6sense renamed itself around the same idea within three weeks, and Forrester expects agents to influence three quarters of B2B purchase decisions by 2027. If you buy B2B media, or sign off the people who do, every pitch you take in Q1 will have the word agentic in it. This piece is about what B2A actually is, what it needs to work, and the one question that separates the pitches worth your time from the rebrands.

A definition first, because the term is doing two jobs. As Demandbase uses it, B2A means the buyer’s agent is now part of your audience: the assistant that drafts the shortlist has to be able to find you, read you and verify you, or the human never sees your name. That is not the same as running ads the agent sees and is swayed by. OpenAI says plainly that ads do not influence ChatGPT’s answers, and the sponsored units inside AI Mode and Copilot reach the person sitting beside the agent, not the agent. What moves the agent is what it can read and check. The second job, and most of this piece, is the agent on your side of the deal: the one that plans and buys the media, whether that is a platform’s own agent, an agency’s, or an assistant working through an MCP against an account graph. I use B2A for the era in which both exist, because one question decides whether either is any use: what is it allowed to see?

The belief I want to push against is that in B2A the agent is the hard part. It isn’t. By the end of Q1 every DSP, SSP and ABM platform will have shipped one, and most will be competent, because the models underneath are the same handful.

The hard part is what the agent is allowed to see at the moment it decides where your money goes. An agent that can’t see the account isn’t doing account-based anything. It’s buying impressions, faster, and describing them back to you in better English.

What you’ll learn in this article:

• What B2A really means, and why 6sense and Demandbase both moved to it inside three weeks

• The three things an agent needs before it can buy B2B media on an account basis

• Why an agent that only sees one platform’s data ends up buying for that platform

• How to audit an agent you didn’t build, by extending the log test to machines

• The five questions to put to any agentic pitch before you sign a Q1 pilot

What is B2A, and why did 6sense and Demandbase both move to it inside three weeks?

Two conference stages, three weeks apart, the same move from both major ABM platforms. On 20 October Demandbase used its GO conference in New York to launch three agents (Campaign, Audience and AEO) and the term itself: B2A, marketing built to be seen and trusted by a buyer’s agent as well as the buyer.

Five days earlier it had been named the flagship data partner for Hunter, the outbound sales agent Salesforce is piloting, so its account, buying-group and intent data now feeds an agent it doesn’t own. Then on 3 November 6sense opened Breakthrough in Las Vegas as the “Partner for Agentic GTM”, with a product slate to match: a BDR Agent in beta this month, AI Visibility in beta from December, and a Sales Intelligence MCP connection that went generally available the same day, so other people’s agents can read its account intelligence directly.

Look at what both chose to ship first and it’s the same thing: distribution of their data into agents they don’t control.

Demandbase into Salesforce’s agent. 6sense into anything that speaks MCP. Neither led with “our agent will run your campaigns”. They led with “whatever agent runs your campaigns will need our account layer underneath it”. That is a bet on where the scarce asset sits, and I think it’s the right one. The agent is becoming the interface. The intelligence it reads is becoming the product.

However, both stories stop at sales and marketing agents. Neither describes what happens when that account layer has to feed a media-buying agent working across a DSP, a CTV platform, LinkedIn and a publisher’s own agentic sales desk at once. That is the problem this piece is about, because it’s the one that turns up on an invoice.

What does an agent actually need before it can buy B2B media?

Forrester published a short piece on 10 November on agentic advertising’s productivity paradox. This is actually a topic that’s quite close to me and one for another day in a feature piece

Its central point is right: layering agents on top of broken processes automates the inefficiency ( I like to call it “automating chaos.”), and the real work is a new division of labour in which people set strategy and guardrails and agents run execution.

It cites the old finding that every dollar spent on hardware needed up to nine dollars of process redesign before it produced anything. The agent is the one dollar.

Its description of what buying becomes matters more: traders stop translating a strategy into hundreds of platform settings and instead express objectives, constraints, audiences, economics and brand-safety requirements for the agent to act on. The scarce skill becomes knowing what to encode. Outcomes is the ultimate end stage of this

In B2B, the thing you have to encode is the account, and that changes the order of operations. From the side of the business that has to make this work inside an auction, an agent needs three things before it goes near a budget, in this order.

First, an account layer it can read before it bids. Not a list, a graph: the named accounts, the buying-group personas inside them, which signals on each account are live and which have expired, and where those people can actually be resolved. The agent doesn’t bid in the auction, because nothing that reasons in seconds gets near a decision made in milliseconds. It sets the conditions for the bid. The account resolution has to happen on the sell side and travel inside the deal IDs the agent is handed, which is the containerisation argument from the addressability matrix piece.

So the agent’s eyesight is exactly as good as the account layer inside the deal.

That is what I mean by an account-blind agent: not a weak model, a model with nothing to look at. Hand it a wholesale CRM export and it will optimise, at machine speed, towards accounts that stopped existing years ago.

Second, an exposure log it can’t edit. Every impression the agent buys, recorded at account, placement and creative level, written somewhere the agent doesn’t control. Without that you can’t tell the difference between an agent that reached 400 named accounts at depth and one that found the 40 cheapest and hit them 300 times each. Both produce a lovely reach number.

Third, instructions written in account terms rather than platform terms. “Reach these accounts, at this depth, across these channels, inside this frequency cap” is an instruction an account-based agent can be held to. “Maximise conversions” is an instruction a platform’s agent will read as “maximise the thing we count”.

This is something that I’ve been spending a huge amount of my time over the past three months thinking through. In 2027, at FunnelFuel, we’re going to break new ground with an MCP that takes audience lockups, segmentation, hygiene, intent modelling, and planning all the way through to activation and back around to the best reporting data in the market. This whole system is predicated on giving the agent the B2B data modelling (from accounts to personas to buying groups) required in order to do the jobs that we’re outlining in this piece today

Why does a single-platform agent end up buying for the platform?

