In the early days of my career, when I worked in B2B brand, I managed the social media channels for my brand. During my tenure, our share of voice grew from 6% to 65% in 6 months. Quoted on its own, that number fascinated leadership. On a closer look, though, it had nothing to do with what I did. It came from a brand relaunch campaign, and the majority of the comments were negative. Suffice it to say that part never went to leadership, and every time I presented the number, I prayed no one would ask for the sentiment analysis.

It is planning season again, so somewhere a 2027 deck has a share-of-voice chart in it. The line goes up. Underneath sits a rule that makes the line sound important. Brands whose share of voice runs ahead of their share of market tend to grow, by roughly half a point of share a year for every 10 points they are ahead. Finance nods, because it sounds like physics.

The rule is real. It just measured something different from the number on your slide.

Even Cannes Lions had a B2B session this June billed as HSBC’s story of going from eighth to first in share of voice in 5 months. The session page doesn’t say what was counted. That is the whole question: what the rule actually measured, and what your dashboard measures instead.

The rule was always about money

The studies behind the rule all measured the same thing. Advertising money.

Back in 1990, John Philip Jones wrote it up in the Harvard Business Review. HBR’s own summary defines share of voice as a brand’s share of the main media spending in its category. Your ad spend, as a slice of all the ad spend in your category.

The half-point number comes from a 2009 Nielsen study of 123 brands in 30 categories, running ordinary advertising, not award winners. Brands whose share of ad spend ran 10 points above their market share gained about half a point of extra share a year, on average. Nielsen offered it as a rule of thumb for consumer goods. Even then, it told brands to work out their own number rather than borrow the average.

The B2B version, from Les Binet and Peter Field for LinkedIn’s B2B Institute in 2019, defines it the same way: typically, your share of all the ad spend in your category.

So the rule describes a dial you control. Take a bigger slice of your category’s ad spend than your slice of the market, and your share tends to creep up, slowly and on average. It is a budgeting rule.

Somewhere along the way, money became mentions

Now look at how the tools you actually use define it.

Twelve companies that sell a share-of-voice number, or the ad space behind one, each publish a guide to what it means. They are social listening and media monitoring tools, SEO tools, HubSpot, and LinkedIn itself.

Read side by side on 23 September 2026, here is what they say. All 12 count mentions or online visibility as share of voice. 8 of them tie that number to market-share growth. 3 name the research behind the claim, and all 3 point to studies that measured ad spend. Not one cites a study that measured mentions.

(The 12, if you want to check: Brandwatch, LinkedIn, Sprout Social, Meltwater, Talkwalker, HubSpot, Semrush, Ahrefs, Cision, Brand24, Hootsuite and Sprinklr. A chosen list, not a random one.)

Hootsuite at least says it out loud: share of voice used to measure paid advertising, and now it covers social mentions and where you show up in search.

LinkedIn’s marketing blog makes the same switch. It works an example from 200 mentions out of 2,000, then says numerous studies have “proven” the half-point rule. The report LinkedIn’s own think tank commissioned called its B2B finding “tentative”.

So the rule came along for the ride. The unit got left behind.

A 2021 report for the Advertising Council Australia, by Robert Brittain, Peter Field and Karen Nelson-Field, puts it neatly. Share of search, it says, is a thermometer. Share of voice is the thermostat. They were talking about search. My reading is that mentions work the same way. A mention count tells you how warm the room is. It can’t turn up the heat. It also can’t tell applause from complaints, which is how a relaunch full of negative comments turns into a 65% share of voice.

The B2B evidence is thinner than you think

Even in its own unit, the B2B case is thin.

The 2019 report drew on fewer than 50 IPA Effectiveness Awards cases from 1998 to 2018. That means campaigns good enough to be entered for awards, mostly from the UK and with relatively big budgets. The authors said so themselves and called their findings tentative. The B2B number came out at 0.7 points of share a year for every 10 points of excess, against 0.6 for consumer brands.

The same report shows how much an average like that hides. In consumer categories, the payoff ran from 0.2 in retail to 1.8 in other services, on cases from 1998 to 2008. That is a 9x spread sitting behind the one number on your slide.

If anyone has repeated the test for B2B since, it didn’t turn up in the research for this piece. The institute’s bigger 2021 study scored 435 B2B campaigns on budget, how long they ran and how many channels they used. Share of voice doesn’t come up once.

If you’re the challenger, read this twice

Here is what the vendor guides leave out. Researchers at the Ehrenberg-Bass Institute, summarising the evidence in 2021, note that bigger brands tend to spend below their market share and still hold it. Smaller brands spend above it just to keep their place.

Nielsen’s 2009 study points the same way. Leaders got 1.4 points of share for every 10 points of excess share of voice. Challengers got 0.4.

Both findings come from consumer categories, so treat them as a warning rather than a law. But if you work for the challenger in your category, spending above your share may just be what it costs to stand still. In Nielsen’s data, each point bought a challenger less than a third of what it bought a leader.

What I’m not saying

Mentions can matter. A 2012 study in the Journal of Marketing Research followed a microlending marketplace for 14 months and found that press coverage and social posts both moved sales. That is real evidence that attention can pay. It just isn’t evidence for the half-point rule, and nothing in the research for this piece tests that rule on mentions.

The rule has serious critics too. In 2016, Ehrenberg-Bass researchers reinvestigated ad spend against brand size across 11 countries and questioned how useful rules like this are for setting budgets. Binet and Field’s data still show the relationship in the unit they measured. Both can be true. Neither rescues a mention count.

What I’d put in the plan instead

Before share of voice goes anywhere near a budget line, write down which one you mean. There are really only three.

The first is your share of the category’s ad spend, measured against a market share you can actually source. This is the one the rule was built on, and it applies, tentatively, in B2B. Fair warning: it is getting harder to measure, with so much spend now inside digital walled gardens where it is hard to track. And if you can’t say where your market share number comes from, you don’t have an excess share of voice. You have a formula wrapped around a guess.

