata’s highest-ARPU customers pay around 580. Roughly 2.7 times the average on the same platform, which does look like the entire expansion opportunity sitting in one number.

The filing’s own footnote says those highest-ARPU customers generated approximately 3% of total revenue in each of 2024 and 2025.

It then takes that same 580 dollar figure, multiplies it across all 23.4 million multifamily units in the United States, and arrives at a serviceable market of 13.6 billion dollars.

I am not saying the number is wrong. It is disclosed properly, the footnote is right there, and market sizing in an S-1 is the most optimistic honest thing a company can produce. My read is that a market estimate built on the spending pattern of 3% of your revenue base is a hope rather than a forecast.

There is one more, small, and nobody has mentioned it. Entrata reports gross retention of 99% in 2024 and 97% in 2025. The glossary says that figure excludes property churn, meaning a customer terminating a specific property without penalty when that property changes owner or operator. The reasoning given is that ownership changes sit outside the company’s control, which is fair enough. It still means the retention number has a whole category of loss carved out of it, and the filing says so plainly. Most benchmark comparisons you will see against those figures are not comparing the same thing.

What I would actually take from this

Not the tactics. You cannot force your customers onto your payment rails on Monday morning, and if you sell into a consolidating market you already know it.

The transferable question is narrower and a bit uncomfortable. Work out what share of next year’s revenue growth arrives without a person making it happen, and what share needs a campaign, a rep, a renewal call. Most teams cannot answer that, because the reporting is built around what marketing sourced rather than around what would have turned up anyway.

The second question follows from it. When you last chose a segment, did anyone ask whether those customers were growing? Not whether they had budget, or a pain, or an obvious use case. Whether their own unit count, headcount, transaction volume, whatever your pricing attaches to, was going up on its own. That is the decision doing most of the work in Entrata’s numbers, and it was taken years before any of it showed up on an income statement.

It is also a quieter version of the argument about what unit you charge for. Picking the meter matters. Picking customers whose meter runs up without you matters more.

Where this could be wrong

Quarterly figures are after all quarterly figures. 13.3% is one three-month period, and the full-year 16.8% is the more conservative anchor, which is why I have kept both in front of you.

A company can also under-invest in acquisition and not pay for it until later. New logos still cost money to win, the filing is candid that attracting new customers is a genuine risk, and a business whose growth leans on its customers expanding inherits their bad years along with the good ones. If the large operators stop consolidating, this can go downhill fast.

I have spent a lot of this year reading about GTM teams getting smaller, roles quietly disappearing through attrition, and efficiency claims nobody can substantiate. This filing is the first document i have read all year that shows the efficiency and then shows the machinery underneath it. Not because the company is unusually honest. Because it had to be signed.

Next week i am looking at how next year’s marketing budget actually gets built, and specifically at what gets defunded to pay for the AI line. The 2026 data says the money is coming out of agencies and out of retention. Most teams will make that trade without ever writing it down as a decision.

Every figure above, linked or not, comes from Entrata’s Form S-1 as amended on 11 June 2026, checked on 5 August 2026. It is one long document with no internal anchors, so rather than send you to the same page 17 times I have linked only the four passages worth going to find.

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Monday GTM is a weekly read on how B2B go-to-market actually works, for the people doing the work. No theory, no fluff. Just what holds up when you look closely.

A week ago I told you that your acquisition engine was filling a bucket with a large hole in it. I also doubled down with this and said that churn was a GTM problem and you had sold it to yourself.

Well, I was wrong about this and I need to come clean.

You see, I read the coverage of the 2026 benchmarks and not the actual benchmarks. I wanted to finish my article and schedule it and I needed to tease the next week’s topic. But this week I went and opened the actual reports side by side. The thing I promised you was the most boring finding. And I promise you the correction is a better story than the original.

The number that got your attention

Here is what set the year on fire. Benchmarkit’s 2026 benchmark report, drawn from 342 companies, found median gross revenue retention had fallen from 88% to 84% in a single year. Even the top quartile slipped, 95% down to 91%. They called it the most alarming data point in the whole study, and the industry agreed loudly for about six weeks.

Then you go and find other reports on this.

SaaS Capital surveyed over 1,000 private SaaS companies and put gross retention at 91%, essentially flat. But KeyBanc’s survey said it is recovering, down to 86% in 2023 and climbing back toward 90%. Datadog told its Q1 call that gross retention was sitting stable in the mid to high 90s. HubSpot’s customer dollar retention is in the high 80s and its net retention went up.

Four readings. Four directions. I spent a while trying to work out which one was right and then realised that I was just wasting my time.

Because the through-line stringing all these reports along is a finding no one argued about. It was present in every dataset and it was the one that should have been the headline.

The number you didn’t notice

The ceiling has come down.

Top-quartile net revenue retention used to sit at 120 to 125%. Rob Belcher at SaaS Capital now puts it around 110%. Benchmarkit’s 75th percentile went from 110-111% down to 108%. Different samples, different methods, same direction, no dispute.

Belcher’s read on his own data is the most useful sentence published about retention this year. People are retaining their customers, he says. They just cannot upsell, cross-sell and raise prices the way they used to.

Let this sink in because this is the crux. Your customers are not leaving you. They just are not growing like before.

Which makes it very pertinent for us to ask this question - what exactly was making them grow before?

How did we build the “good ol’ days”

Let us go back to a typical renewal in 2021. Your customer hired 40 people that year, so the seat count increased. Their contract had a 4% increase built in which came into effect automatically on renewal. There was no negotiation. The account manager sent the invoice to someone in procurement, who signed it and your NRR went up to 118%. A great day in the office.

You put that number in your slide and the accolades kept coming.

Belcher did the math no one wanted to do. A 5% annual inflation adjustment, compounded, is roughly half of that NRR. Half. Not from a motion, not from a strategy play, from a clause somebody’s lawyer put in the paper years ago and everybody forgot about.

HubSpot said the same thing about itself, out loud, on its own Q4 earnings call. Customers coming up for renewal saw up to a 5% price increase, and management credited it directly with holding up net revenue retention. That is a public company telling its investors that part of its expansion story is a price rise on the existing base.

And the other half, the seats? Seat-only pricing now sits at 98% median NRR. Below 100. Below the line where the base sustains itself without you selling anything new. Usage-based sits at 108%, which sounds like a pricing lesson and mostly is one, though I got into why the unit matters more than the model a few weeks back and won’t regurgitate it here.

Look at Datadog if you want to see what a genuinely healthy 2026 expansion number looks like. Net retention in the low 120s, up from the high 110s, and the 10-Q attributes it to increased usage growth from existing customers. The product usage increased, not the team size.

So the passive levers were headcount inflation and actual inflation. Customers stopped hiring. Price increases stopped being easy to impose. Both went away at once.

The tough pill to swallow

When these levers stopped working, we hoped for the expansion engine to pick up the slack but there wasn’t one.

There never was one. The metric had been providing for a decade without anyone having to build anything. Then why would anyone build it. Everyone just assumed the engine existed because the numbers kept coming.

I keep thinking about this alongside the MQL, which I went after a few weeks ago for exactly this crime. A number that went up, that everyone reported, that measured effort and got treated as though it measured outcome. NRR is the same con, just told at the other end of the funnel and to a much more senior audience. It measured your customers’ hiring plans and your legal team’s contract templates, and you took it into board meetings as evidence of your GTM motion.

I come with receipts

If you think I am being unfair, here are the two numbers that settle it.

Expanding an existing customer costs $0.80 per dollar of new ARR. Acquiring a new logo costs $1.63. Expansion is, by a distance, the cheapest revenue in your entire business, and it has been the cheapest for years.

And roughly 1 in 5 companies can actually tell you what it costs them. Benchmarkit describes expansion CAC as one of the least-tracked metrics of the last four years, and was pleased that about 20% measured it in 2025. That is the good news version.

Now go and pull up your own org chart. Go on, I am waiting! Try to find the person whose number this is. Mark from marketing carries the pipeline. Sandra from sales is responsible for new logos. Cathy from CS drives the renewals and gets measured on churn. And boy does Cathy have a stressful time with it! When Cathy prevents a cancellation she has had a good quarter. When Cathy grows an account by 30%, she has had exactly the same good quarter. But when Cathy loses a customer, she has had the worst quarter ever which follows her into her yearly appraisal. None of them have an expansion pipeline. Nobody forecasts it and nobody sure as hell gets fired for missing it.

Meanwhile expansion is now 40% of net new ARR at the median, and 44% for the companies growing slowest. Benchmarkit’s own reading is that once it crosses 40%, expansion has stopped amplifying new logo growth and started substituting for it.

So roughly half of your growth number is coming from a motion that has no owner, no target, no forecast and, in four companies out of five, no idea what it costs.

What I actually think

I think most of the retention panic this year has been aimed at the wrong end of the metric, and that a lot of budget is about to go into churn prediction, health scores and customer success tooling to solve a problem the data does not clearly show you have.

What the data does show, consistently, across every source I could find, is that the growth you used to get for free is gone and nothing has been built to replace it.

If you only do one thing with this, do the boring one. Work out what a dollar of expansion revenue actually costs you, and put that number next to what a dollar of new logo revenue costs you. Most teams have never seen those two figures side by side. When you do, the conversation about where next year’s money goes changes on its own, and you will not need me or a benchmark report to have it for you.

The rest of it, the quota, the pipeline, the owner, follows from that one comparison. It just will not happen until somebody makes it.

Next Monday

I am pulling apart Entrata, which filed to go public in May on the back of 233 customers.

Not 233 thousand. 233. Those accounts are 84% of its revenue, in a category whose underlying market grows about 2% a year, and the whole thing is laid out in an audited filing anyone can read. After a piece built on surveys where companies grade their own homework, a teardown of numbers somebody had to sign for feels like the right follow-up.

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Monday GTM is a weekly read on how B2B go-to-market actually works, for the people doing the work. No theory, no fluff. Just what holds up when you look closely.

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Everyone selling AEO right now is only talking about the first step in the buying journey.

Show up in the answer. Be the first name that the chatbot returns when a buyer asks for recommendations in your category. The advice is solid, I gave a version of it myself a few weeks ago, when i spoke about how the decision is already made in rooms you cannot enter, much before anyone fills a lead form.