The sentence in Forrester’s piece I’d put on the wall is this one: an agent that only sees data inside one platform will optimise for that platform’s yield, not the advertiser’s return. The agents being pitched to you this quarter are being built inside platforms whose own yield is under more pressure than it has been for a decade, by the people who need that yield. Read into that what you will

Take Google. Its search revenue grew 12% in the third quarter against 17% in the second, and on the earnings call its chief business officer said plainly that lower monetisation of AI Mode queries was part of the reason. Alphabet lost around $500bn of market value in the days after. Google’s answer was not fewer ads: AI Mode ads are expanding to ten more countries, with the UK in the fourth quarter, and into follow-up questions in the US. AdExchanger reported this week that six advertisers and agency executives had told The Information they’d moved budget from Google to Meta. Whatever agent Google offers B2B advertisers next year will be built in that context.

Then take Amazon, the clearest picture of what opaque automation does to trust. The FTC and 22 states sued it on 31 August, alleging that its auction mechanics pushed prices towards advertisers’ maximum bids while the system was presented as a second-price auction, adding more than $20bn to advertisers’ costs. Amazon disputes all of it, and the point here isn’t who is right. It’s that an auction whose clearing price nobody can predict in advance is one advertisers have to take on trust, and agentic buying makes that worse unless the record of what happened is designed in from the start.

Two rival protocols now exist for agent-to-agent media buying, AdCP and IAB Tech Lab’s AAMP. However, Adform’s Jochen Schlosser said in September that little money moves through either, and Google, Amazon, The Trade Desk and Microsoft were absent from AdCP’s founding roster. If you want an agent that can see the account across platforms, you will be bringing the account layer yourself, or paying someone who does.

Know someone about to sign an agentic pilot on a platform’s own agent? Send them this section before the kick-off call, not after the first invoice.

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How do you audit an agent you didn’t build?

The log test, from the marking-their-own-homework piece, was simple: don’t take the vendor’s report, take its logs, and reconcile the two. An agent needs the same test with one extension, because it makes decisions as well as reporting on them. You need to see what it saw, what it did and what it was told.

What it saw is the account layer at the moment of decision, including how fresh each signal was. 6sense’s explainability work, in open beta this month, is the most honest attempt at this I’ve seen from a platform: it tells humans and agents how much weight to give a given call. However, it explains the score, not the media buy. Ask any media agent for the equivalent: at the point it allocated budget, which accounts did it believe were live, and on what evidence that hadn’t already expired?

What it did is the exposure log, at account by placement by creative level, held somewhere the agent can’t touch. PubMatic reported on 9 November that its AgenticOS ran more than 120 fully autonomous campaigns in the third quarter, up from more than 80 in the second, and created more than 14,000 AI-built deals. The question for a B2B buyer is what those deals were built from, and whether the record of delivery sits with the buyer or the seller.

What it was told is the instruction set and the guardrails. Forrester’s advice is to train practitioners to interrogate why an agent moved $100,000 from CTV to paid social. In B2B the question is simpler and harder to dodge: which accounts did it reach, which did it over-serve, and which did it never find at all?

An agent without an account graph is a very fast way to buy impressions.

What should you ask before signing anything agentic in Q1?

Five questions, in the order I’d ask them.

1. Which account layer does the agent read before it bids, who maintains it, and how do expired signals get removed from it?

2. Can the agent act across platforms, or only inside this one? If only inside, whose yield is its objective written in?

3. Can I get the exposure log at account level, in a form the agent cannot edit, to reconcile against the report?

4. Is the agent’s objective written in my accounts or in your KPIs?

5. Where are the dollar thresholds, the frequency caps and the kill switch, and who holds them?

A good answer to the first question is a named data source with a refresh cadence. A good answer to the third is “here is last month’s log”. A vague answer to either is the pitch telling you what it is.

One more thing, because this can be read as an argument for pulling spend off the open web, and it is the opposite. Of all the channels an agent might buy, the open web is the one where account resolution inside a deal ID is a mature, working mechanism today, and where the exposure log is yours by default. The walled gardens and the new AI surfaces are where an agent is blindest, because the account layer stops at their door. The account graph is what lets an agent buy the open web well, and it is the thing you will need to carry into every other channel too.

So is B2A a new era, or a new interface?

B2A is real, and the direction is right: the intelligence is leaving the dashboard and travelling with the decision, on both sides of the deal. But the vendors doing the repositioning are telling you, through what they shipped first, that the agent isn’t the scarce part. The account layer is. Agent-based marketing is account-based marketing with a new interface on the front, and an account-blind agent, however good its model, is the old impression machine with a better vocabulary.

So the agentic pitches worth your time in Q1 are the ones that can answer the first question on the list above without reaching for a slide. The rest are a rebrand, and you should price them as one.

Which brings me to the question I’d like your answer on, and it is the title put another way: is the agent the product, or is the account graph the product and the agent simply the next interface onto it? I think it’s the second. If you think the agents get good enough, quickly enough, that the data layer underneath them becomes a commodity, tell me why in the comments.

This piece looks at the guarantees a B2B media agency or media owner can put its name to, the ones it should refuse, and what carrying the risk costs. It matters now because it’s budget season: finance and procurement want part of the fee at risk, “outcomes” is on every media owner’s slide, and agency teams are being asked to sign something new.

In a tough economic climate, including rising interest rates, two global wars, and a wider supply chain challenge, it’s no wonder that the guaranteed outcomes are particularly attractive as we stare 2027 in the face.