Then there is share of search, your brand’s share of the searches in your category. It is the only other version with published evidence behind it, from 30 cases across 12 categories and 7 countries, and even its champions call it correlation. If there is a B2B test, it is well hidden.

And then mentions, and now AI citations. Put them in the plan as visibility. Report them, and enjoy it when they go up. Just don’t attach the half-point to them. One of the 12 guides already puts AI share of voice right next to the growth claim. I went through what Google and Microsoft actually publish about AI search a couple of weeks ago, including what Google thinks of chasing mentions. So I will leave the tactics there.

How much to spend overall is a different fight. Last month I went through why the budget benchmark you can find in 90 seconds is not your number.

If you are planning a share-of-voice line in your budget, the best place to start is one question. What does the share-of-voice number in your last board deck actually count: ad spend, impressions, mentions or AI citations? Answers in the comments, please.

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Every figure above comes from the source linked beside it. The 12 vendor guides were read on 23 September 2026 and can change without notice; the studies carry their own years, printed where they appear.

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.

I write Monday GTM alongside my work at Loop & Ladder, a growth consultancy.

I recently wrote an article on how to build the marketing budget for next year. After the writing was done and I hit schedule, I realised I skipped a major part of B2B marketing - partners. It is funny how partner marketing is always an afterthought in B2B marketing discussions.

You will hear it in your own planning session, usually late, after the paid budget has been fought over twice. We need another 15% of pipeline from somewhere. What about partners. And then it goes quiet, because nobody in the room can say what a good partner actually looks like.

That is not laziness. The industry has never agreed. So I went and read what the four biggest partner programmes in software actually ask for. Not the summaries, not the agency blogs. The programme documents themselves, sitting on the companies’ own sites, free and open to anyone with an afternoon.

They disagree. Not about details. About the first question.

The question nobody asks out loud

Every partner programme is a ladder and every ladder has a bar. Whether that bar is the same for everybody is the real question, and almost nobody says their answer out loud.

Picture two agencies doing identical work. One sells into a market where the vendor already has a sales team, an office and a brand people recognise. The other sells somewhere the vendor has none of that and the buyer has never heard the name. Same pitch, same hours, nothing like the same difficulty.

A revenue threshold flattens all of that into one number. Forrester has made this point about tiering generally: revenue is still the main thing that moves a partner up, and it leaves smaller partners, and partners in smaller markets, stuck below the top tier while doing everything right.

So you pick. One bar for everyone, and accept that it rewards whoever drew the easiest territory. Or adjust it, and then defend what you adjusted for.

Four companies made that call in public. They picked four different things.

HubSpot decided it was the map

HubSpot pays double.

Tier points come from the revenue a partner brings in, 5 points per 100 US dollars of monthly recurring revenue. For a customer sitting in what HubSpot calls a growth market, it is 10.

By January 2027, Gold costs 1,150 USD in sourced monthly revenue if every one of your customers is in those markets, and 2,300 if none of them are. Same badge, half the revenue, and the only thing that changed is where your customer’s business sits. The list runs across most of Asia, all of Latin America and all of Africa, and it is published.

Here is the part I did not expect. Until 2022 the break followed the partner. Register your agency in one of those countries and your targets dropped. Then HubSpot moved it onto the customer instead, so now anybody earns the 2x for selling there. A firm in Chicago selling into Jakarta gets it. A firm in Bangalore selling into Germany does not.

That one change turned a fairness policy into a targeting instrument. It stopped paying partners for where they are and started paying them for where they sell. Perfectly legitimate. Also a completely different thing, and it looks identical from the outside.

One more line from the same page, because it tells you who holds the pen. Customer location is decided at HubSpot’s sole discretion.

Then Microsoft picked something else entirely

Microsoft looked at the same problem and landed on how big your customers are.

Its partner designations run on a score, 70 points out of 100 to qualify. Before any of that gets measured, Microsoft sorts you into one of two tracks, Enterprise or small business. You land in the small business track if 80% or more of your customers are small businesses. The thresholds in each are not the same.

On the Enterprise track a customer only counts if they spend at least 1,000 US dollars on Azure. On the small business track, 500 does it.

The certification rule is where it gets good. An Enterprise partner needs two people through each of two required exams, and those exams earn zero points. They are gates. You clear them, get nothing, and only then does anything else you have done start counting. A small business partner needs one person per exam, and each of those pays four points.

Same exams. Half the headcount. And the smaller partner gets paid for clearing the hurdle the bigger one clears for free.

Then Microsoft does something I think is quietly excellent, and I have not seen anyone point at it. It flexes what it takes to qualify and refuses to flex what counts as success. Usage growth and deployments are scored identically on both tracks. The inputs bend for who you are. The results do not bend for anybody.

If you take one rule out of this piece, take that one.

The bit that made me go back and check

Microsoft does not ignore geography. It uses it to subtract.

Azure subscriptions in isolated and sovereign environments, Azure China among them, do not count toward eligibility or scoring at all. A partner doing that work scores nothing for it.

HubSpot pays double for a customer in China.

Both current, both published, both written by the vendor for its own partners. Two of the most sophisticated channel teams in software looked at the same country, and one decided it was worth twice as much and the other decided it was worth nothing.

Shopify thinks the whole question is noise

Shopify’s tiering guide is four pages long and it adjusts for nothing. Select starts at 100,000 US dollars of new referred revenue. Platinum wants 5 million. Those numbers are the same in Toronto and in Manila.

And for 2026 it has switched capability off entirely. Credentials are waived, the guide says. Only commercial activity is evaluated.

Which is the exact opposite of what Salesforce did in the same year.