This week I will take you through what happens after you have entered the chatbot’s answer.

Because the research house that documented the shift in discovery process, went back to the field and found something the first one did not. Evaluation has overtaken research as the longest stage of the buying journey, at 40% against 36%. The bottleneck did not disappear when AI made discovery easy. It moved ahead to a place your content strategy could not reach.

The room you are not in

The shortlist of vendors is created exactly how everyone says it does. Someone asks a question to an AI chatbot, gets yours and 3 other names back, adds them to a document. Yayy! You have won. Aced your AEO strategy.

Then the document goes to security review. 39% of buyers name IT security review as the single biggest source of delay after a vendor is selected, and among enterprise buyers that rises to half. Budget approval follows at 32%, implementation planning at 25%.

None of those people saw the AI answer. None of them care that you were in it.

Your champion is now doing something no AEO playbook prepares them for. They are defending a choice, internally, to colleagues who were not there when it was made, using material you did not give them.

The two who kill your deal

The first is finance.

49% of buyers say their CFO vetoed an already-approved software purchase in the last 12 months. This means that the CFOs are now reversing purchases that the buying team thought was settled. Separately, and this is a different finding rather than the same one restated, finance involvement in software decisions rose from 31% to 46% in a single year.

Buyers who have been through a veto now expect positive ROI within 6 months at a rate of 3 in 4. They also push for contract terms under 12 months at more than double the rate of buyers who have not, 40% against 18%.

The second killer is newer and I think more interesting. Buyers citing internal resistance to AI adoption as a concern rose from 16% to 29%, which the study flags as its largest single-year shift.

Read that alongside the argument I made about the MQL and the picture is clear. The buying group did not just get bigger, it also grew a sceptic who does not want another AI tool in the stack. And their objection is not about your product. It is about your category.

Take these numbers with a grain of salt

I want to be straight about these studies.

Both of those buying studies are G2’s, and G2 is not a neutral party. It sells buyer-intent and visibility tooling, and it is itself a review site. So when the July study reports that review sites have edged ahead of AI chatbots as the top influence on shortlists, 38% against 37%, treat it accordingly. That it is a one-point gap between two different samples 3 months apart, published by a company that owns review sites. My reading is that the two channels are tied, not that one overtook the other.

More importantly, the CFO veto figure has no independent corroboration that I could find.

And that is what I want you to read between the lines. The pre-contact stage of B2B buying has many independent studies. The post-selection stage has essentially one. Nobody is measuring what happens between “you got shortlisted” and “you got the deal” which is precisely why the visibility market can keep selling you the first half of the problem without ever being asked about the second.

Two independent checks do hold. TrustRadius, surveying 1,862 buyers in January, found that 63% used AI in the purchase journey while 94% of those fact-check its answers, 72% always or very often, and 47% now trust online resources less than a year ago. Different company, different sample, same conclusion: being in the answer is necessary but it is nowhere near sufficient. And Forrester puts procurement professionals as decision-makers in 53% of buying cycles, engaging from the start, with more than 60% of buyers now running a trial before committing and 78% on purchases above $10 Mn.

TrustRadius is a review platform too. I am not pretending otherwise. Its relevance here is that it is a separate sample reaching the same inference, not that it does not have any skin in the game.

What the report says to do about it, and why I think it is wrong

G2’s own conclusion is that you should arm the buyer with proof and counters to its objections. It sounds obviously right, and it is also an argument for buying more of what G2 sells.

Here is my counter arguement. The rest of the evidence says vendor-supplied proof is exactly the thing that gets discounted now.

94% of AI-using buyers fact-check what they are told. 47% trust online resources less than they did a year ago. When buyers rank what actually influences them at the point of selection, demos, free trials, prior experience and peer reviews come out on top. Every one of those is something the buyer experiences or hears from someone who is not you.

So a beautifully built ROI deck, authored by the vendor, handed to a CFO who reversed a purchase last year, is the least persuasive object in the room.

The useful version of the advice is narrower and hits harder. Your champion does not need your proof. They need to either experience it or hear it from someone else who experienced it.

Documentation beats any argument. A SOC 2 report, a sub-processor list, a completed standard questionnaire, a penetration test summary. These work not because they are persuasive but because they are verifiable. Security review is the biggest post-selection delay in the data, and most vendors treat it as a sales-engineering task that begins after the demo. Put the pack somewhere your champion can forward without having to reach out to you first.

Give them the model, not the answer. Evaluating total cost of ownership is the joint-top task buyers now hand to AI agents. Your cost is getting modelled either way by an AI agent. An open model your champion fills in with their own numbers survives scrutiny.

Make the trial the proof. More than 60% of buyers now run a trial, rising to 78% above $10 Mn. A trial is the only evidence in this list the buyer generates themselves, which is exactly why it carries the weight a case study cannot.

Find them a peer. The single hardest objection to answer is the internal sceptic who does not want another AI tool. A twenty-minute call with someone who bought it last year and is a credible voice in the buyer’s industry can placate most of the sceptic’s doubts.

To be clear about what this is: the facts are from the research, the disagreement with the recommendation is mine.

There is a fifth thread in this data I am leaving alone. The same study shows real shifts in how software gets priced, and I have tackled this previously.

I know that I have added a lot your already endless to-do list. Let me unburden you a bit. If there is only one item that you can work on then make it the security pack. Because it unblocks the longest delay and the work is mostly assembling documents your company already has rather than creating something new.

The bit that should bother you

There is a scenario where you did everything right and still did not land the deal. You got into the AI answer. You made the shortlist. Your champion genuinely fought for you at the meeting. But then a security questionnaire got lost in someone’s queue for weeks and the finance head asked what the 6 month ROI looked like. And your champion was mute because you did not give them the answer. Unfortunately, this is the likely scenario.

The industry has spent the entire year focusing on and buying visibility and it does not look like they will stop anytime soon. Ironically, this is also the year visibility stopped being the constraint.

Being in the answer is great. it will get you considered but it won’t build your case for the decision makers.

The market is still selling the first whereas the deals are dying because of the second.

Next up: the 2026 benchmarks say your acquisition engine got more efficient while your customers left faster. Churn is a GTM problem, and you sold it.

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I spent the larger part of last week reading reports about how GTM teams are being rebuilt and came out of it more confused than when I started. And i don’t feel so good admitting this in a newsletter that claims to tell you how things really work.

Here’s what i think is happening.

Your GTM team probably got smaller in the last year. There were no layoffs. No reorg deck, no all-hands meeting, not even the difficult email from the CRO. Someone left in March and the role was never posted for hiring. A vendor’s renewal came and went without any action. The junior content writer’s role which finance approved, is still at that stage.

This is the 2026 headcount story.

The cuts have already happened

Wynter surveyed 100 directors, VPs and heads of marketing at mid-market and enterprise SaaS companies in May. Nearly half said they had already cut or reduced marketing roles because of AI. The part worth noting: most of these cuts were not layoffs. Companies just stopped filling open jobs and let attrition take its natural course to shrink teams over time.

No layoffs means no public announcments and no announcements means no decisions that have to be publicly defended.

Which functions were affected? Content and copywriting led at 60%, then design and creative at 37%, product marketing at 26%, junior and entry-level roles at 20%, marketing operations at 19%, analytics at 18%. Several respodents described senior marketers using AI to do work that used to take multiple junior people or agencies.

This is the crux. The work did not disappear. It just got upward delegated. A senior marketer now does their own job plus the execution that used to sit with the layer beneath them.

Everyone was watching the wrong role

If you have followed this story at all, you have heard it as a sales development story. The Sales Development Representative (SDR) is dead, AI does prospecting now, the Business Development Representative (BDR) job is over.

That story is real, and it is mostly about 2025. Emergence Capital surveyed 560+ venture-backed B2B software companies in April 2025 and found 36% had decreased SDR/BDR headcount over the previous year, the highest reduction of any sales role, with only 19% increasing. But 44% kept their teams exactly the same size, which is not what an extinction event looks like.

Move forward twelve months and the picture flips. ICONIQ’s 2026 data on 150+ B2B software GTM leaders finds hiring concentrated in Sales and Post-Sales, growing roughly 10-20% depending on company scale, while Marketing and RevOps expand more slowly and in some cases stay flat, because leaders are betting on AI tooling, process and agency support before headcount.

Sales is still hiring. Marketing is not. And on the SDR question specifically, ICONIQ does not claim to know the answer. They frame it as open: if a BDR becomes 5x more productive, do you hire fewer of them, or expand the role and hire more?

So the role everyone was watching is still an open question, and the role nobody was watching is getting erased.

The part that bothers me

Every one of these decisions rests on a premise: that AI is delivering enough leverage to justify the smaller team. It is worth asking whether that premise is proven.

ICONIQ says yes, emphatically. The most AI-forward, high-performing companies run GTM teams 20-30% leaner than their peers, and high adopters generate roughly 2x net new ARR per GTM FTE, $640K against $370K.

Emergence Capital published Beyond Benchmarks 2026 in late June and reached a different conclusion. Their headline: AI is not yet delivering the efficiency gains most expected, and across every segment, non-AI companies still generate more revenue per employee. AI remains an investment story more than a productivity one.

Two credible firms. Both published within about a month. Pointing opposite directions.

My conscience requires me to say these are not measuring the same thing, and anyone telling you this is a clean contradiction is either wrong or selling something. ICONIQ measures net new ARR per GTM head, sorted by how much AI a company has adopted. Emergence measures revenue per employee across the whole business, comparing AI-native companies against everyone else. Different metric, different population.

But look at how each one was built. ICONIQ asked GTM executives to describe their own organisations. Emergence went to Carta, Ashby, Pave, Stackpack and Standard Metrics for proprietary data across thousands of operating companies, and were explicit that this was not survey data and not sentiment, but actual software spend, actual comp decisions, actual hiring outcomes.

One dataset is what leaders say is happening. The other is what their hiring systems recorded. When those two disagree, I know which one I trust more.

Both firms are venture investors with money in the outcome, which is worth calling out loud. ICONIQ’s own disclaimer notes that no information on conflicts of interest is contained in the report, and that those conflicts may be significant. Emergense has the same exposure in the other direction. I am not accusing either of painting an incorrect picture, I am highlighting the footnotes of these reports.