However, nearly every version of that conversation I hear starts from the same belief: that a guarantee means promising leads, pipeline or revenue. My view is that this is the wrong place to start, because those are the things an agency controls least, and a guarantee on something you don’t control is either a bet or a bluff. So let’s take it in order.

What you’ll learn

• Why performance incentives are at a 34-year low

• What separates a guarantee from a target

• Why the platform guarantees lock B2B out

• Three guarantees a B2B agency can sign, and two it should refuse

• Who carries the risk, and the measurement a guarantee needs

Why is everyone suddenly talking about guaranteed outcomes?

Start with the number that explains the mood. The ANA’s 2025 Trends in Agency Compensation study found that only 15% of marketers use performance incentives with their agencies, a 34-year low, down from 61% in 2013. Value-based pricing has fallen to 8%, from 20% in 2016.

Two flags: a sample of 99 marketers, and a linked article written by Decideware, a vendor, with the full report paywalled. The direction holds, though, because the same study has 86% of marketers planning to revisit agency compensation within 12 months, only 28% with adequate visibility into how their agencies make money, and 84% naming the absence of shared measurement as a barrier. Gartner’s 2026 CMO Spend Survey adds the pressure: paid media is now 31.4% of marketing budgets, “funded by cuts to agencies”.

The holding companies know it.

WPP’s CFO, Joanne Wilson, said in May that performance-linked fees now make up 20–25% of net sales. However, WPP’s CEO, Cindy Rose, says fully outcome-based pay is “a few years” away, and Publicis’ CFO put it at “roughly 10% of our total remuneration… still very early days”. Meanwhile, outcomes-focused buying technology like Viant is markedly outstripping the growth (or lack of) seen by the trade desk, and wider ad tech service businesses like Channel Factory have seen huge growth since adopting an outcomes-predicated buying model - all of which makes it fairly evident that there’s a market for this.

The gap between the talk and the contracts is measurement. MediaSense and the WFA’s June 2026 report (a Meta co-sponsored survey of 77 agency leaders, so directional) found 85% expecting outcome-based pay to grow, with attribution (69%) and data access (68%) as the barriers and hybrid models expected to win.

And measurement is the obvious bottleneck to all of this, not least in B2B. The reality is, attribution and tracking has only gotten more challenging as the privacy net has tightened and the funnel has gotten darker - meaning, one way or the other, more of a buying journey is happening in environments where we can’t place a pixel, so when we can’t infer the progress towards pipeline.

What actually counts as a guarantee?

A guarantee has three parts, and most of what the industry calls one is missing the third: these three things are

1. A KPI - what is the outcome that we’re talking about?

2. An agreed way of measuring it

3. And a remedy if it’s missed.

Take away the remedy and you have a target. Take away the agreed measurement and you have an argument.

The clearest public template is Epsilon’s Ad Quality Guarantee: viewability of at least 50% and fraud at or below 0.99%, measured by IAS, with four make-good impressions for every failed impression. The small print: a $100k minimum spend per 30 days, make-goods that expire after 90 days, and an offer Epsilon can revoke.

Compare Amazon’s DSP terms, where below 70% viewable delivery the parties “use commercially reasonable efforts to agree upon the conditions of a makegood flight”. That is a promise to talk, which makes it a target.

Other agencies are only getting close to an outcomes model when a predefined flight of media on a regular CPM has been run beforehand.

This is creating a very clear divide between outcomes as an objective and guaranteed outcomes as a contract point

Xandr’s Guaranteed Outcomes is the third template, and it prices the risk: the buyer pays per view or completion, the seller is still paid on CPM, and Xandr sits in the middle carrying the risk for a 10% market-making fee. Beyond those three I found no true pay-on-outcome guarantee product from the big TV networks or the measurement firms; they mostly sell measurement and benchmarks, and a benchmark without a remedy is a target.

To be brutally honest, most of these “outcomes” are soft media metrics, or, in the case of viewability standards, it should be the de facto norm and not need guaranteeing anyway. So most of what we talked about above doesn’t really count as outcomes at all - which is why we’ll now move on to talking about outcomes in the way that I would see an outcome being, and line it up against B2B specifically.

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“Can we run ChatGPT ads?”

It arrives in different forms. Can we buy into Perplexity. What is Google doing in AI Mode. Is there a programmatic route. But the question underneath is always the same one, and it is a good instinct badly aimed. The instinct is that the buyer has moved and the budget should follow them. That is right. The aim is that the way to follow them is to buy the surface they moved to. That is where it goes wrong, and the evidence for why is better news than most marketers expect.

This is the piece I have been meaning to write all year, and it is the companion to something bigger that I will get to at the end.

What you’ll learn

• The 2026 research on how B2B buyers now research, in one place, with the numbers that matter and the ones that don’t

• What you can actually buy inside ChatGPT, Google’s AI surfaces, Copilot and Perplexity as of November 2026, and the B2B caveat nobody mentions

• The 75,000-brand study that reframes the whole question, and why it points away from the engines and towards the media you already run

• What paid media actually does for AI discovery, surface by surface

• The answer I now give in the room

The buyer moved. Here are the numbers.

Everyone in B2B marketing knows this has happened. Very few have seen the evidence side by side, so here it is.

Start with our own data, because it is what made me take the question seriously. FunnelFuel tracks what identified accounts do when they land on a vendor’s website, across hundreds of millions of page views. A few years ago roughly 30% of account-based first touches landed on educational content: blog posts, guides, comparisons. The buyer was learning on the vendor’s site and the vendor could watch them do it. That share has collapsed. The first thing a target account does now is land on pricing, a demo request, a comparison written for people who already know the category, or the trust centre. The learning did not stop. It moved somewhere nobody can log a page view.

The market research says the same thing from the buyer’s chair.