I want to be fair to it, because a flat bar is legible. Every partner can work out where they stand in about ninety seconds, nobody argues about whether their market qualifies, and Shopify never has to defend a list of countries. The cost is the one Forrester named.

Salesforce took the revenue ladder out

Four consulting tiers became two. And 170 separate distinctions became 28 competencies, each scored on certifications, delivered projects and how happy the customer was at the end.

So Salesforce’s answer is what you can actually do. Not where you sold it, not who you sold it to.

The company calls this radical simplification, and its partner chief is quoted talking about stripping away complexity. I would push back gently here. Two tiers, times two levels of recognition, times 28 competencies is more for a partner to keep track of than four tiers was. Better measure. Not a simpler one.

One thing I went looking for and did not find. Every agency blog covering the change says Agentforce competency is what gets you to the top tier. It is in a dozen of them. It is in neither Salesforce document I read, which say certifications, projects and customer satisfaction. The product gate everybody is repeating is not in the place it would have to be.

What is actually being handed out

Strip the four answers away and the prize is identical everywhere.

HubSpot’s partner matching, the tool its own reps use to route real customers to partners, opens at Gold. Shopify marks its partner success manager and both of its funding programmes as by invitation. Salesforce ties marketplace visibility to competencies. Microsoft puts co-selling behind designations.

The benefit is not the badge. The badge is a receipt, and what it buys is a share of the vendor’s own pipeline.

Which changes what you are doing when you set a tier. Setting a tier decides who gets your leads, and every adjustment to the bar decides which of your partners eats.

HubSpot pointed its leads at markets it cannot reach alone. Microsoft pointed them at partners serving the segment it is built for. Shopify pointed them at whoever sells the most. All three are coherent, and not one of them is neutral.

What I would actually do with this

Decide what your bar is compensating for before you pick the number. Market difficulty, customer size, capability, or nothing at all. Nothing is a real answer and Shopify has taken it. What is not a real answer is setting a revenue threshold and working out eighteen months later that you built a programme which only ever rewards partners in your two biggest markets.

Steal Microsoft’s rule while you are there. Change what it takes to qualify, never change what counts as a good result. The moment growth or retention means something different depending on which partner you are looking at, your programme has stopped measuring anything at all.

And if you do adjust, be clear whether it follows the partner or the customer, because those are not two versions of the same policy. Attach it to the partner and you have a fairness mechanism, which is defensible right up until somebody incorporates in the convenient country. Attach it to the customer, the way HubSpot now does, and you have a targeting mechanism that quietly points your entire channel at the markets you want opened. Both work. I would want everyone in the room to know which one they had picked.

If you only do one thing, go and read the programme you are already in. All four of these documents are free and none of them takes more than an afternoon. Most people planning a channel for 2027 will not open a single one. They will read a blog post about them, written by an agency that holds a tier in one.

If there is a partner line in your 2027 plan, what does your tier table adjust for, and can you name who decided that?

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Every figure above comes from the companies’ own partner documentation: Salesforce’s FY27 Consulting Partner Program brochure and programme page, HubSpot’s tier points page, Microsoft’s Solutions Partner documentation for Azure, and Shopify’s Q3 2026 Partner Program Tiering Guide. All were opened and read on 14 September 2026. Shopify recalculates tiers on 1 October 2026.

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.

Every client I have spoken to in the past month has asked me a version of this question - how do I get cited by AI? The FOMO is tangible in their voices as they do not want to delay getting on the AEO hype train and wanted my opinion on what should go under their 2027 AI visibility plan. And honestly I did not have answers to these questions. I did try to throw some light on this in this piece on buyers researching through AI before they reach you. But now, I had my own new set of questions - is this just hype or is it something worth investing marketing budgets on? If it is not just hype, what should an organisation work on and what line items should they include in their budget?

So, I went looking for the answers.

Google answered this in May and nobody noticed

On 15 May 2026, Google published a guide to optimising for generative AI features. On the same day, it also clarified that its spam rules now cover AI answers. The first line of that policy now includes “attempting to manipulate generative AI responses in Google Search”.

The two entries sit next to each other in Google’s own changelog, which is where I found them. Google’s stated reason for writing the guide is the part I keep going back to. It is there to help people work out how to optimise, “and what they can ignore”.

So the help and the warning turned up on the same day, deliberately.

The list of things you do not need to buy

There is a section in that guide headed, more or less, here is what you do not need to do. Five things.

llms.txt. If someone has charged you to add one, Google Search does not read it. Google’s own wording is that it “will neither harm nor help your site’s visibility or rankings in Google Search”. People clearly kept arguing about it, because Google went back on 15 June and added another note saying the same thing more slowly.

Chopping your pages into small chunks. Not needed. There is no ideal page length either.

Rewriting your content so a machine can follow it. Also not needed.

Chasing mentions. Google names this one directly and is politely unenthusiastic about it.

Piling on structured data. Not required for AI answers, and there is no special markup to add.

Now read that list again as a shopping list, because that is what it is. Google went further on 5 June and published a page about third-party SEO tools and advice that names “AEO or GEO tools” as a category to be careful with, and says flatly that no third-party tool can see its ranking data or promise you an outcome.

I will be honest here and say that I was quite taken aback when I saw this list. It is quite the opposite of every “best SEO practices” that has been drilled into us for a decade.

The bit everyone has been getting slightly wrong

Worth slowing down here, because most of the coverage has smudged this.

The scaled content abuse policy still says the offence is producing pages “for the primary purpose of manipulating search rankings”. Those words have not changed. The link to AI answers is made in the optimisation guide instead, which says that spinning up a separate page for every possible way someone might phrase a question, in order to push into rankings or AI responses, breaks that policy.

Two sentences, two documents, one idea. And the policy adds that it applies “no matter how it’s created”. So this is not a rule against writing with AI. It is a rule against volume with nobody at the other end.

Then Microsoft wrecked my theory

I had a neat argument going. Google is the only one who has written any of this down. Then I opened Bing’s documentation and had to throw it away.