One more thing that cuts against my own argument, because leaving it out would be dishonest: the analyst behind the Standard Metrics data has pointed out that revenue per employee is growing faster at AI companies than at non-AI ones. If that holds, the gap closes and then flips. The efficiency case might simply be early rather than wrong.

What I actually think

I do not think anyone knows yet whether the lean GTM org works. That is an unsatisfying thing to publish but it is where the evidence lands.

What I am confident about is narrower. The org charts are already being redrawn as though AI has proven to be efficient. Roles are going unfilled on the back of a productivity claim that the hardest available data does not yet support. And because it is happening through attrition rather than layoffs, there is no moment where anyone has to share a justification.

If a company decides its marketing team should be 30% smaller, that is a strategy, and strategies can be argued with. If a company arrives at a 30% smaller marketing team through eighteen months of not replacing people, that is not a strategy. It is a drift that only becomes visible when something breaks.

So the question worth asking in your next planning cycle is not whether to cut. It is whether anyone can actually prove that AI is efficient enough to justify the cut.

Go and count. Not headcount against budget, but the work. Every job that used to belong to someone who left, and where it sits now. My guess is that most of it landed on whoever was closest and senior enough not to complain, and that nobody has looked at that list in one place.

Next Monday: you got shortlisted by ChatGPT and still lost the deal. What happens after the citation.

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There is a cafe in Paris where I worked most of my mornings in May, and it took me till my second outing to notice something strange about my coffee.

The same espresso had 3 different prices. One, if you drink it standing at the bar. Two, if you sit at a table indoors. And three, if you wanted it at the terrace with the view of everyone walking past. This was written on the board and no one found it odd. The cafe was not confused about what it sells. It sells coffee, but it sold the chair and the view separately and decided to charge for both.

This was the first thing that popped up in my head when I spent the day reading about people announcing that seat based pricing is dead.

All announcements were the same, AI has killed the seat. Everyone is charging for the outcome now. My guess is that a lot of this is coming from companies that edited their pricing page with the trendiest words.

So let me do the dirty work and look at what the loudest companies are actually charging.

What the numbers say

Kyle Poyar’s 2026 State of B2B Monetization report, which surveyed 230 software and AI companies across April and May, found that the most common pricing model in B2B right now is hybrid, at 37%, up from 25% a year earlier.

Read that word slowly. Hybrid.

Not usage. Not outcomes. Hybrid usually means a seat, and add-ons. This means that the per-seat model has survived. The fastest growing B2B pricing model is the one where you still charge for access and then charge for consumption on top of it. Which is significantly different from the headlines.

One product, 3 pricing models

Here is the part that made me write this article.

Salesforce sells Agentforce on three pricing models at the same time. There is $2 per conversation. There are Flex Credits, where a standard action costs 20 credits, roughly $0.10, bought in blocks of 100,000 for $500. And there is a per-user licence at $125 per user per month, sold as separate add-ons for Sales, for Service, and for Field Service. All of them for one product. Shipped across roughly eighteen months.

Those are the facts and they sit on Salesforce’s own pricing page. What follows in this article is my take on them, and I want to keep the two apart.

A company does not have 3 pricing models for one product because it has 3 mutually exclusive customer groups. It does this because it has not settled on what the unit of value is and running all 3 at once is cheaper than being wrong in the market. And that is a logical thing to do in a market that is so nascent. It is just hard to digest when this is done by the company that has been so outspoken about their adoption of the concept of “digital labour”. This phrase only holds meaning if the per-seat model is really going away and being replaced by pure outcome based pricing.

And then in June it went further. On 15 June, Salesforce signed a definitive agreement to acquire Fin, formerly Intercom, for about $3.6bn. Fin charges $0.99 every time its agent resolves a conversation. So the largest software company in the category spent $3.6bn on an outcome-priced agent while continuing to sell a $125 seat for its own. The deal is not closed yet, completion is expected in Salesforce’s next financial year, but this is a direction they are moving towards.

The counter-example

If per-seat pricing was genuinely on its way out, you would see the biggest AI product in enterprise software to be priced in a different way.

And that’s not the case. Microsoft 365 Copilot, at the enterprise tier, is $30 per user per month, on an annual commitment, sitting on top of a qualifying Microsoft 365 licence. Per user. Per month. The most widely deployed AI product in corporate software is sold per seat, and it is sold that way because seats are recognisable to a procurement team in a way that a credit balance is not.

What an actual revamp looks like

Two companies have done the thing everybody claims to have done.

Sierra gets paid only when its agent resolves the customer’s problem without human intervention. Not per conversation, not per user, but per resolution. Bret Taylor’s argument for it is that salespeople have always been paid on commission, so it is strange to treat software as the exception. Sierra reached $100M ARR in under two years, which is what takes this from being an interesting philosophy to a working business model.

Fin does the same at $0.99 per resolution, with a fifty-outcome monthly minimum and no volume discount. This model only works if the agent actually resolves things at a high enough rate to be worth someone’s budget. Salesforce markets Fin at around a 76% resolution rate, though different sources claim it to be much lower. This is another small example of the gap this whole piece is about - the number a company puts out and the number it actually delivers.

Notice what both of those companies gave up. If you charge per resolution, a quiet month for your product is a quiet month for your revenue. You have handed the customer a lever that used to be yours. Nobody does that as a marketing exercise. It costs too much when it goes wrong.

That is the difference between the two groups. One changed what it is willing to be measured on. The other changed a heading.

The test

If you want to know which side of the line you’re on, there is one question you need to ask.

Has the unit you charge for changed, or only its name?

If you renamed seats to workspaces, or licences to digital workers, and the invoice still goes up when the customer hires someone, you have not revamped anything. You have just redecorated. There is nothing wrong with that as long as you don’t tell yourself or publish it as a pricing model revamp.

If your revenue now moves when the product does its job well, independent of headcount, then you’ve rebuilt. And you will already know it, because the first quarter of it was probably horrible.

The bottom line

Earlier in the trip, in Geneva, I stood in front of a shop window of watches with no prices on any of them. Not one person buying these watches is calculating the cost per hour of accurate timekeeping. The unit is not the point here.

Most companies currently panicking about per-seat pricing do not have a usage problem. They have a value problem. If your customer cannot clearly say what they are paying you for, changing the unit will not solve that. You will just confuse them with a new metric, and you would have spent two quarters of IT time to do it.

The Paris café can charge three different prices for one espresso because everyone walking past understands precisely what the third one buys. Very few software companies can say the same thing about their own pricing page. That is the actual problem.

Next week

I’m looking at what happened to the go-to-market team itself. Headcount across GTM teams is down, output is up, and the seat being cut is the SDR. The story being sold is that AI simply does that job now. I don’t think that’s quite what happened, and the numbers underneath it are more interesting than the story on top.

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I was in a quarterly review once where a marketing guy put up a slide with a big green number, MQLs are up 40% this quarter. There were a few nods around the table. Then someone from the sales team, who clearly wanted to do this all along, asked the most obvious follow up question - “How many of them led to closures?”. The room went silent because everyone knew what the answer was but didn’t want to say it out loud.

This meeting came back to me now because last week my feed was filled with articles declaring that the MQL is dead. But what struck me was that these confidently worded articles were written almost entirely by companies that were dying to sell you whatever they declared to be the replacement.

Here’s what I want to achieve with this article - tell you what really died, what didn’t, and if the suggested replacement is worth our time at all.

The number that buried the MQL

In a traditional B2B lead funnel, the typical conversion from inquiry to closed won is under 1%. That’s lesser than one deal for every 100 people who showed interest.

Now step back and think how you would feel about this number in any other scenario. If your factory scrapped 99 out of the 100 units it produced, you would not be putting money into producing more units. But marketing teams did exactly this for years and got away with it because the MQL did have one redeeming quality - it was quantifiable. You could put it on a slide, the more budget you spent it would climb further up and your weekly reviews were sorted. Now your quarterly reviews, they were managed by quieter conversations you had in smaller rooms.

The MQL does not lie. It is a measure of marketing effort. But effort does not always translate into revenue.

What actually died

The MQL shouldn’t be done away with. You absolutely need to know how many inquiries were registered.

What should go is the assumption that one lead is one buying decision.

Let me paint a picture of how a B2B purchase happens. Someone in operations hits a wall and rants about it in Slack. A colleague recommends a tool they vaguely heard about in a conference. A third person posts the competitor’s tool which is more lucrative. Two weeks later finance is in loop as the cost is out of budget. All of this has happened before the vendor even knows the company exists. By the time one of them in this chain fills the lead form, the decision is already made and the people who were a part of it will not be captured by anyone’s CRM.

The MQL points towards the one person who downloaded your brochure or filled the lead form, scores them and ships this off to sales as the key decision maker, oblivious to the others who were part of the decision.

The average B2B purchase decision now runs through 13 internal stakeholders and nine external influencers, per Forrester’s State of Business Buying 2026. Before you write that off as admin bloat, the same study found 94% of buyers in groups of six or more say the size actively helps them - more eyes, easier validation, a smoother path to sign-off. Big groups are not the problem. They are exactly how corporates cushion themselves by having more people sign on the dotted line.

The MQL did not become irrelevant because the scoring got tough, it was because it attributed itself to one person for a decision that was made by an entire committee.

The part the obituaries skip

This is where I get to call out what the sponsored articles won’t.

The replacement being offered is the MQA - Marketing Qualified Account. Stop scoring the person and score the whole account instead. Sounds like the obvious fix doesn’t it.

The reality is far from that.

Here’s the detail everyone conveniently leaves out of their slides - almost nobody uses the MQA. In 6sense’s own 2025 metrics survey, just 7% of demand gen teams use MQAs as their primary success metric, and only 13% take ABM-sourced closed-won revenue to the board. The metric that’s supposedly replacing the MQL is mostly a thing people say on stage and not really reporting to their stakeholders.

It gets worse. The MQL at least comes with a brutal, honest number attached, the under-1% from earlier. The MQA has no published conversion benchmark at all. So the pitch is: drop the metric that’s proven to fail, adopt the one that hasn’t shown any proof of it woking, and buy a platform to run it. This is, crudely put, the actual argument in the articles.