G2 surveyed 1,076 B2B software buyers in March this year. Fifty-one per cent now start research in an AI chatbot more often than in Google, up from 29% eleven months earlier. AI chatbots have become the number one influence on which vendors make the shortlist, ahead of review sites and ahead of vendor websites. Sixty-nine per cent chose a different vendor than they had planned because of what a chatbot told them, and a third bought from a vendor they had never heard of before the chatbot named it.

6sense asked nearly 4,000 buyers about their last purchase. Ninety-four per cent used a large language model somewhere in the process. First contact with a seller now happens 61% of the way through the journey. And the number that should be on every CMO’s wall: the vendor that won was on the buyer’s shortlist on day one 95% of the time, up from 85% two years earlier.

Forrester’s State of Business Buying 2026 puts 13 internal stakeholders and nine external participants on the average purchase, and finds generative AI the single most cited “meaningful interaction” in the research phase. Its buyers do not trust the AI output on its own; they validate it through colleagues, peers and industry experts before acting. Procurement arrives earlier than it used to. Trials are a precondition.

Gartner’s headline prediction for 2026 is that by 2028, 90% of B2B buying will be intermediated by AI agents. Dreamdata’s 2026 journey benchmarks put 81% of the B2B journey before a lead enters any tracked pipeline, up from 70% a year earlier. And on the Google side, Ahrefs’ February study of 300,000 keywords found the top organic result loses 58% of its clicks when an AI Overview appears, up from 34.5% ten months before.

Put those together and the shape is clear. The consideration set is drafted inside an answer engine, by a committee that is larger and more cautious than it used to be, before anyone visits a website. If you are not in that first draft, the research says you will spend the rest of the cycle trying to displace someone who was. That is the problem the client in the room is feeling when they ask about ChatGPT ads. They are right to feel it.

What you can actually buy inside the answer

Here is the honest state of play. I have dated it, because it has moved every quarter this year and it will move again.

ChatGPT. OpenAI started showing ads in the United States in February, opened a self-serve Ads Manager in May and extended to the UK and other markets over the summer. Ads appear only for users on the Free and Go tiers, which is the first B2B caveat: a meaningful share of senior buyers sit on paid plans and will never see one. Targeting is contextual intent matching against the conversation. No keyword lists, no job titles, no firmographics, no account lists in the ABM sense, though custom audiences from first-party data arrived in May. Bidding is CPC. StackAdapt was reported in May as the first programmatic platform with ChatGPT inventory, which is the first sign of this surface entering the DSP world and the thing most of my clients are really asking about.

Google AI Overviews and AI Mode. You are probably already there. Ads in Google’s AI surfaces are served from campaigns you already run, with Search broad match or AI Max, Performance Max and Shopping all eligible, smart bidding required and no opt-out. The practical move is an audit of what you have, not a new budget line.

Microsoft Copilot. Eligible Microsoft Advertising campaigns are included automatically. No opt-out, no guaranteed display. If your buyers live in Microsoft 365, your existing spend is in there.

Perplexity. Stopped taking new advertisers in late 2025 and, per the Financial Times in February, stepped away from advertising altogether. It is an organic-citation surface only, and for B2B software it is the most review-site-heavy engine of the lot.

Gemini and Claude. No ads.

So the direct answer to the client’s question is: yes, in ChatGPT and in the Google and Microsoft surfaces you already buy, with real limits on who you can target and who will ever see it. A small, ring-fenced test is sensible. I would structure it around the intent clusters your buying committee actually prompts with, land it on proof pages rather than demo forms, and judge it on identified target accounts arriving rather than on clicks.

But that is the answer to the wrong question. The right question is what moves the needle on being named in the answer in the first place. And the data on that points somewhere else entirely.

The study that reframes the question

In 2025 Ahrefs analysed 75,000 brands to find out which measurable factors correlated with a brand being named in Google’s AI Overviews. In December they repeated the exercise across ChatGPT, AI Mode and AI Overviews. The results are the most useful numbers I have seen on this subject, and they are not the numbers the AI-SEO industry leads with.

The factor most strongly correlated with AI visibility was branded web mentions, anywhere on the web, at 0.664. Branded anchor text followed at 0.527 and branded search volume at 0.392. Then the things SEO has spent twenty years optimising: Domain Rating at 0.326 and raw backlinks at 0.218.

And at the bottom, the two factors most marketers reach for first when they hear the word “paid”: branded ad traffic at 0.216 and branded ad cost at 0.215. Ahrefs wrote plainly that throwing money at paid search would not necessarily help.

Twenty-six per cent of brands in the study had zero mentions in AI Overviews. Brands in the top quartile for web mentions earned up to ten times the AI mentions of the quartile below. When the exercise was repeated in December, branded mentions correlated at 0.656 to 0.709 across the three engines, and YouTube mentions came in highest of any factor at around 0.737. A separate analysis by Kevin Indig found brand search volume correlating at 0.334 with chatbot mentions.

Two cautions, both of which Ahrefs made itself. These are correlations, not controlled experiments. And large brands have more of everything, so some of the relationship is size. Nobody has yet published a trial showing that adding mentions moves AI visibility. What we have is a replicated pattern across a very large sample and a mechanism that explains it, which I will come to. I treat that as strong enough to plan on and weak enough to measure.

Here is the reading that matters for the client in the room. The factor with the weakest relationship to being named in AI answers is paid traffic to your own site. The factors with the strongest relationships are people mentioning you, people searching for you by name, and video about you. Every one of those is a downstream effect of people having heard of you, talked about you and gone looking for you. And every one of those is what a brand-to-demand media programme is built to produce.

Why mentions matter and clicks don’t

The mechanism is worth a paragraph, because it tells you where to point the money.