The Bing Webmaster Guidelines now say, in their opening line, that they cover how Bing surfaces content across Bing, Copilot and grounding API results. Follow them and your pages are eligible to be indexed, ranked and cited. Fail them and you can lose “eligibility for grounding experiences”, or be dropped from the index altogether.

Microsoft then does something Google has not. It uses the word GEO, defines it as content being eligible for citation in AI answers, and adds the sentence that never makes it into a vendor deck: neither SEO nor GEO guarantees you anything.

On manipulation it is more blunt than Google. Content “should be created for users, not to manipulate ranking systems or trigger citations or AI responses”. Trigger citations. That is the precise thing a large part of this market is selling.

It also gives you switches Google has not. One tag (NOARCHIVE) keeps your content out of Copilot answers entirely. Another (NOCACHE) limits Copilot to your title and link. There is even a way to mark which parts of a page Bing is allowed to quote, using the data-snippet attribute.

Every time I mention Bing in a meeting, clients are usually in a hurry to dismiss it or ask me how relevant is it anyway. And I always correct them that a lot of organisations and hence their customers use Copilot or Bing at work, and then they ask me to go over the slide again.

Where the two of them flatly disagree

Put the documents side by side and they tell you different things.

Google says do not restructure anything for AI. No chunking, no ideal length, no writing for machines.

Bing ties structure straight to eligibility. Get to the point early. One topic per page. Name things the same way each time. Make sure a claim stands up on its own, without the reader needing the rest of your site to decode it.

Neither of them is wrong. They are running different systems. But if someone handed you a single AI content checklist, they wrote it as though one answer exists.

The other three did not write rules at all

Now the ones your team actually talks about.

OpenAI’s page for publishers is a door policy. Let the search crawler in. Use noindex if you would rather your links were not shown. Block the training crawler if you want to. For measurement, ChatGPT tags referral traffic with a UTM parameter, and that is the whole offer.

Anthropic’s is the same shape. Three crawlers, what each one is for, what happens if you block it.

Perplexity’s is mostly firewall configuration, plus one sentence worth knowing. When a user asks it something, Perplexity “generally ignores robots.txt rules”.

Not one of the three publishes anything about what makes content eligible, what counts as gaming their answers, or how you would ever know it worked. I am not saying they owe you that. I am saying you should know it before you put a number in front of a CFO.

What you can actually measure

Microsoft’s AI Performance report gives you the most, and is still careful about it. It says plainly that it “does not measure rankings, authority, performance, or importance”, that the phrases it shows you are grouped rather than real queries, and that it will not tell you why a particular page got used.

If your 2027 plan has a line in it called AI-influenced pipeline, it is an estimate. We have done this before with a metric everyone eventually agreed to stop believing.

What I would actually put in the plan

Start with the question that never gets asked first. Which of these surfaces do your buyers use? Not all four. If you sell into Microsoft-heavy enterprises, Copilot is sitting inside Word, Outlook and Teams all day, and it happens to be the one surface with both a published rulebook and real retrieval data. If you sell to developers, it is Claude and ChatGPT, and neither will tell you anything at all. Answer that before you buy a tool.

Then fund the overlap. Where Google and Bing agree, you are on safe ground, and what they agree on is almost disappointingly ordinary. Write something original. Get your facts right. Make it easy to follow.

Bing’s structural rules only apply to Bing, so fund those if Copilot came up in the first question and leave them alone if it did not.

If you only take one thing from all this, take the defund list, because Google has already written most of it for you. One caution, and it matters. Google saying llms.txt does nothing is a statement about Google. Nobody else has said either way. That is proven for one surface, not four.

It is worth holding the whole thing up against what your sector already spends on search before anyone adds a fresh line to it.

What none of this proves

Every document here is a company describing its own machinery. None of it can be tested from the outside. Nobody outside these companies can check how any of these systems choose their sources, and that includes me.

Both publishers have a stake, too. Google telling you to ignore third-party tools and use Search Console suits Google very nicely. That does not make it untrue. It means a rulebook tells you what a company says it wants, which is not proof of what it does.

It is still more than three of them have given you.

The useful bit is not any single rule. It is that you can now tell the difference between someone quoting a rulebook and someone inventing one, and checking costs you about fifteen minutes.

None of this is settled, and there is still nobody neutral keeping score.

So the honest answer to the question my clients keep asking me, how do I get cited by AI, turns out to be another question. By which one. Two of the four will tell you what they want from you. Two will not tell you anything at all.

That is a thinner answer than anyone wants to put in a 2027 plan. It is still a better basis than funding all four as though they were one channel, which is roughly what most plans are about to do. And the part that survives all of it is the part nobody can sell you: knowing how your buyer actually behaves, and writing the thing that deserves to be quoted.

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One question, and I would like an actual answer in the comments: which of these four surfaces did you actually pick for 2027, and what did you use to decide?

Every document referenced above was read directly on 14 September 2026. Google’s pages carry last-updated dates of 5 June, 10 July and 28 August 2026. Bing’s webmaster guidelines and AI Performance documentation, OpenAI’s publisher FAQ and Perplexity’s crawler page were read the same day. Anthropic’s page is dated 7 April 2026.

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.

I run a marketing consultancy and a lot of my clients are plagued by the same conundrum - AI has made software cheap and easy enough to build that their customers will stop buying it from them. Every category that has a workflow in it will be replaced by something a competent team creates in an afternoon.

These are serious problems to have and I wanted to placate my clients. More importantly, I wanted to know if it was true, so last week I went looking for who is actually saying it.

The number you see everywhere

The figure doing most of the work in this argument is that 35% of enterprises have already replaced a SaaS tool with custom software. It is everywhere. I found it repeated in 9 of the first 10 results I opened.