And look at who’s making it. The “MQL is dead” line has been pushed hardest by the vendors, 6sense and Demandbase and Terminus among them, who sell the account-based machinery you’d need to report the new metric. Even Demandbase, the name most attached to the MQA, eventually published a piece admitting the industry may have ditched the MQL too fast, and now suggests running both at once.

I’m not saying all critics are wrong. The good ones are right, which is what makes this hard. Jon Miller co-founded Marketo, the company that turned the MQL into an entire industry, and he now calls the old “budget in, MQLs out” model a gumball machine that stopped paying out. Kerry Cunningham, who spent years as a SiriusDecisions analyst, gave the whole apparatus its name: the “MQL-Industrial Complex”, the tools and benchmarks and agencies all profiting from lead production whether or not revenue ever gets booked. That’s a real and useful description.

Know where Cunningham works these days. 6sense. He isn’t wrong about the disease. He just also happens to sell the cure. Both of those can be true, and in a market this cluttered, figuring out who has their skin in the game in the thing they’re telling you is the real skill.

What to do now

Stop counting all the form fills, white paper downloads and webinar attendees as genuine leads. Look for the account where 3 or more people are conveying the same problem within the same few weeks. That cluster of finance, operations and the eventual lead all implying the same thing is the only signal that can predict a deal.

You don’t need an expensive platform to begin. Buy the tools only when you know what you want to track, not before it.

The MQL maybe dead. Just don’t take the obituaries at face value when the person giving it is also selling you the replacement.

Next week

The metric fight has a sequel, and it’s about money. Next week I’m getting into B2B pricing: how “seat-based pricing is dead” became the most repeated sentence of 2026, who’s genuinely rebuilt their model versus who just rewrote their pricing page, and how to tell which side you’re on.

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There’s a specific kind of envy that hits when you watch a company grow without doing the thing everyone told you growth requires.

No floor of SDRs. No sales org burning through money to hit quarterly targets. Just a tool that most people will tell you is genuinely hard to learn, surrounded by a crowd outside the company who talk about it constantly, for free, in public. If you build go-to-market for a living, that crowd is the part worth staring at. The talking is the distribution. And it wasn’t an accident.

The lazy version of this story is that Clay built a great product and a community happened. That’s not what happened, and the real version is more useful to steal from.

The overnight success that took eight years

Everyone repeats the same headline number. Clay went from around $1M to $100M in ARR in roughly two years, then raised a $100M Series C at a $3.1B valuation in August 2025. Over 14,000 customers now, OpenAI and Anthropic and Canva and Rippling among them.

Looks like a rocket. It wasn’t. Clay says that two-year sprint sat on top of six years of slower product work where they were mostly still working out what they were building. So the question that matters isn’t how they grew fast, because lots of companies grow fast and lose money doing it. The question is how they grew that fast on a fraction of the acquisition spend you’d expect at that stage.

They stopped treating customer acquisition as their job, and handed it to people who didn’t work there.

They went looking for the loudest people on purpose

This is the part almost nobody copies, because it doesn’t look like a growth tactic.

When co-founder Varun Anand went hunting for Clay’s ideal customer, he didn’t book a positioning offsite. He sat inside a sales community, read the threads, and pulled out about thirty people who clearly knew what they were talking about when it came to enrichment and outbound. He talked to them. What came back was that cold email agency owners were the most vocal of the lot, the most technical, and the most openly frustrated by the exact problem Clay existed to solve.

That became the beachhead. Not “B2B SaaS.” Not some industry vertical. A small, specific tribe who built their living on Clay’s problem and, conveniently, talked for a living too.

Then came the move that actually spins the flywheel. Clay brought in Eric Nowoslawski, a sales influencer the agency world already listened to, and let him build things in front of everyone on LinkedIn. Agency owners watched a peer make something they wanted, and signed up. New users didn’t just get a login either. They went on a waitlist, got vetted, and were told to turn up to onboarding with a real dataset or a real problem to solve live. So the first thing a new user ever did in Clay was fix something that genuinely mattered to them, with someone from Clay watching.

Sit with the difference for a second. Most tools land a customer and then spend a year nagging them to actually use it. Clay landed customers whose actual job was recommending tools to other people.

The product is hard, and that turns out to be the point

Here’s where it looks like a flaw.

Clay is not easy. Reviews keep landing in the same place: a steep curve, two to four weeks before you’re building workflows that hold up. One partner guide puts the difficulty somewhere near implementing Salesforce. Credits drain fast. You can lose a week to a table that ends up doing nothing.

Normally that’s a churn problem. Here it’s the engine.

The difficulty opens a gap between what Clay can do and what a normal person can actually get it to do, and that gap has to be filled by somebody. The somebody isn’t on Clay’s payroll. It’s agencies, freelancers, people selling courses, bootcamp grads, all of them earning a living off being the person who can make Clay do the impressive thing. Clay puts its ecosystem at 108 agencies and around $50M a year flowing to its data and integration partners. Those are Clay’s own figures, a company quoting its best numbers, so take them with the usual pinch. But you don’t need to trust the exact count to see the shape of it from outside. There’s a whole little economy of people who earn more, the more Clay matters.

Each of them is a salesperson Clay never hired. Each client they sign is a Clay seat Clay didn’t have to sell. And every “here’s how I built this” post is content marketing Clay didn’t write.

This is the whole game, and it’s the bit most teardowns miss. Clay didn’t acquire customers. It created practitioners, and the practitioners went and acquired the customers.

Then they named it, and a job appeared out of thin air

Most companies would have parked it there, pleased with a tidy partner programme. Clay did something weirder. It gave the work a job title.

In 2023 it started calling this person a GTM Engineer, someone who builds revenue systems instead of working on a prospect list by hand. It sounded like marketing, and honestly, early on it was. But putting a name on something is how you drag it into being.

Look at what followed. By January 2026 there were more than 3,000 GTM Engineer roles live on LinkedIn, for a job that barely existed three years earlier, growing at 205% year on year. Someone went through a thousand of those postings and found a median salary near $127,500, sitting above what a senior RevOps hire pulls.

Not every one of those jobs runs on Clay. Plenty use a different tool, or a whole stack of them, and the role would exist in some form even if Clay had never named it. But Clay did name it. The entire market now recruits in vocabulary Clay invented, and in that same analysis of a thousand postings, Clay is the single most common tool in the list. You don’t have to own every job to win when you wrote the job description everyone else borrowed.

Now read that back as a GTM person. Clay put a name on a way of working, and the market picked the name up. That alone is worth more than it looks. The vocabulary a market hires in shapes which tools sit at the centre of the job, and Clay made sure it sat at the centre of this one. It is the most common tool in those job postings, and it turned proficiency in itself into a certifiable skill, a credential for a job it helped name.

What you can actually take from this

Most of us are never going to invent a profession, and that’s fine.

Start with who you seed. The reason Clay’s seeding worked is dull when you say it plainly: the first users talked for a living. So when you pick an early ICP, lean towards the ones with an audience and a reason to use you, not just the ones with the sharpest pain. A smaller segment that posts beats a bigger one that stays silent. Treat distribution as something you select for at the start, not something you bolt on once the product’s built.

Then there’s the difficulty thing, which runs against every instinct you have. The reflex with a hard product is to sand it down until anyone can use it. Sometimes that’s right. But the friction is also exactly what lets other people build a business on top of you, and those people turn into a salesforce you don’t pay. If your product mints experts, give them somewhere to stand and something to win. A directory. A certification. Status they can wave around. The mistake is competing with your own ecosystem instead of handing it the ammunition.

And last, name the thing. People won’t organise around a feature. They’ll organise around who it lets them be. Clay never told anyone to “use the product more.” It gave them a title for their LinkedIn headline and a room full of people doing the same work. If your product implies a new way of working, naming it costs almost nothing, and the downside is just that nothing happens. The upside is recruiters writing your positioning for you.

None of it needs a $3.1B valuation behind it. It needs you to decide that your actual job is making other people look good in public, and then getting out of the way while they do.

Next week

Next Monday I’m going after the MQL. Specifically the bit where everyone declared it dead and then rebuilt it under a new name. If you still report on lead volume, read that one first.

If you got something out of this, subscribe. Every Monday I take apart how go-to-market actually works and tell you which bits you can use.

Monday GTM takes apart how B2B companies actually go to market, every Monday. Teardowns, playbooks, and the occasional hot take. No fluff, no recycled LinkedIn wisdom, just the moves you can steal.

It is late, I am writing this in bed with a laptop that is slowly cooking my legs, and I have just spent twenty minutes doing the exact thing this whole piece is about. I needed a tool. A boring one, scheduling software. And without thinking I opened a chat window, typed “compare the three best options for a small team,” read the answer, and picked one. I did not visit a single vendor website. I did not fill in a form. Nobody in any marketing team anywhere has the faintest idea I just chose them.

That is the whole problem in one small bedtime story. And if it is how you buy, it is how your buyers buy too.

So here is the hot take, and I will say it plainly before I back it up. Your funnel does not start at the top. It starts before the top, in a place you cannot see, and by the time anyone shows up in your analytics the important part is already over.

Let me show you what I mean, because this is the kind of thing that sounds like a slogan until you watch it happen.

The meeting that already happened

Picture a real buying decision. Not the tidy one in your funnel diagram, the actual one.

A product manager at a mid-size company has a problem. On Tuesday she asks an AI assistant to lay out her options. It gives her four names and a quick pros-and-cons for each. She drops two of them into her team’s Slack with a “anyone used these?” One colleague replies with a name that was not even on the list, because his old company ran it and liked it. Someone else posts a link to a comparison they saw on a podcast. A week later, in a planning call, she says the words “I think we should look at [vendor],” and the room nods.

Notice what has happened here. A decision has formed. A favourite has emerged. A name has been spoken out loud in the meeting that actually matters. And not one piece of it touched anything you could track. No ad click. No form. No “how did you hear about us.” When she finally lands on your pricing page two weeks later and your analytics logs a tidy little “direct” visit, the work is finished. You are not at the start of her journey. You are at the end of it, watching the closing credits and calling it the trailer.

This is not a fringe behaviour. It is the median. Forrester’s State of Business Buying, 2026 found that 94% of B2B buyers now use generative AI somewhere in their buying process, up from 89% the year before. And the more telling number underneath it: buyers named AI and conversational search as a more meaningful source than vendor websites, product experts, or sales reps. Read that twice. They trust the machine’s summary of you more than they trust you.