An answer engine has two places to get information from. The first is what the model learned in training: the open web, over years. If your company has been written about, reviewed, discussed and compared across the web, the model has a representation of you before anyone types a prompt. This is why a brand can be named in an answer with no link attached, and why a brand with no footprint can be absent even when its content is perfectly retrievable. The second is what the engine fetches at the moment of the question, from a live index, in a handful of sub-queries. This is where citations come from, and the 2026 citation studies agree on what gets fetched: Reddit is the most cited domain across every engine, with YouTube, LinkedIn, Wikipedia and the editorial press behind it. AirOps’ estimate is that 85% of top-of-funnel brand mentions in AI answers come from domains the brand does not own.

A click from a paid search ad to your pricing page teaches the model nothing about who you are. A thousand people recognising your name in a comparison thread, searching for you after a podcast, or watching a demo on YouTube, teaches it a great deal, and leaves behind the third-party pages that the retrieval step then fetches. Paid media does not buy you a citation. It builds the conditions under which a citation becomes likely.

What paid media actually does for AI discovery

Once you see it that way, the media plan reorganises itself around the buying committee rather than around a channel budget, and each surface has a specific job.

LinkedIn’s most useful format here is not the brand ad. It is amplifying the long-form, dated, specific posts from your executives, your employees and the creators you work with, because LinkedIn content is cited in roughly one in seven ChatGPT search answers and named human statements are what survive retrieval.

YouTube is the single most under-used surface in B2B. It leads citations in Perplexity and AI Overviews, and video mentions carried the highest correlation with AI visibility of anything Ahrefs measured. Paid distribution of chaptered demos and honest comparison walkthroughs builds view velocity on the one video domain every engine trusts.

Programmatic display belongs on the publishers the engines cite. Your citation audit tells you which trade titles and comparison publishers feed the answers in your category; that list is a publisher list, and a sponsored data piece on one of those domains is a mention the engines will retrieve.

CTV, audio and digital out-of-home are the roles LinkedIn under-reaches: the economic buyer, finance, procurement. They build brand memory, and brand memory is branded search, which is the 0.392. In our own client work, accounts exposed to sustained account-based brand campaigns show more pricing-page first touches and more branded search than unexposed accounts on the same list. That is our data, directional, and we are building the holdout tests to make it harder evidence.

Paid search keeps a job, and it is not the one it used to have. It is retrieval protection: making sure your pricing, comparison and alternatives pages are what ranks on Google and Bing for the committee’s evaluation queries, because classic rank still predicts what the engines fetch. Do not expect it to move the brand’s presence in answers. The data says it won’t.

Then close the loop. AI referrals are about 1% of sessions and convert several times better than organic search, because the engine has already done the qualifying. Resolve those visitors to accounts and retarget at account level with the next piece of proof, not a demo ask. That is where an AI discovery programme and an account-based media programme become the same programme, and it is where the interesting work is right now.

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New here? I’m Mike, in adtech since 2011, building programmatic platforms (PowerLinks, now FunnelFuel) and writing about the gap between how B2B marketing actually buys media and how the industry talks about it. If this topic is up your street, you’ll also want The Xerox Strategy: Why B2B’s Best Answer to the Dark Funnel Is Becoming a Verb and The Account Reporting Layer Brand Advertisers Keep Throwing Away.

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I am hearing from partners and my network that the big ask of the moment is ‘how to drive more LLM citations’ and to run AEO/GEO or general LLM SEO.

Understandably, every brand is very keen to drive more citations and to show up more within LLMs. And with good reason, all of the biggest and best research at the moment points to brands beginning a purchase journey with a consideration set and commencing the true research within their LLM/s of choice.

The below graphic that I created from mining hundreds of millions of vendor site web visits tracked by FunnelFuel analytics shows quite clearly the impact that AI is having. The precipitous decline in educational content views at the account level is the most demonstrable visualisation of the dark funnel in action that I’ve seen so far. The fact that increasingly the first account-level touch point is with decision pages (where the LLM journey naturally comes to an end because they don’t have access to pricing information) is a further indication of this direction of travel. The crossover came between 2024 and 2025, when decision pages became the most likely first touch point over educational content - where AI flipped Google Search as the go-to web-based research tool.

Therefore, the desire to show up in LLMs is driven by very real rationale. However, as we’ll explore today, your brand share of model (which equates to the score of how often leading AI tools like ChatGPT mention your brand) has yet to prove it has the ability to truly move a deal. Based on the above graphic and everything we know about AI chatbot-based research, that sounds like an almost preposterous thing to be saying as we approach 2027, but bear with me.

My sense is that we need to keep clear heads and remain rational without overreacting whilst we remember that the ever-darkening B2B buying journey has nothing to do with ChatGPT specifically, and the bigger opportunity today is probably to treat AI mentions as another dark funnel signal, rather than trying to build a new standalone channel wrapped around it.

To keep some context: Demandbase data shows ChatGPT referrals to B2B vendor sites up 300% year on year, yet independent analysis puts them at a meagre 0.33% of B2B vendor visits, with Google still sending roughly 190x more traffic. With this context, let’s dive in.

What you’ll learn in this article:

• Where the term “share of model” actually comes from, and why it’s the fourth name for the same underlying problem

• The real number behind AI referral growth, and the smaller number that keeps it honest

• Why the B2B buyer journey went darker in 2026 for reasons that have nothing to do with ChatGPT

• Why “share of model” tools stop at the edge of the CRM, and why that’s structural, not a maturity gap

• The account-graph fix: treating an AI mention as another dark-funnel signal, not a new standalone channel with its own dashboard

• What changes in an omnichannel programmatic plan once you stop trying to make AI search prove itself alone

Plus, in Field Notes: a brand already fighting for position inside AI answers with no attribution model at all, and the attribution finding hiding behind this year’s dark-funnel headline.