It comes from a report published in February by Retool, and Retool’s own page is straightforward about where it came from: a survey of 817 Retool customers and builders. People who had already bought a platform for building internal software were asked whether they build internal software.

That is not a scandal. It is a perfectly normal customer survey and the company disclosed the sample plainly. The problem is what happened to it afterwards. Most of the pages repeating the number had quietly changed “35% of teams” into “35% of enterprises”, and almost none of them mentioned who was surveyed. A number about one company’s customers became a number about the economy in about a fortnight.

I have written before about how two benchmark numbers can look like a disagreement when they are really just two different sets of companies, so I will not do that again here. But the pattern is the same, and it is worth noticing that the most-cited datapoint behind “AI is killing SaaS” is a vendor’s customer list.

The companies that actually did it

So I went looking for real cases, and there are real cases.

Palantir told investors in May that it had replaced what it called its old expensive CRM with an AI-first system built on its own platform, in a few months, and that the people using it loved it. GoDaddy said it was testing the replacement of smaller third-party SaaS tools with internally built solutions, particularly across corporate functions. C.H. Robinson said it was scaling its own custom-built AI agents instead of stitching third-party products together.

These are not hypotheticals. Somebody’s renewal died inside each of them.

Now look at who these companies are. Palantir and GoDaddy sell software. C.H. Robinson mentioned its 450 in-house engineers and data scientists in the same breath, and said outright that this is what lets it move faster than a buy-and-integrate model.

These are not customers who found a clever tool. They are engineering organisations with the capacity to build, insourcing work they were always capable of insourcing, and doing it faster than before. That is a change in speed. It is not evidence that your mid-market accounts are about to do the same thing.

The place I expected to find the panic

If software companies genuinely believed their customers were about to build the product themselves, there is one place they would have to say so.

Public companies file a 10-K every year, and in it they list the things that could damage the business. That section is written by lawyers, reviewed by auditors, and read by people who sue. Companies put uncomfortable things in it precisely because failing to disclose a known risk is worse than disclosing it. If the ground were shifting, this is where it would show up first, in cautious language, before anyone said it out loud on a stage.

The SEC publishes a full-text search over all of it, free. So I counted.

Every number below is the count of filings containing that exact phrase, form type 10-K, filed between 1 January and 31 August of the year in question. I held the window identical across all seven years so that a partial 2026 is measured against the same partial window everywhere else. Set those dates yourself and you will get these figures back.

Mentions of “artificial intelligence” in 10-K filings went from 384 in 2019 to 3,776 in 2026. “Generative artificial intelligence” went from appearing in nothing at all to 603 filings. Software companies are clearly not quiet about AI.

Then I counted the other thing. “Customers may choose to develop” ran 18 filings in 2019, 20 in 2023, 16 in 2025 and 22 in 2026. “Internally developed solutions” went 19, 21, 18, 20, 20, 17. “Internally develop” peaked in 2023 and has come down since. “Develop their own solutions” peaked in 2024 at 37 and has fallen to 26. “Build rather than buy” appears zero times, in every year I checked.

The pool those come from is stable. Roughly 6,000 10-K filings mention software each year and that number has barely moved since 2019, so this is not an artefact of more companies filing.

Seven years. A tenfold rise in how much these companies talk about AI. And no change at all in how many of them warn investors that customers might build the product instead.

The part where I nearly got this wrong

The first phrase I searched was “build their own solutions”. It went 1, 1, 1, 2, 5, 3, 5, 7 across those same years. A rising line, in the right direction, sitting exactly where I wanted it to sit.

If I had stopped there I would have written the opposite article, and it would have been more fun to read. Vendors are quietly warning investors about the thing they will not say publicly. It is a good piece. It is also wrong, because that line moves from one filing to seven, and one filing is a rounding error, and it did not hold in a single other phrasing I tried afterwards.

I am telling you this because it is the whole method. A number moving is not a finding until you have checked whether it moves anywhere else. You can rerun all of this yourself in a browser in about ten minutes, and I would rather you did than take my word for it.

What the market did while everyone was declaring it dead

Gartner has the enterprise application software market growing 13.7% this year, reaching $740 billion by 2029, and its most recent forecast has that growth continuing in double digits into 2030.

One honest caveat, because it cuts against me slightly. A good part of that growth is enterprises buying AI capability inside their applications, which is not the same as the old categories holding steady. Some of that money is going somewhere new. But it is still going out of the door and into someone’s software, and it is not being redirected into internal builds at anything like the scale the narrative suggests.

So what is actually happening to you

Here is what I think is going on, and it is worse than the version where you can dismiss the whole thing.

Your customers mostly cannot build it. What has changed is that they now believe they could.

That belief does not need to be correct to cost you money. It shows up as a procurement team asking what your product does that an internal team could not do in a quarter, and it shows up in the renewal as a number rather than an argument: a discount, a shorter term, a seat count trimmed at the edges, a two-year deal that becomes a one-year deal.

And none of it is visible to you, because you do not lose those deals. You keep them, slightly worse. There is no closed-lost reason code for a customer who decided you were replaceable and negotiated accordingly. The strongest version of the threat is the one that never produces an event your CRM can record.

This is the same shape as something I have argued here before. When a deal you thought you had won goes quiet inside the buyer’s own review process, the loss is real and it is invisible in the pipeline. This is that, moved one stage later and made permanent.

What to do about it, given that it is a belief and not a build

Beliefs are answered with evidence, and you are almost certainly not carrying the right evidence.

The first thing is to stop selling against a competitor and start selling against the internal build, because it is now in most competitive sets whether or not you have put it there. That means knowing, specifically and per account, what the thing costs to maintain rather than to make. The build is not the expensive part and never was. It is the second year of it: the integrations someone has to keep alive, the compliance surface, the person who leaves.