The number that should ruin your quarter, gently

Here is the stat I would put on a poster if I were running your marketing team.

According to 6Sense’s 2025 Buyer Experience Report, the winning vendor is already sitting on the buyer’s shortlist on day one, ninety-five percent of the time. Roughly four out of five deals go to the vendor the buyer already preferred before a single sales conversation took place. Buyers do reach out a bit earlier than they used to, sure. But they reach out to confirm a choice, not to make one.

And before you tell yourself this is a one-year blip, it is not. The 2024 version of the same study found 81% of buyers already had a preferred vendor at the moment of first contact, with most of their requirements locked before they ever raised a hand. Two years running, same result, thousands of buyers each time. This is not a trend you can wait out. It is just how buying works now.

Sit with what that does to your pipeline maths. Your SDRs are calling people who have, four times out of five, already quietly decided. Your “request a demo” form is not the opening of a relationship. It is a buyer double-checking a decision they made in a Slack thread you will never read.

So when your sales team grumbles that the leads are bad, they are half right and entirely wrong. The leads are not bad. The leads are late. Lead quality is usually a lead timing problem wearing a disguise.

Where the journey actually lives

Marketers gave this hidden territory a name a while back. The dark funnel. It is an ugly phrase but a useful one, and it covers everything that shapes a buying decision without leaving a trace you can measure. Slack and Teams chats. WhatsApp forwards. A name dropped at a dinner. A podcast. A review site read in an incognito tab. And now, sitting on top of all of it, an AI assistant quietly assembling the shortlist.

If you want to see the size of it, look at where traffic to the most aggressively marketed companies in B2B actually comes from. Similarweb pulled the numbers and the share that arrives as plain “direct” traffic, with no traceable source attached, is staggering. Gong, 72.1%. HubSpot, 71.6%. Outreach, 71.1%. Salesforce, 64.5%. These are companies with enormous, sophisticated marketing machines. And for most of them, two-thirds of the people showing up arrived through a door nobody can see.

“Direct” does not mean someone typed your URL from memory. Mostly it means attribution gave up. It is the label your analytics slaps on a visit when the real story happened somewhere it could not follow. Two-thirds direct traffic is not a measurement success. It is a measurement white flag.

And the dark funnel just got a powerful new resident. Those AI research tools are not a niche habit anymore. The top few of them now pull billions of visits a month between them, with one of the popular ones, Perplexity, growing its traffic roughly seven times over in two years. Your buyers are doing their most important research inside a system that has no record of your sales funnel, no retargeting pixel, and no obligation to send anyone your way. You cannot buy your way in. You cannot retarget your way in. You are either in the answer or you are not.

It is not as tidy as “the robots decide now”

I want to be careful here, because the lazy version of this argument is “AI runs B2B buying now,” and that is not quite true. It is more interesting than that.

Buyers do not trust the machine blindly. The same Forrester work found a real streak of scepticism about AI accuracy. Some buyers came away more confident for having used it. A meaningful chunk came away less confident, because they hit answers that were wrong or thin. So what do they do? They take the AI’s shortlist and they go validate it with humans they trust. Peers. Old colleagues. A Slack community. A friend who ran the thing two jobs ago.

Which means the buying group you are actually trying to win over is bigger and more scattered than your CRM will ever show. Forrester puts the typical decision at thirteen people inside the company and nine outside it. Your form captures one of them. The other twenty-one are off in the dark, talking about you in rooms you are not in, using language you did not write.

So the funnel does not begin with an AI answer either. It begins with a question a buyer asks a machine, and then a second question they ask their network to check the first one. The whole decisive stretch happens between those two questions, in the dark, before your analytics records a thing.

So what do you actually do about it

This is the part where a worse newsletter would tell you to buy an attribution tool that promises to “illuminate the dark funnel.” Do not. You cannot floodlight a dark funnel. The whole point is that it resists tracking, and chasing perfect attribution into private Slack channels is a way to spend a year and a budget learning nothing.

The shift is harder and simpler than buying software. You stop trying to measure the invisible journey and you start trying to influence it. A few honest moves:

Get into the AI answer. When a buyer asks an assistant to compare options in your category, you want to be one of the names it returns. That is a content and reputation job, not an ad-buying one. It means publishing things specific and clear enough that a machine can extract you and a human can verify you. Vague thought-leadership soup does not get cited. A sharp, sourced, genuinely useful piece does.

Be mentionable in the dark. The currency of the dark funnel is the offhand recommendation, the name someone drops in a thread because you were useful to them once. You earn that by being worth mentioning, not by being easy to track. Communities, real expertise out in the open, a product people actually like talking about.

Ask the one question your analytics cannot. On every discovery call, every form, every won-deal review, ask it plainly: how did you really first hear about us. The answers will not match your dashboard, and that gap is the most honest market research you will ever get for free.

Stop judging your channels on a thirty-day last-click window. If most of the journey is invisible and the typical buying cycle runs the better part of a year, last-click attribution is not just imperfect, it is actively lying to you. It hands all the credit to the final “direct” visit and none to the eight months of dark-funnel influence that produced it.

The uncomfortable bit

Here is what I think most teams get wrong, and it is not a tactic, it is a posture.

We were all trained to believe that what you cannot measure, you cannot manage, so we quietly decided the measurable part was the whole game. We poured the budget into the trackable slice at the bottom because it gave us clean dashboards, and we told ourselves the dark part was small. It is not small. It is most of it. We built an entire discipline around the ten percent of the journey we can see and called the other ninety percent “brand,” and filed it under nice-to-have.

The funnel you can see is real. It is just not the start. It is the receipt. By the time someone is a trackable lead, the decision that mattered was already made in a place you were not invited. Your job is not to light up that place. Your job is to be the name that gets spoken there when you are not in the room.

That is the work. It is slower and less satisfying than a dashboard, and it is the only thing that actually moves the part of the funnel that decides who wins.

What comes next

This week was the funnel that starts before the funnel. Next week I want to make it concrete by pulling apart a company that won the dark funnel on purpose, with almost no budget, before anyone had a name for it.

The company is Clay, and it is one of the fastest-growing B2B companies of the last few years. The interesting part is not how fast it grew. It is how it grew: by finding its first real customers inside a single online community, one careful message at a time, and turning that into a flywheel everyone else is now trying to copy. I am going to show you exactly which moves you can steal. No hype, just the mechanics.

Before you go, try the experiment I started this with. The next time you buy something for work, watch how you actually do it. Notice the chat window, the Slack message, the colleague’s offhand recommendation. Then go look at where your own buyers show up in your analytics, and ask yourself how much of their real journey you are seeing.

Probably the receipt. Almost never the decision.

Monday GTM is a weekly newsletter for B2B founders and growth teams. One practical playbook, teardown, or hot take, every Monday. No fluff. No filler. Just the frameworks that actually move the pipeline.

Next up: how Clay, one of the fastest-growing B2B companies of the last few years, built its entire go-to-market inside a single community, and what you can copy from it.

See you then.

Key data points in this post are linked inline to their original sources.

Here is a number that should reframe how you think about your entire paid budget.

In 2025, LinkedIn was the only major paid channel that delivered a positive return on ad spend for B2B — 121%. Google Search came in at 67%. Meta at 51%. The top quartile of LinkedIn advertisers hit 279%.

Read that again. Every other channel you are running lost money on a blended basis. LinkedIn made it.

This is the part where most marketers get defensive about LinkedIn, because they ran a campaign once, saw an $11 CPC, and decided the platform was a scam. I understand the reflex. LinkedIn is the most expensive paid channel in B2B by almost every on-platform metric. The CPMs are roughly three times Meta’s. The clicks are brutal.

But here’s what eight-plus years watching B2B teams burn paid budgets taught me: the people who think LinkedIn is too expensive are measuring the wrong thing. They are looking at cost per click when they should be looking at cost per closed deal. And on that metric, the only one that pays your salary, LinkedIn is not the expensive channel. It is the only one that works.

The catch is that how you make it work changed. Three things shifted between 2022 and 2026, and if you are still running the LinkedIn playbook everyone copied off the same five agency blogs in 2022, you are paying premium prices for a motion that stopped compounding.

Let’s fix that. We are going to do it through a framework I laid out a few weeks back - the GTM Canvas, which maps any B2B go-to-market onto twelve boxes across four layers. LinkedIn Ads lives inside two of them: Channels (where your buyers are, and how you reach them) and Acquisition Metrics (how you know it’s working). Those are the two boxes we are going to fill in properly.

First, the baseline you are actually working with

Before the three shifts, the numbers so you have something to benchmark your own account against. These are 2026 figures, triangulated across roughly half a dozen independent sources, not one agency’s hero numbers.

• CPC: $5–12 for Sponsored Content, pushing $15–25 when you target the C-suite.

• CPM: around $30–50, with LinkedIn’s own 2026 benchmark sitting at $33.80.

• CPL: $60–175 for lead gen, with a cross-industry average of $94 - up from $87 the year before.

• Lead Gen Form conversion: roughly 10–13%, versus 2–6% on a landing page. The pre-filled form is the single biggest friction-remover on the platform. Use it.

• The practical budget floor: the technical minimum is $10 a day, but $50–100 a day is the real floor before you have enough data to make a decision. Below that you are not running a campaign, you are buying a hunch.

Keep one number in your head above all others: CPCs rose 9% year-on-year, the steepest jump since 2022. That rise is not random. It is the direct symptom of the first shift.

Channels, Part 1 — the targeting everyone copied stopped working

This is the box where most LinkedIn budgets quietly die.

The move everyone learned in 2022 was simple: layer job title and seniority. Target “Director and above” in “Marketing” at companies with “200–1,000 employees,” and congratulate yourself on a tight, decision-maker audience. Every agency taught it. Every founder copied it. Which is exactly why it stopped working.

Think about what happens when every B2B advertiser targets the same narrow pool of senior people. You are all bidding in the same auction, for the same few hundred thousand inboxes, on the same handful of attributes. LinkedIn runs a second-price auction, the more of you crowd into the same audience, the more each impression costs all of you. That 9% CPC rise is not LinkedIn getting greedy. It is sustained demand for the same senior-level inventory, with the steepest increases - B2B SaaS +11%, IT and cybersecurity +12%, financial services +10% - landing in exactly the verticals where everyone adopted the same targeting recipe. The overlay didn’t stop working because it was wrong. It stopped working because it became crowded.