Part of the B2B Stack guide to B2B attribution and measurement.

The wrong belief?

Here’s the belief I keep hearing in briefings, and I think it’s wrong: that “share of model” is a new metric for a new problem. From share of voice to share of search, we have been here before, trying to capture the hard to capture.

So this isn’t as new as it first sounds, and once you trace where the term comes from, you can watch the industry rename the same problem every few years without ever solving it. Tom Roach, VP of Brand Planning at Jellyfish, laid out the lineage plainly in Marketing Week: share of market, then share of voice, then share of search, and now share of model. Four labels, but they all carry only one underlying question really, which is whether anyone actually knows what’s influencing a buyer before they show up somewhere you can track them. AI chat didn’t create that question. It just gave it a new logo.

LLM visits are up 303% YoY to B2B Vendor Sites - BUT They Are Only 0.33% Of B2B Traffic

Let’s start by trying to understand how significant LLM traffic is for B2B vendors today within their marketing mix. Demandbase’s Labs data shows ChatGPT-referred visits to the B2B websites it measures rose 303% year on year, from roughly 645,000 in June 2025 to 2.6 million in June 2026, with a sharp jump in May 2026 that the data doesn’t fully explain. That’s a real behavioural shift, not an SEO scare story. This absolutely validates our idea that LLMs are a consistent and big part of the dark research journey that’s happening behind B2B procurement today. This is also absolutely no surprise at all!

Here’s the number that keeps it honest and grounded, though: independent analysis of that same dataset puts ChatGPT-referred traffic at around 0.33% of total measured B2B traffic, roughly 190 times smaller than what Google still sends.

So both numbers are true at once; the growth rate is real and the absolute share is still tiny.

A metric can be the fastest-growing thing in your dashboard and a rounding error in your traffic at the same time.

And “share of model” reporting tends to show executives the first number without the second - not least because the vendors peddling the ability to measure share of models are naturally keen to make the LLM models sound like the most important thing in the marketing mix, period. They also have a track record of being wildly disparate and diverging in the stats and insights they give back, so the true ability to dive in and be accurate here has a big question mark next to it anyway, as we’ll explore deeper into this piece.

The buyer journey went darker in 2026, and it’s barely about AI

Here’s the part that should worry you more than any AI-visibility score. Dreamdata’s 2026 benchmarking, built from over 66 million B2B sessions, found the average B2B deal now runs 88 touchpoints across 4 channels and 10 stakeholders over a 272-day cycle, up from 76 touchpoints and 6.8 stakeholders the year before. And 81% of that journey now happens entirely outside anything a CRM can see, up from 70% a year earlier.

Read that again: four-fifths of the journey was already invisible before a single buyer typed a question into ChatGPT. The other standout point that hits me as a business owner is just how blind my CRM is to the true state of the pipeline that we are working to generate. We see this with our own business. HubSpot, our CRM of choice, is essentially only valuable for forecasting the next 2 to 4 weeks.

I digress, but the big point is that the dark funnel didn’t appear because of AI search. It was already there, already growing, already the majority of the buying process.

AI chat is a new room in a house that was already mostly dark.

Quick answers

What is “share of model”? A score for how often AI tools like ChatGPT mention your brand, usually against named competitors. It follows share of market, share of voice and share of search as the fourth label for the same question: what influences a buyer before you can track them.

How much B2B traffic comes from ChatGPT? Demandbase’s Labs data shows ChatGPT-referred visits to the B2B sites it measures up 303% year on year, but independent analysis puts them at around 0.33% of measured B2B traffic, roughly 190 times smaller than Google.

Can share of model prove pipeline? Not yet. There’s no session ID, cookie or deterministic handshake back to a CRM record, because the model isn’t serving an ad with a clickthrough. It’s answering a question.

How should B2B teams measure AI visibility instead? Treat an AI mention as another dark-funnel signal. Fold it into the same account engagement score as intent, site visits and HVA progression, and read it against account-level pipeline velocity over a quarter.

The rest of this piece covers why “share of model” tools stop at the edge of the CRM, and the account-level fix. It’s free for subscribers.

Share of model is the dark funnel with a new name: you are trying to find out whether the engines cite you, and what they cite instead. Counting mentions is only the start. You need a repeatable audit, role by role, and a plan for the paid media that feeds those answers.

That method is in The Advanced B2B AI Discovery Playbook. Get the playbook. Subscribers get $30 off: the code is in the Subscribers’ Toolkit.

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Be in the answer when the CFO asks. Your own AI does the legwork.

121-page PDF + the tools to run it with your own AI · $149 · 14-day refund

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From reading to running it

Most playbooks end where the work starts. You finish the book, you agree with it, and then someone has to turn 121 pages into a query map, an audit, a set of pages and a plan. That is where good intentions go to die.

This one ships with the part that does the turning.

The Playbook Operator is one document you load into ChatGPT, Claude or Gemini alongside the PDF. Type “Begin” and it interviews you: your company, category, competitors, buying committee, which pages you already have, your certifications, your pricing model, the media you run. Then it works through the method in eight phases, for your brand:

1. Your Committee Query Map: the questions each buying role asks, in their words, scored and cut to a core set of 50 to 60.

2. Your audit set, with the exact protocol, and the analysis of your results when you paste them back.

3. Your scorecard and committee heat grid.

4. Five proof-page drafts in your brand’s voice.

5. Your source graph and people plan.

6. Your media plan from the Committee Coverage Model.

7. Your measurement set-up.

8. Your 90-day plan, ending with a page called “what to do on Monday”.

It stops after every phase for your go-ahead. It does not make things up: anything it does not know about your company is marked NEEDED for you to fill in. You stay in charge; it does the drafting, the structuring and the heavy lifting.