The second thing is to work out which of your accounts can genuinely do this. It is not many, and it is knowable. Engineering headcount is a real signal and you can see it from outside. So is whether the thing you sell sits in a corporate function, where a rough internal version is tolerable, or in the revenue-critical core, where it is not. Palantir replacing its own CRM is the uncomfortable case, because that is nobody’s admin tool. But Palantir also employs the people who built it.

The third is a measurement problem more than a marketing one. If the cost shows up in renewal terms rather than lost deals, then someone has to be watching term lengths and discount depth as a competitive signal instead of a finance one. I do not think most GTM teams have anyone doing that, and it is a cheaper thing to start than a repositioning.

What I would not do is build the ROI calculator. Everyone reaches for that here, and a spreadsheet that proves your product is cheaper than a build is a spreadsheet your buyer knows you commissioned.

The annoying part about this whole ordeal is that the narrative, albeit overstated, is still going to show up on your quarterly reports. Put the internal build into your competitive set and sell against it properly, and the belief stops quietly costing you money.

So here is what I actually want to know. The last time a renewal came back worse than you expected, did anyone write down that the customer thought they could build it themselves? Or did it go into the system as pricing pressure and get forgotten? Tell me in the comments, because I suspect almost nobody is recording it.

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Filing counts were run on 31 August 2026, using the search parameters set out above. All other sources were checked on 7 September 2026.

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.

For about a decade, the most repeated piece of advice in demand generation was to cut your form fields. Every extra field costs us conversions. Get it down to 3, or 4, or whatever number the agency blog said that year.

And companies listened. Gong’s demo form now asks for 1 thing.

The questions did not go anywhere. They just moved somewhere you cannot see them.

I opened the demo request forms at Adobe, HubSpot, Clay and Gong on 20 August 2026 and read what each one actually collects, including the parts that are not on the screen. I did not submit any of them. Everything below is on the public page if you want to check it yourself.

Adobe asks for 10 things and works out another 20

Adobe’s request form has 10 visible fields and every one is required. First name, last name, business email, business phone, job title or role, department, organisation name, country, postal code, primary product of interest.

By the standards of the advice above, this form is blasphemy. The long form everyone was told to kill.

Except the visible 10 are not the interesting part. The page carries 52 fields. Alongside the ones you fill in, there is a block that fills itself: employee count, SIC code, revenue range, internet service provider, city, street address, all lifted off your IP address as you type. There are 8 separate campaign attribution fields. And there are 3 consent flags rather than 1, tracking permission to email you, to call you, and to send you physical post.

Adobe is the honest one. It asks you for a lot, it takes a lot, and the ratio between the two is roughly 1 to 2.

Keep this ratio in mind.

HubSpot gives you three doors

HubSpot’s get-started page opens with no form at all. Two buttons: get a demo, or get started free.

That second button is rarer than it should be. It is a company saying you may buy without being sold to.

Choose the demo and you get 6 fields, all required, phone among them. Choose free and you skip the entire apparatus.

A third door opens by itself. An AI assistant appears beside the form offering to chat with sales, book a demo, get you started free, or help with your account. The same page runs a form, a self-serve route and an agent, simultaneously.

One more thing, which is the tell. That 6-field form is rendered 9 times on the page, once per product, each instance carrying its own identifier. The form is identical every time. The only thing that differs is which one you touched. HubSpot is not asking you 9 different questions. It is recording which shelf you were standing in front of.

Clay makes you identify yourself before you can say what you want

Clay asks for 5: first name, last name, business email, phone, and a dropdown asking which best describes you. Four are required.

Then there is a Next button, and behind it a second step where you choose your use case from about 15 options. Account scoring, intent signals, automated outbound, list building, CRM clean-up, and so on.

So the sequence is name, email, phone number, and only then are you permitted to say what you came for. The thing you actually want is the last thing anyone asks about.

While you are still on step 1, the page is already working. Change the dropdown from rev ops to marketing and the entire right-hand column rewrites itself. The heading swaps. The testimonials swap. The statistics swap. Choose rev ops and you get ElevenLabs on automated lead scoring. Choose marketing and you get Verkada on ad targeting match rates. You have submitted nothing. You have been segmented and re-pitched anyway.

There is a sentence beside that form, from a customer, about how every lead arrives pre-qualified, scored and routed automatically. It sits directly next to the box where you type your phone number in by hand.

I do not think that is hypocrisy. I think it is the most honest thing on the page. Automation happens after you have identified yourself, never before, because the whole system runs on knowing who you are first.

Gong asks for one thing and the page carries thirty

Gong’s form is a single box. Work email. One button. A consent line underneath. Even the site navigation has been stripped off the page, so there is nowhere else to go.

By the logic of the advice, this is the perfect form.

There are 31 inputs on that page. Twenty-nine are hidden.

Most are the ordinary furniture of a marketing stack: the UTM set, a Google Ads click identifier, an analytics client identifier, the page you arrived from, your billing country, a partner referral key. Everyone has those.

Three are not furniture.

There is a field called Clearbit_Job_Seniority__c. That is the job title question. It did not get deleted when the form got shorter. It got answered on your behalf, from your email domain, before you finished typing.

There are fields called mk_customer_fit_segment__c and mk_customer_fit_signals__c. You are being scored while the page is open.

And there is a field called self_schedule_presented__c. That one records whether you were shown the option to book a slot straight away.

Read that again. Somewhere in the system there is a decision about whether you are the kind of visitor who gets to skip the queue, and it is made before you have told anyone anything except your email address.

Who gets to skip the queue

Line the four up by how much they ask.

Adobe asks for 10 things, and works out your employee count, revenue range, SIC code and street address from your IP address while you type. HubSpot asks for 6, and offers you a free door out and an agent sitting beside the form. Clay asks for 5, then another 15 behind a Next button, and rewrites its own testimonials to match whichever role you pick. Gong asks for 1, infers your seniority from your email domain, and scores your fit before you submit anything.