LinkedIn itself will now tell you, in its own targeting guidance, that job-function targeting is too broad and that over-layering attributes collapses your audience below the delivery threshold. When the platform is gently telling you your favourite tactic is a problem, the tactic is a problem.

So where did the edge go? It moved off LinkedIn’s firmographic filters and onto your own data.

The teams winning in 2026 are not describing their ICP to LinkedIn through dropdowns. They are showing LinkedIn their ICP, by feeding it first-party seed data and letting the model find the rest:

• Matched Audiences: upload your target account list and LinkedIn matches it at 95%-plus on company lists, 70–85% on contact lists. This is the foundation layer for any serious ABM motion.

• Predictive Audiences: seed the model with your closed-won customers and it builds a lookalike of likely converters. The payoff is real: 21% lower CPL on average versus standard interest or job-title targeting. The catch is that it needs clean seed data, at least 100 conversions, ideally 500-plus, to train on. Garbage seed, garbage audience.

This is the single targeting move most teams are still leaving on the table. The dropdown overlay is a commodity now; everyone has it, so it prices like a commodity. Your customer list is proprietary. Nobody else can bid on it. That is the whole game.

And while we are here, stop chasing audience size. A tight, high-intent audience of 10,000 beats a broad 200,000 every time. The proven ranges: 50,000–400,000 for prospecting, 10,000–50,000 for retargeting, 1,000–30,000 for ABM. Density is the metric. Reach is vanity.

Channels, Part 2 — the Audience Network is worth it, for exactly one job

Here is a change almost nobody uses correctly.

The LinkedIn Audience Network extends your ads off-platform, onto partner apps and sites. For years the advice was to leave it switched off, because off-platform impressions felt like leakage. That advice is now half-wrong, and the half that’s wrong is worth money.

The Audience Network runs at 35–50% lower CPMs than the in-feed inventory - roughly $16–22 against $33.80. That is a serious discount on reach. But, and this is the part people miss, the click-through and conversion rates both drop off-platform. So the economics only work for one job.

The Audience Network is not a CPM hack you bolt onto every campaign. It is an awareness lever. Net economics tilt positive for upper-funnel awareness work and negative for lead gen, unless you have built creative specifically for the off-platform placement. Run it for reach at the top of the funnel. Switch it off the moment you are asking for a conversion. And switch it off entirely for ABM, it dilutes the tight account targeting you worked to build.

The mistake isn’t using the Audience Network. The mistake is using it for the wrong box on the Canvas.

Channels, Part 3 — the format hierarchy flipped

The third shift is the quietest, and it changes what you should actually be putting in the feed.

For years the default was the Single Image Sponsored Content ad - a clean graphic, a headline, a Lead Gen Form. It still works. It is no longer what works best. Two formats now beat it on the only metric that matters, cost per qualified lead.

• Document Ads. The carousel-style download - a guide, a benchmark report, a framework. CPL runs 30–40% lower than a generic lead form, because the prospect is trading their details for something they actually want, not just raising a hand. They double as cheap retargeting-audience feeders - content downloads cost $5–12 each.

• Thought Leader Ads. You sponsor a post from a real person’s profile - your founder, your head of product - instead of the company page. The numbers are not subtle: click-through 2–5 times higher than brand-sponsored content. The catch is that the creative has to be native to that person’s actual posting voice. Rebranded marketing copy with a face slapped on it gets ignored, and people can smell it.

Meanwhile the cheapest formats are a trap. Text Ads have the lowest CPC at $2–6 and the worst click-through on the platform. Cheap is not the same as efficient. It never was.

The pattern across all three shifts is the same one the Canvas keeps hammering: the lazy default is now the crowded, expensive option, and the edge has moved to the thing that takes actual work.

Acquisition Metrics — measure the channel by what it is, not what it looks like

This is where I lose half the room, because the metric LinkedIn looks worst on is the one most people judge it by.

LinkedIn’s CTR sits at 0.44–0.65%. Google Search runs 2–5%. By that number alone, LinkedIn looks broken. But CTR is a vanity metric on a channel where the entire value proposition is who clicks, not how many. A 0.5% click-through from verified decision-makers is worth more than a 4% click-through from whoever Google’s keyword match dragged in.

The Canvas rule holds: if a number changes and you don’t know what to do about it, it is not a metric, it is a data point. On LinkedIn, three numbers earn their place.

• Cost per SQL, not cost per lead. CPL is the number everyone reports because it is easy. It is also nearly useless on its own. The metric that ties spend to pipeline is cost per sales-qualified lead, and that is what you defend your budget on.

• Pipeline-to-spend, measured over the real cycle. Here is the stat that should change how you report LinkedIn entirely. Dreamdata’s 2026 analysis found the average B2B journey runs 88 touchpoints, across 4 channels, with 10 stakeholders, over 281 days, from first LinkedIn impression to closed revenue. If you judge LinkedIn on a 30-day last-click window, you will systematically conclude it doesn’t work, and you will be wrong, because you measured a nine-month channel with a one-month ruler.

• A brand-versus-generic split. The most uncomfortable number in the research: branded campaigns returned $12.99 in ROAS against $0.68 for generic ones - a roughly 19x gap, across 1,400-plus campaigns analysed by LinkedIn’s own B2B Institute. Even on a “performance” channel, the ads where your brand is prominent crush the ads where it isn’t. If you are running stripped-back direct-response creative because a growth thread told you brand is fluff, you are leaving an order of magnitude on the table.

The one mistake I see every time

Teams treat LinkedIn as a slot machine. They put money in, pull the lever, check the CPC the next morning, panic, and turn it off.

LinkedIn is not a slot machine. It is the slowest, most expensive, highest-quality channel in your stack, and it only pays out if you measure it on its own clock. You need 60–90 days minimum before the channel has told you anything true. The first 30 are just the learning phase. Most teams kill the campaign in week two, right before the data would have started meaning something.

The channel works. The 121% ROAS is real. But it is the reward for patience and first-party data, not the default outcome of switching ads on. The teams that win on LinkedIn in 2026 are not the ones spending the most. They are the ones feeding it their own customer list, building for the right funnel stage, and measuring on the cycle the buyer actually moves through.

Everyone else is just bidding against each other for the same crowded inbox and calling the bill LinkedIn’s fault.

What comes next

This was Channels and Acquisition Metrics - two boxes of the Canvas, one channel deep. Next week we go up a level, to the box that decides whether any of your channels matter in the first place: GTM Motion.

More specifically, the trap inside it. There is a funnel everyone draws that starts at “awareness” and ends at “closed.” In 2026, that funnel is a lie. It starts a long way before the part you can see, and most of the buying decision is made somewhere you are not even measuring. That’s the hot take. Bring your assumptions; I’m going to take a few of them apart.

One thing worth doing this week: go look at whether your best campaign is seeded with your own customer list or with LinkedIn’s dropdowns. That one check will tell you most of what you need to know.

Monday GTM is a weekly newsletter for B2B founders and growth teams. One practical playbook, teardown, or hot take, every Monday. No fluff. No advice. Just the frameworks that actually move the pipeline.

Next up: Your B2B funnel doesn’t start where you think it does. A hot take on the dark funnel, AI-led buyer research, and why your first touch isn’t your first touch.

See you then.

Key data points in this post are linked inline to their original sources.

Last week I told you Notion raised at an $11 billion valuation in 2021 and then spent four years quietly growing big enough to deserve it.

Half of that sentence is wrong.

The half that’s wrong is the interesting part.

Notion’s Series C closed in October 2021 at a $10 billion valuation, not $11 billion. They raised $275 million from Coatue and Sequoia, and that was the last primary round they’ve ever done. Four and a half years later, they still haven’t raised another dollar.

The $11 billion number is real, but it’s from an entirely different event. In December 2025, Notion ran an employee tender offer at $11 billion, employees and early investors selling shares to GIC, Sequoia, and Index. No new capital came into the company. No dilution. The valuation barely moved in four years.

Here’s what did move. In October 2021, Notion was doing roughly $31 million in ARR. By December 2025, that number was $600 million. Revenue grew 19x. The valuation moved 10%.

The multiple compressed from 322x ARR to roughly 18x. To put those numbers in plain English: at 322x revenue, investors are paying for a company that doesn’t exist yet, a bet on a future eight to ten years out, in a market where money was basically free. At 18x, you’re valuing a company for what it actually is today - profitable, compounding, proven. Notion moved from one to the other without lowering their price tag. Most companies can only make that trip by taking a down round. Notion made it by just growing.

Most people writing about Notion this year are staring at the valuation story. The valuation story is a symptom. The actual question, the one worth spending the next fifteen minutes on, is this: what kind of GTM machine compounds for four years without new capital, grows revenue 19x, converts half its customer base to an entirely new product line midway, and stays profitable the whole time?

That’s the teardown. Let’s use the Canvas.

Layer 1: WHO

The customer isn’t who you think it is.

Most B2B companies pick an ICP and go sell to it. Notion did something weird. They picked a sequence.

The first customer was never the customer. It was the acquisition channel for the customer.

Notion built the free plan around an individual - a student, a solo operator, a knowledge worker tired of switching between Google Docs and Microsoft Word and Confluence and whatever the shiny new thing was that week. That individual wasn’t the buyer. They were the Trojan horse. Every person who set up a personal Notion workspace was a potential inviter, because the interesting features only unlocked when someone else joined the doc.

So individual users pulled in their teams. Teams showed up as a unit. And once teams were roped in, Notion layered in the enterprise features - SSO, admin controls, SOC2, audit logs - so whoever was already paying for Teams could escalate to Enterprise without a sales rep ever starting the conversation from cold.

The ICP wasn’t one buyer. It was three, in sequence. The individual got you in. The team made you the system for records. The enterprise signed the annual contract and made leaving expensive.

Akshay Kothari, Notion’s COO, told CNBC last September that 90% of their business now comes from “multiplayer usage”- teams, not individuals. The individual never was the customer. They were always the channel.

Most B2B GTM starts with the question “who’s the buyer?” Notion’s answer was a sequence, not a target. The buyer came last.