The agent runbook goes further. If you or someone on your team uses Claude Code or another coding agent, the runbook has the agent do the audit as well. With your OpenAI and Perplexity API keys (Gemini optional), it runs your queries through the engines with the audit runner, fills the workbook, and writes every deliverable to a folder for you to review. It never touches your live site and never posts anywhere on your behalf.

You do not need to read the whole book first. Start with the Operator, and read the chapters it points you to when you want the reasoning behind a step. The book is the method; the Operator is how you put it to work this week.

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The problem, in the buyer’s own numbers

Fifty-one per cent of B2B software buyers now start research in an AI chatbot more often than in Google, and AI chatbots have become the number one influence on which vendors make the shortlist (G2, April 2026, 1,076 buyers). The vendor that wins was on the buyer’s shortlist on day one 95% of the time (6sense, nearly 4,000 buyers). The committee doing the choosing has 13 internal stakeholders and nine external ones (Forrester, 2026). And the deal is increasingly decided late and by the roles marketing never writes for: 49% of buyers say a CFO vetoed an already-approved purchase in the past year, and IT security review is now the single biggest post-selection delay (G2, July 2026).

In the analytics we run for B2B vendors, the effect is visible on the website. Target-account first touches used to land on educational content. Now the first thing an account tends to do is land on pricing, a demo request or the trust centre. The learning still happens. It happens inside the engines, where nobody can log a page view.

So the question is not whether you need to be in the answer. It is whether you can find out, role by role, where you are not, and whether you can do anything about it that a dashboard vendor cannot.

What the method does that nothing else does

It maps the committee, not the keyword. The Committee Query Map is a 7 x 5 grid: seven buying roles (champion, economic buyer, technical, security and legal, finance, procurement, end users) against five decision moments (framing, shortlisting, evaluation, validation, justification). Each cell holds the questions that role types into an engine at that moment, in their vocabulary. Seventy-odd queries per category, prioritised to a core set of 50 to 60. The Operator builds yours in Phase 1.

It audits whether you are in the answer, and who is. A repeatable protocol across three engines with a logging schema, six metrics, variance rules and a gap register. The most valuable output is not your score. It is the list of domains the engines cited instead of you, which becomes your plan. The audit runner does the running; the analysis script does the counting.

It gives you a scorecard a board can read. Seven weighted metrics and a committee heat grid that shows, in one picture, which rows of the buying committee you are invisible to. In most audits that is finance, procurement and security, which is exactly where deals now stall.

It builds the proof the engines cite. Twelve proof pages, a citeable-unit writing standard, three complete sample pages, twelve-term word banks for every role, crawler policy, structured data, and an honest position on llms.txt. Plus the agent-readable proof stack and a chapter on buying agents, for the 61% of buyers who currently use or plan to use AI agents in software buying (G2, June 2026). The Operator drafts five of the pages in your voice.

It connects AI discovery to paid media. This is the part you will not find in an SEO guide, because their toolset cannot see it. Across 75,000 brands, branded web mentions correlate with AI Overview visibility at 0.664 and branded ad traffic at 0.216 (Ahrefs). The things that get you named are mentions, branded search and video presence, and those are what a brand-to-demand media programme manufactures. The book has:

• the evidence that every surface moves branded search (TV, CTV, YouTube, podcasts, out-of-home, display);

• the Committee Coverage Model, which turns your heat grid into a media budget, with a worked allocation;

• the proof-claims register;

• the state of ads inside ChatGPT, AI Mode and Copilot as of late 2026;

• a one-quarter in-engine test plan with success criteria set in advance.

The workbook’s media plan recomputes your budget shares every time the audit updates.

It closes the loop to accounts. The landing page a visitor arrives on from an AI engine tells you which committee role just joined the deal. The book shows how to invert the audit into a page-to-role classifier, score committee emergence, target the roles that have not arrived, and light a heat grid per account.

The two workflows and the flywheel

Everything in the book reduces to two pieces of work. On-site: content the engines go looking for, in the structure they cite, un-gated, dated, attributed. Off-site: tactical paid media that builds brand gravity, aimed at the committee roles the engines are silent about. Content without gravity is retrievable and unnamed. Gravity without content is named and uncited. Run both, measure them on one scorecard, and the arrivals feed the next round of targeting. That is the flywheel. The book is the operating manual, and the Operator is the operator.

Everything that comes with it

One purchase, $149:

• The 121-page PDF, edition 2027.2, with 43 numbered sources and seventeen figures.

• The Playbook Operator for ChatGPT, Claude or Gemini.

• The agent runbook for Claude Code or another coding agent.

• The audit runner, a Python script that runs your query map through ChatGPT, Perplexity and Gemini, captures every cited source and the fan-out queries where the engine exposes them, resumes where it stopped, and computes the six metrics, the heat grids per engine, the source-of-citation table and the gap flags. It needs your own API keys; Google AI Mode is covered by manual capture or a SERP-vendor key.

• The toolkit workbook, fourteen sheets, opens in Google Sheets without conversion: the map, the audit log with live metrics, the heat grid with an engine selector, the scorecard, the gap and source-graph registers, the RACI, the 90-day tracker, the proof-page and creator briefs, and the media plan.

• Forty prompts and ten templates, every one referenced from the chapter that uses it, with the prompts also as a copyable doc.

• Every update to this edition.

Get the playbook →

Who it is for

Content, SEO and growth leads who need a method rather than a mood. Demand and ABM teams who want AI discovery connected to accounts. Paid media teams and agencies who suspect, correctly, that the money they already spend is doing more for AI visibility than anyone gives it credit for. CMOs who need a scorecard that survives a finance review. And anyone who wants the outcomes without spending a month turning a book into a plan by hand.