The ask collapses from 10 to 1. What sits underneath it does not.

That is the whole thing. The industry optimised the visible surface and left the collection intact underneath it. Nobody removed the questions. They just stopped asking you.

Which is why the field-count argument was always the wrong argument. It measured the only part of the system the buyer could see.

It is worth knowing what the most-quoted rebuttal actually says, because it does not say what people use it to say. RevenueHero, which sells inbound routing software, analysed over a million form submissions and found top performers converting at 77% with 2 fields and 76% with 13. That pair of numbers now circulates as proof that field count does not affect conversion. It measures something else. It counts how often an already-submitted form becomes a booked meeting, which starts after the moment the field-count debate is about.

So the case for shorter forms has not been refuted. That study never measured the thing shorter forms are supposed to protect, which is whether people finish the form at all.

I am not against enrichment. Guessing a company’s headcount from its domain is not a crime, and a shorter form genuinely is kinder. The problem is narrower than that and worse. When a question is visible, you can decline to answer it. When it is inferred, you cannot. And when a field like self_schedule_presented__c exists, the sorting has already happened by the time you would have had a chance to object.

What to do with this on Monday

Not a field count. Four questions about your own form, which is almost certainly an accident nobody has audited since the last person left.

Who wrote each field, and are they still here? Most demo forms are a sediment of requests. Sales wanted headcount in 2023. Someone wanted attribution. Compliance wanted a consent box. Nobody removes anything. Go through yours field by field and name the person or the reason. Any field without one comes off.

What are you inferring, and would you say it to the buyer’s face? That is the test, not whether enrichment is permitted. If the answer is no, the inference is the problem. Hiding it is not a fix, it is just the reason nobody has had to deal with it.

Does a fast lane exist, and who decides who is in it? If you route some visitors to instant booking and others to a queue, that is a real call affecting real revenue. It should be a decision someone owns, with criteria written down, not a setting inherited from whoever configured the routing tool.

Is there a door for people who do not want to talk to you? HubSpot has one. Gong does not. Neither is automatically right, but it should be a choice about your buyer rather than a habit.

I was once building a lead form for an acquisition campaign. The sales team wanted postal code on it, so they could work out which account manager the lead belonged to. I knew it would cost me drop-offs. They knew it would get someone on the phone faster. I lost that one, and the field went on the form.

What I did not appreciate at the time is that we were arguing in the open. The field was on the page, the buyer could see it, and I got to lose the argument in a room with other people in it.

Adobe still asks for postal code. Gong works out where you are from your IP address, and nobody has the argument at all.

What is on your demo form that nobody can justify any more?

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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.

As we enter the second half of the year, I was reminded of the dreaded yearly ritual of budget allocation. Now I have sat on both sides of this - inside a bank and telco, belligerently defending a number I built on an excel sheet, and outside as a consultant watching client leadership build one, hoping and praying that my retainer makes the cut. The ritual is the same across the board; a senior will ask you what the number should be and everyone starts scrambling for benchmarks to justify their asks.

The number you will find in 90 seconds

During your budget discussion, a senior is going to ask you what share of the revenue should marketing get next year. And it will take about 90 seconds for you to find the answer. Let me make it easier for you.

Two surveys dominate every result. Gartner’s 2026 CMO Spend Survey puts marketing at 7.8% of company revenue, up slightly from 7.7% the year before. The CMO Survey, run by Duke’s Fuqua School of Business with Deloitte and the American Marketing Association, puts it at 9.0%.

That looks like a disagreement. It’s really not.

Gartner surveyed 401 CMOs across North America, the UK and Europe between January and March 2026, and the vast majority run companies above $1bn in revenue. The CMO Survey polled 308 US marketing leaders in January 2026 across every size band, with roughly a third of respondents under $100M in revenue. Smaller companies spend a higher share of revenue on marketing. Two populations, two averages, nothing to reconcile.

Two things to watch while you are searching. A 9.4% figure still circulates widely as the current number; that is the spring 2025 reading. And marketing as a share of company revenue is not the same as marketing as a share of the total company budget, which sits at 9.6% and has fallen to its lowest level since 2021. The two get swapped freely.

So you pick one. Most people would be tempted to pick 9.0%, because it comes from a free academic survey that publishes its methodology, and because it is the more generous of the two.

That is where the trouble starts.

The same report already has your number in it

The CMO Survey does not report one figure. It splits its results into four economic sectors, and the split is on a single slide that almost nobody quotes.

SectorMarketing as % of revenueB2C product12.0%B2B services10.1%B2C services7.2%B2B product7.0%

If you sell a B2B product, your peer figure is 7.0%. Not 9.0%. The average you were about to plan against is being pulled upward by consumer product companies spending nearly twice what your sector spends, and the gap is not a rounding difference. On $50M of revenue it is the difference between a $3.5M budget and a $4.5M one.

Keep reading the same report and the picture gets sharper, because the sector cuts run through every question.

Over the prior 12 months, overall marketing spending at B2B product companies changed by −0.2%. It did not grow slowly. It shrank. B2B services grew 2.8%. B2C services grew 8.0%.

Marketing organisations at B2B product companies grew 0.0% over the same period. Not slowly. They did not grow at all for the surveyed orgs. B2B services added 3.9%, B2C services 4.6%.

When profits are lower than expected, and executives decide to trim the fat, marketing is the thing they cut 49.9% of the time at B2B product companies, the highest rate of any sector. At B2C services it is 28.8%.

And underneath that sit the softer readings, which point the same way. B2B product companies rate the importance of their marketing capabilities at 5.6 on a 7 point scale, the lowest of the four sectors. They rate digital marketing’s contribution to company performance at 4.83, also the lowest. They rate the CFO as a business partner in building the case for marketing spend at 4.3, against 4.9 for B2C services.