Layer 2: WHAT

The offer is a primitive, not a product.

Here’s where most Notion teardowns go wrong. They describe Notion as a “workspace tool” or an “all-in-one productivity app” and move on. That’s a feature description and it misses the point entirely.

Notion doesn’t sell a product. It sells a primitive. A block that can be a paragraph, a database row, a page, a task, a toggle, a button, a callout, an embedded calendar. Every feature they have ever shipped is built out of the same primitive. Add up enough blocks in the right order and you get a CRM, a project tracker, a meeting notes hub, or a company handbook. The product scales in capability without ever scaling in complexity.

This is the move most founders miss. A product that scales by adding features gets heavier every quarter. A product that scales by combining the blocks stays light forever. Notion figured this out early and hasn’t deviated from it once.

And here’s the clever bit - once the primitive exists, the customer becomes the product team. Users build templates out of blocks. Other users download those templates. Some of them edit and republish. The long tail of the Notion catalogue isn’t built by Notion, it is built by users who have figured out use cases Notion never imagined.

Thomas Frank’s Ultimate Brain template alone generated $760,000 in a single year. Easlo crossed half a million dollars in lifetime template sales. Others are running full-time businesses on the Notion template economy. When your users are building businesses on top of your product, you have crossed the line from tool to platform and you don’t have to fight for relevance anymore.

Most products are sold as use cases. Notion is sold as a primitive that generates use cases. Use cases decay. Primitives compound.

Layer 3: HOW (ACQUIRE)

Three channels stacked, each one cheap because of the one above it.

The Canvas says pick two or three channels. Notion picked three, but not in the way most companies pick three. They built three stacked layers where each one made the next one structurally cheaper. Miss any layer and the whole thing falls apart.

Community-led top of funnel. Notion didn’t build a community the way most B2B companies do - they didn’t run a few events and call a Slack group a community. They built an infrastructure for other people to build communities. The Ambassador program has over a hundred sub-communities run by volunteer moderators. There are over a hundred thousand people in r/Notion, one of the largest product-led communities on Reddit. The template gallery is stocked by users, not by Notion. None of this is paid media. All of it is the top of the acquisition funnel.

Per Semrush’s March 2026 data, notion.so got 159 million visits last month and 85.5% of that was direct. People typing the URL from memory or clicking a bookmark. Roughly 136 million direct visits per month, no paid search, no ad spend. That’s pure brand recall, not performance marketing. Most B2B companies spend seven figures a quarter trying to manufacture demand at a fraction of that volume. Notion manufactures it for free because the product IS the asset.

PLG conversion engine. The community produces the awareness. The product converts it. Free plan, team invites, paid tier - the block being shared is also the sign-up moment. Nobody has to be sold anything. The viral coefficient isn’t marketing copy, it is the product’s default state.

Enterprise sales overlay. Built on product-qualified leads, not marketing-qualified ones. Sales doesn’t hunt, it harvests teams that already self-selected into paid. Kothari again: “We’re doubling this year and likely going to double the sales team next year.”

Each layer is cheap because the previous layer did the hard work. Community is cheap because the product is the asset. PLG is cheap because the community did the acquisition. Enterprise sales is cheap because PLG did the qualification.

Most companies run one channel and call it GTM. Notion runs three that compound into each other. That is the actual moat, not the product, not the brand, not the template gallery.

Layer 4: HOW (KEEP)

The retention layer that quietly became a new revenue line.

This is the part of the Notion story that most teardowns get wrong. Or ignore entirely.

Notion launched their AI product in November 2022 two weeks before ChatGPT. For the next two and a half years, they ran it as a paid add-on at roughly $10 per user per month on top of whatever plan you were already on. Most customers didn’t buy it. In 2024, the AI attach rate sat at 10 to 20%.

Then something interesting happened.

Kothari told CNBC in September 2025 that the attach rate “shot up to 30% or 40% earlier this year and recently crossed 50%.” Within roughly eighteen months, AI went from a minority add-on to a feature more than half their paying customer base was already opting into. And the moment it crossed majority, Notion did the smart thing. They killed the add-on.

In May 2025, AI got bundled into the Business and Enterprise plans at no extra charge. If you wanted AI, you had to be on a team plan. If your team was scattered across the free tier, someone had to pay to upgrade everyone. The attach rate turned into an expansion event. The same customers, paying more, without a single upsell rep picking up the phone.

By the end of 2025, more than half of Notion’s ARR came from AI customers. That share had doubled in a single year. At Ramp alone, 9 out of 10 employees across a company of 1,200 now use Notion’s AI features monthly. That is not an account-level attach rate. That is saturation at the individual seat level, inside one paying customer.

Most SaaS companies price AI as a premium. Notion used it as a tripwire. They let the attach rate build on its own, waited for majority, and then flipped the pricing model so the feature did the expansion work for them.

Retention became expansion. Expansion became a new revenue line. None of it required a new GTM motion - just the one they already had, with a better lever pulled at the right moment.

The lesson

Back to the opening paradox. Why did Notion’s valuation barely move while ARR grew 19x?

The passive answer is “they grew into it.” That framing is lazy and it makes the whole thing sound accidental. The active answer is that every layer of the Canvas was built to reinforce every other layer, and the machine kept compounding for four years without needing new money.

The WHO sequence made the WHAT obvious. The WHAT produced the community that drove the HOW (ACQUIRE). The HOW (ACQUIRE) created the installed base that made the HOW (KEEP) layer work. None of this happened in parallel. It happened because each layer made the next one structurally cheaper.

The valuation staying flat wasn’t the story. It was the byproduct of a machine that didn’t need new capital to grow.

If you are a B2B founder who raised in 2021 or 2022 at a multiple you cannot defend on paper today, you don’t fix that with a down round or a pivot narrative. You fix it by building the version of this machine that works for your product. Sequence your WHO. Find your primitive. Stack your HOW. Make your retention layer do expansion work.

Or burn capital trying to outgrow the valuation. Notion just showed you which one works.

Monday GTM is a weekly newsletter for B2B founders and growth teams. One practical playbook or teardown, every Monday. No fluff. No advice. Just the frameworks, teardowns, and hot takes that actually move the pipeline.

Next Monday: LinkedIn Ads for B2B — the only guide you’ll need in 2026. What actually works, what everyone is still doing wrong, and the single targeting move most teams are leaving on the table.

See you then.

Key data points in this post are linked inline to their original sources.

Most marketing teams I have worked with do not have a GTM strategy.

They have a wishlist.

It is buried in the folders in a Word doc or a deck somewhere. It is a bulleted list or a Smart Art that says LinkedIn ads, upsell to existing customers (or low hanging fruits as we like to call them), maybe a podcast, a PR roadshow, some SEO. Definitely a referral program because someone on the leadership team heard that they work really well.

This is not a strategy. It’s anxiety dressed up as a plan.

I know because I spent 8+ years in fintechs, B2B platforms and a global consulting practice working with really smart people building these wishlists, spending hours discussing them and then running around fetching approvals, finally executing them and then 6 months later wondering why nothing compounded. The issue was not in the effort. We worked 60 hour weeks on them. The issue was that no one forced us to answer 12 specific questions pertaining to our product in a single sitting.

So now I built a framework to force the answers. I call it the GTM Canvas, and it fits in one page.

What the GTM Canvas is (and isn’t)

12 boxes, 4 layers and 1 rule that holds it all together - everything has to be answered in one sentence.

The one sentence restriction is the whole point. If you cannot answer it in a sentence, you do not have a strategy, you have a wishlist. It is the difference between “we will run paid ads and do some content marketing and a partnership” and “we acquire customers through a single inbound motion that runs on LinkedIn Ads targeting the Head of RevOps in Series B fintechs.”

The second is a strategy. The first is what makes people call the marketing team a cost centre.

The Canvas doesn’t tell you what the right answers are for your product. No framework can do that. What it does tell you, and brutally so, is where the gap is. When you sit down to fill it, you may notice that you have left 3 or 4 boxes blank — not because you forgot, but because you haven’t thought about them yet. This is the real work. The Canvas just points it out to you.

Most B2B GTM frameworks I have seen, stop at acquisition. It will walk you through the target customer persona, the positioning, the channels and then tumble down to the finish line. It may mention retention as an after-thought as it would be weird not to. This is because most of these companies treat the customer as a prey they are hunting and once it is shot down, the work is done. That’s a grave mistake and more so in 2026. Because the honest truth is this, in a world where your customer can onboard themselves, self-serve through the pricing and cancel in two clicks, the experience IS the go-to-market. You don’t get to separate the two anymore. The product is the pitch, the onboarding is the close and the first support ticket is your renewal discussion.

This is why the Canvas has a fourth layer most GTM frameworks don’t. I will get to it.

Layer 1: WHO

The foundation. If you get this wrong, then nothing else matters.

1. ICP

Who you’re selling to, with the precision of a sniper, not a shotgun.

“Mid-market SaaS companies” is not an ICP, it is a category. “Series B to Series D funded SaaS companies with 100-400 employees, a RevOps lead on the team, with a sales cycle greater than 45 days” is an ICP. You should comfortably be able to list down 10 companies with your ICP. But if you can list two thousand, you haven’t narrowed it enough, and if you can only name three, then you have narrowed it beyond the point of a reasonable market. The right ICP will give you anywhere between a few dozen to a few hundred companies, sharp enough to launch a focused attack and broad enough to build the business at scale.

I have watched B2B teams waste months trying to sell to every customer that showed the faintest interest because they were afraid to pick a lane. In my experience, this fear of missing out is the biggest cost to a B2B company, not over-hiring, not picking the wrong agency or the wrong CRM. It is refusing to choose.

2. Buyer JTBD

The job the buyer is hiring your product to do.

It is not the features. Not even the outcome your marketing team promised the product will deliver. It is the actual job. The Head of Marketing does not buy an email marketing tool to make “email engagement better”. She buys it because her CMO asked her last week why the pipeline is low, and she wants a new chart for the next board meeting deck. She wants your product to make the chart look better.

Once you see it this way, the messaging writes itself.

3. Anti-ICP

What your product has explicitly decided not to be.

This is the box most teams decide to skip, and it is the one that gives most clarity once filled in. Anti-ICP isn’t just “who you do not sell to”. It is the buyer types you are refusing, the use cases you are turning away from, the features you decided not to build, the problems you are telling your customer isn’t your job.