Each of those numbers sits on its own slide but once read in sequence, they describe a sector that spends less, grew not at all, gets cut first, and has the weakest finance relationship of anyone in the survey.

There is one reading that cuts against this, and it matters. B2B product marketing leaders are the most likely of any sector to be asked into board meetings, at 5.5 on the same scale, and into earnings calls at 5.3. Both are the highest of the four groups.

So the importance is real. B2B product marketers are in the room. They just don’t get funded.

Half of these companies spend 5%

There is a second document, and it changes the shape of the whole exercise.

The CMO Survey publishes a Highlights report and a Topline report. The Highlights report is the one that gets read, screenshotted and quoted, and it reports the mean. The Topline report gives the full distribution for every question, and hardly anyone opens it.

So me, your friendly neighbourhood GTM person, went through the reports to make your job easy.

Marketing as a percent of company revenues: mean 8.96%, median 5%.

Overall marketing spending change over the prior 12 months: mean 1.74%, median 0%.

Read the second pair again. Half the companies in this survey did not move their marketing budget last year. Not up, not down. Flat. Zero. The 1.7% growth figure that gets reported as the headline is produced by a minority of companies making real increases while the middle of the market stood still.

And on the revenue share, half of these companies are at 5% or below, against an average of 9.0%. The distribution is not a bell curve with a fat middle. It is a long tail of heavy spenders dragging an average away from where most companies actually sit.

This is not a flaw in the survey. It publishes both figures. It is a choice about which one to lead with, and the same report uses the median elsewhere, reporting that the median duration of marketing’s effect on customers is 6 months. So the median is there when it matters. It just never gets applied to the number everybody quotes.

Where the money actually goes

Knowing how much to spend is half a budget. The other half is what it gets spent on, and here Gartner is the better source, because it publishes the split.

Gartner divides the marketing budget four ways. Paid media takes 31.4%, labour 24.5%, martech 19.4%, and agencies the remainder. Those figures come from Gartner’s 2026 survey as reported by Chief Marketer in June 2026.

The direction matters more than the levels. Paid media is at a five-year high, up from 25.1% in 2021. Martech is at a five-year low, down from 26.6% over the same period, and that is happening while 62% of CMOs say they plan to invest more in martech. Labour has risen from 21.9% to 24.5% over the past year, while 43% say they expect to reduce labour spending. Both are worth sitting with, because in each case the stated intention and the actual allocation point in opposite directions.

Digital now takes 67.5% of marketing expenses, up from 54.9% in 2023. Within digital, search advertising takes 16%, social advertising 15.7% and SEO 9.4%.

The offline half is where B2B looks least like everyone else. Across all respondents, event marketing takes 23.1% of offline budget, up from 19.4% the year before, followed by sponsorships at 18.2% and linear TV at 15.5%. Split by sector, B2B CMOs put 27.6% of their offline budget into events against 16.5% for B2C, and 13.2% into linear TV against 19.9% for B2C. If you are defending an events line this year, that is the number to defend it with.

One more worth planning around: 56% of organisations increased their allocation to consumption-based martech pricing, and half of those that adopted it are continually renegotiating contracts to avoid usage and cost spikes. If you are moving to usage-based tooling next year, the renegotiation is not an exception. It is the operating model.

A caveat that follows from everything above. This is the Gartner population, which means companies above $1bn across North America, the UK and Europe. Treat the split as shape rather than prescription. The direction of travel holds better than the actual numbers do.

The plan is not the budget

One last thing, and it reframes what you are actually doing this month.

Goal attainment across the Gartner sample fell on every measure year on year. Only 49% of CMOs exceeded their brand awareness goals, against 57% a year earlier. Only 46% exceeded campaign impact objectives, against 58%. Only 43% exceeded ROI objectives, against 56%. The share falling short of acquisition goals rose to 20%, from 13%.

And 62% said that an inability to meet 2026 growth expectations would result in marketing budget cuts.

Meanwhile, 20% of teams already reallocate more than 5% of their budget across channels every month. Another 38% do it quarterly, and 14% do it continuously.

So the number you write down in November is not the number you will run. It is an opening position that will be revised within a quarter, and cut outright if the first miss arrives. Which means the plan’s real job is not to be accurate. It is to decide, in advance and in writing, what survives the first miss and what does not.

The benchmark cannot tell you that. Your sector’s actual position can get you closer.

I hope you now have all the arrows in your quiver to respond to the senior when they ask you what share of revenue should marketing really get. You now have your sector’s actual figure, the information that half the market sits at 5% or below, and the channel split to answer where the budget is being allocated to. The takeaway for the stakeholders in the room should be that discussing these benchmarks is not worth more than 10 minutes; the real decision is what to hold on to when the return is not as expected.

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One question, and I would like an actual answer in the comments: what percentage is in your 2027 plan right now, and which of the four sectors did it come from?

Figures in this piece come from The CMO Survey 2026 (35th edition, 308 US respondents, fielded 7 to 29 January 2026) and the Gartner 2026 CMO Spend Survey (401 CMOs, North America, UK and Europe, fielded January to March 2026). Gartner’s budget mix figures are drawn from Chief Marketer’s June 2026 reporting, as Gartner’s own summary page no longer carries them. All sources checked on 9 August 2026.

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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.

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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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 nine 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. Every Monday: one go-to-market claim, checked against the primary source, for people who have to defend a plan, a budget or a metric. A teardown, a playbook, or a hot take. No filler, no recycled LinkedIn wisdom.

Where to begin

Three pieces to start with.

The numbers you need before you build next year’s budget. The marketing budget figure everyone quotes is an average. The median, and the number for your sector, tell a different story.

Everyone says AI is killing SaaS. The filings disagree. Ten times as many annual reports mention AI as in 2019, while the warning that customers will build the product themselves has barely moved.

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 Monday. Sources are linked, so you can check them yourself. The weekly issue 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.

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