Netflix’s Anti-ICP is live sports, user generated content and someone whose average viewing time is less than 5 minutes. This is not target market exclusion. This is Netflix declaring what it has decided not to be and that’s why it is so clarifying.

When you call out your Anti-ICP, three things happen immediately. Your sales team stops wasting time on bad-fit leads, your product team stops building features for buyers you would never close and your marketing team finds their edge because nothing is sharper than a brand that says it is “not for everyone.”

Layer 2: WHAT

The offer. What you actually hand to the buyer.

4. Positioning

The one statement that tells the buyer why you exist.

It’s not your mission, vision or tagline. It is the statement the buyer would use to describe you to a peer. “It’s like Stripe but for insurance payouts” is a positioning. “Empowering the future of commerce through innovative solutions” is a war crime.

The litmus test: can your positioning statement be sometimes confused for your competitors’? If yes, then you haven’t positioned yourself, you have described.

5. Pricing model

Not the price, the mechanism.

Per seat, usage-based, flat or hybrid; the pricing model is the single biggest GTM decision that most founders make without realising they are making it. The per seat model locks you in the “land-and-expand” mode - you start with one team, then another and then the whole company. Your sales team’s job essentially becomes adding more seats. Usage based model flips the script. You make $500 in one month and $50,000 in the next month, which sounds amazing until your finance team sits down to forecast your revenue and realises they can’t. Flat pricing is the easiest to sell but it leaves serious money on the table when a buyer would have happily paid 5x of what you are asking. Each model requires a different motion downstream, which means you cannot pick channels or structure teams unless you have decided on this.

I have watched B2B teams rebuild their entire GTM motion because they changed the pricing model midway. And honestly this is the single most expensive pivot in B2B which no one even acknowledges as a pivot and brushes it under the rug as a simple “tweak.”

6. Proof

The three things you can say to make the buyer choose you.

Customer logos, case study numbers, a methodology you developed that no one has. Whatever the three things are, they need to be specific and verifiable. “Trusted by industry leaders” is not proof. “Customers spend 3.5% less and close their books 8x faster after switching” is proof. That’s the number Ramp shows every buyer, and it’s the reason their sales team doesn’t have to fight for attention.

If you have fewer than three, you’re pre-proof. And that’s okay, it just means your GTM motion needs to be built around creating proof, not leveraging it. Those are two different strategies and both are valid.

Layer 3: HOW (ACQUIRE)

The motion that brings them in.

7. Channels

The two or three places where your ICP lives in scale.

Two or three channels, not ten. If your answer in this box is “omnichannel” or a “mix of paid, organic and community”, then you don’t have channels, you have FOMO. The question is not “where can we reach our ICP?” The question is “where can we reach them frequently, at the lowest cost and with enough intent that it compounds?”

Most early stage B2B companies need only one channel to work, two if they are lucky. Three is a luxury that most companies haven’t earned yet, I say yet because there is no harm in running 3 channels eventually. The mistake is starting with three. Anything more than two channels in the early days is the sound of a budget being set on fire.

8. GTM motion

Product-led, sales-led, hybrid, community-led and why.

The simpler version of this question is who does the selling, the product or a human? If your product is simple, has an ACV below $5,000 and the buyer can realise the value in the first session, then the product can do the selling. This is PLG. If your product has an ACV above $25,000 and the buyer has to convince a procurement committee headed by the CFO, a human has to do the selling. That’s sales led. The awkward gap between $5K and $25K is where the hybrid motion resides, and this is where most founders get stuck pretending they have picked when they haven’t.

The mistake I see most often is founders picking PLG because it is trendy, and later realising that their ACV and product complexity does not support it.

9. Acquisition metrics

The two or three metrics that tell you acquisition is working.

Not a dashboard with forty-seven metrics. Just two or three. Usually: CAC, CAC payback, and one leading indicator specific to your motion (MQL-to-SQL conversion rate, trial-to-paid rate, pipeline-to-closed-won rate). The rule: if a number changes and you don’t know what to do about it, it’s not a metric, it’s a data point. Delete it.

Layer 4: HOW (KEEP)

This is the layer most frameworks exclude. It is the one that decides whether your GTM compounds or leaks.

Here’s the thing about B2B in 2026: you don’t really win customers anymore. You rent them. And the rent is due every month, every quarter, every renewal date. The first sale is just the trial. The real sale is the renewal and it’s happening quietly in the background whether you’re paying attention to it or not.

A customer who churns in month three doesn’t just cost you the rest of their contract. They cost you everything you spent to acquire them, they cost you the word-of-mouth you would have earned if they had stayed, and they cost you the case study you were going to build around them. A leaky bucket doesn’t just lose water, it discredits the tap.

So here are the three boxes that decide whether the bucket holds.

10. Onboarding

How a new customer reaches value for the first time, and how fast.

Time-to-value is the most underrated metric in B2B. If a customer signs up on Monday and hasn’t experienced value by Friday, they are gone. Not dramatically, just quietly. They will stop logging in, miss the first follow-up email, and by the renewal date they will have forgotten why they bought you in the first place.

Onboarding isn’t just a welcome email. It is the whole first experience - the sign-up, the first setup, the first “aha” moment, the first measurable win. Your job is to stitch that journey to be as short and as un-skippable as possible.

11. Support and service

How issues get resolved, and how trust compounds over time.

I am going to say something that will sound strange coming from a marketing newsletter - your support experience IS marketing. And possibly the most important kind. Every time a customer hits a wall and you help them through it beautifully, you have just earned another year of trust. Every time you don’t, you have just written the first draft of their churn reason.

In B2B especially, support is where the brand promise gets tested. Everything before the sale is words. Everything after it is actions. And in an AI-assisted world where a lot of support is becoming self-service or bot-first, the companies that still invest in a human touch at the hard moments are going to win disproportionately. Trust is the last moat.

12. Renewal trigger

The moment the customer decides to stay or leave.

Not the renewal date itself, the moment the decision actually gets made. Sometimes that is six months before the contract ends. Sometimes it is during a failed onboarding in week two. Other times it is the day your account manager quits. Your job is to know when that moment is in your business, and to architect the whole customer experience around reaching it on the right foot.

If you cannot name the renewal trigger for your product, you are not doing retention. You are hoping.

How to use the Canvas

Here is the thing I wish someone had told me the first time I tried to build a GTM strategy: the Canvas isn’t a plan. It’s a diagnostic.

Sit down with a blank version. Give yourself one uninterrupted hour, phone in silent, Teams shut, you know the drill. Try to fill in all twelve boxes with one sentence each. You will probably get through six or seven before you hit the first one you can’t answer. That’s the box that needs work. That’s the one your strategy is missing.

The boxes you can fill in fast are the ones you already have conviction about. The boxes you cannot fill in are the ones where your team is going to disagree in three months and you’re going to lose a quarter sorting it out. Catching that disagreement now, in a one-hour exercise, is worth more than any playbook I could write for you.

Do it with your co-founder. Do it with your head of product. Do it alone at 11pm with a cup of coffee that’s gone cold, if that’s your thing. Just do it before you spend another dollar on ads.

The one mistake I see every time

Founders fill in the Canvas and then lock it in a drawer.

The Canvas is a living document. Your ICP will sharpen as you start selling. Your channels will prove themselves or fail. Your pricing model will get tested by actual buyers who push back on it. The answer in each box will evolve, and if you are not revisiting the Canvas every quarter, you are working with instruments that were calibrated six months ago.

The best GTM teams I’ve been part of have the Canvas pinned to a wall - physical or digital - and they update it the way engineers update architecture diagrams. Not because it is pretty but because it’s the single source of truth for what the business is trying to be.

What comes next

Every post on Monday GTM from here on out is going to reference back to this Canvas in some way. When I tear down Notion’s GTM strategy next Monday, the company that raised at an $11B valuation in 2021 and then spent four years quietly growing the business big enough to actually deserve it, I will be using these twelve boxes to do it. These twelve boxes are the shared language for everything that follows, and I will keep pointing back at them.

The Canvas is the shared language for everything this newsletter is going to do.

So just try the exercise. One hour. Twelve boxes. One sentence each. No prep, no preamble.

You’ll learn more about your GTM strategy in that hour than you will from a month of reading Substack posts about it. Including this one.

Monday GTM is a weekly newsletter for B2B founders and growth teams. One practical playbook, every Monday. No fluff. No advice. Just the frameworks, teardowns, and hot takes that actually move the pipeline.

Next Monday: How Notion raised at an $11B valuation in 2021, and then spent four years quietly growing big enough to deserve it. A GTM teardown using the Canvas we just built.

See you then.

Key data points in this post are linked inline to their original sources.

I’m Meghna. I’ve spent about eight years in B2B and fintech growth, and Monday GTM is where I write down how it actually works.

If you run growth at a B2B or fintech company, or you’re a founder trying to build a go-to-market motion that compounds instead of leaking, this is for you. One practical piece every Monday. A teardown, a playbook, or a hot take. No filler, no recycled LinkedIn wisdom.

Where to begin

Three pieces to start with.

The GTM Canvas. The twelve-box framework that makes a team answer the strategic questions it has been quietly avoiding. Later issues refer back to it.

How Clay sells without a sales team. A company that got to $100M ARR by handing acquisition to people who did not work there, and why the product being hard to learn is the reason it worked.

The MQL is finally dead. Everyone agrees the MQL is dead. That agreement is the problem, and almost nobody is using the replacement they are selling you.

What you’re signing up for

One email a week. Every fact and number in it is checked and linked. It stays free.

Who’s writing this

I worked as a client-facing consultant at Mastercard, supporting clients on their go-to-market and campaign execution across Southeast Asia and the Middle East. Before that I led digital go-to-market and acquisition for consumer lending products at HDFC Bank, and ran B2B product and digital marketing at Vodafone Business. So this comes from having built the funnels and missed the targets, not from reading about it.

I also run a growth consultancy, Loop & Ladder, working with B2B, fintech, and D2C companies on go-to-market, demand generation, and growth. If that’s the kind of thing you’re dealing with, it’s at loopandladderco.com. The newsletter itself asks nothing of you except a read.

Subscribe below and I’ll see you Monday.

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