Who it is not for

Anyone looking for a list of hacks. There is a chapter on why the hacks do not work, and a conduct standard the book, and the Operator, expect you to keep.

How the evidence is handled

Every number carries a label: Measured (a study with a published method), Reported (a vendor or press figure), Our data (FunnelFuel’s analytics), or Working rule (a judgement from doing this). Forty-three numbered sources with primary links where they are stable. The book is my reading of the deepest research available; it is not a statement from the AI companies, and it says so. The Operator follows the same rule: when it estimates, it says so.

A worked example you can check

Every chapter ends with the method applied to The B2B Stack itself: what I would do, and am doing, to make this newsletter the thing an engine names when a marketer asks who to read on account graphs and the dark funnel. The baseline audit is running; the results go to subscribers first and into the next edition.

What is new in the second edition (2027.2)

121 pages. The Playbook Operator, the agent runbook, the audit runner and the workbook. The Committee Coverage Model. The account-graph closed loop. The buying-agents chapter. A media note at the end of every chapter. The channel evidence on branded search. Seventeen figures. The G2 July 2026 Evaluation Maze findings. Three sample proof pages and role word banks. Buyers of the first printing receive it free, tools included.

Price

$149 for the book and every tool. 14-day refund.

Get the playbook →

Want the Signal System too? The bundle adds The B2B Signal Optimisation System: what buyers do once they land, with 100+ high-value actions mapped to stage, scoring templates, a GTM set-up guide and an example dashboard. It is $199.

Get the bundle →

Want only the Signal System? It is $97. See the System →

Subscribers get $30 off

Subscribing to The B2B Stack is free, and subscribers get $30 off via the Subscribers’ Toolkit page, along with the free report and the other subscriber tools.

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FAQ

Do I have to read the whole book? No. Start with the Operator; it works through the method in order and tells you which chapter each step comes from. Read those chapters when you want the reasoning. Page 2 has the whole plan on one page.

How much does the AI actually do? In a chat (ChatGPT, Claude or Gemini), the Operator drafts every deliverable: the map, the audit set, the analysis, the scorecard, the proof pages, the media plan and the 90-day plan. You answer its questions, approve each phase, fill anything marked NEEDED, and run the audit queries yourself or with the runner. A chat window cannot query the engines on your behalf. With Claude Code and API keys, the agent runs the audit too and fills the workbook. In both cases you review before anything goes live, and accuracy is scored by a human.

Do I need to be technical? Not for the Operator, which runs in a chat window. The audit runner and the agent runbook need someone comfortable running a Python script or a coding agent with API keys. If you do not have that person, the Operator gives you the manual audit protocol instead.

Is this just GEO or AEO with a B2B label? No. The unit of analysis is the buying committee role, the method is an audit with a scorecard, and half the book is about paid media and accounts, which GEO guides do not cover.

Which engines does it cover? ChatGPT (search), Google AI Mode and AI Overviews, Gemini, Perplexity, Copilot and Claude. The audit runs on at least three.

Does it tell me how to run ChatGPT ads? It tells you what you can buy inside ChatGPT, AI Mode and Copilot as of November 2026, why it is a test line and not a strategy, and gives you a one-quarter test plan with success criteria set in advance.

Will it go out of date? The platform details will; they are dated in the text. The buyer behaviour and the method will not. Updates to the edition, and to the tools, are free.

I bought the first edition. Do I pay again? No. The second edition and the tools are already in your Gumroad Library, free.

Can I share it with my team? One licence per reader. For a team, email mike@funnelfuel.io.

What if it is not useful? Email within 14 days for a full refund. No form, no questions.

About the author

Mike Harty has been building B2B programmatic platforms since 2011, first at PowerLinks and now as co-founder of FunnelFuel, a B2B advertising specialist built on a living account graph. He publishes The B2B Stack, read by teams at dentsu, WPP, Informa, Salesforce and the large vendors and agencies who run B2B media for a living.

Updates and refunds

Platform details change quarterly. Every revision of this edition is free to buyers. If it is not useful, email within 14 days for a full refund. No form, no questions.

Get the playbook →

Mike

I’m Mike. I’ve worked in adtech since 2011, building programmatic platforms (PowerLinks, now FunnelFuel), and I write here about how B2B marketing actually buys and measures media. What’s below is the practical side of that: the data, the frameworks and the playbook.

What’s here

• The free report: what $39.6m in managed-service media spend taught us. A 15-page PDF built on 1B+ tracked page loads, normally $19.

• A free sample of the signals playbook: the introduction and Section 1, on the seven layers of a signal stack.

• The subscriber price on The Advanced B2B AI Discovery Playbook: $30 off the 121-page second edition, tools included.

• A subscriber price on The B2B Signal Optimisation System: 30% off the 35-page playbook.

• The guides: the four topic hubs and the Start Here page, in one place.

Subscribe free below and the links, and your subscriber code, appear straight away.

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Over the last three years we’ve managed $39.6m in managed-service media spend. Our data covers over 1 billion page loads of two different kinds: 402.6 million across 94 B2B vendor and advertiser sites, and 600M+ across the B2B niche publisher sites our campaigns run on. We keep the two apart, and so does the report. I’ve written up what actually happened once that spend turned into traffic, and checked each finding against Gartner, Demandbase, 6sense, Forrester, G2 and the controlled studies on AI search. It’s free for subscribers.

The findings in one minute

1. Roughly a quarter of raw B2B web traffic is confirmed business engagement once you compound the filters honestly. The rest is noise most reach reporting counts anyway.

2. 37% of accounts come back. Those returners average around 13 sessions each and stay 72% longer per visit. That’s a buying group at work, not a bounce.

3. Engagement is bimodal. Accounts either bounce or go deep. The second visit is the switch.

4. First-touch landings on educational content collapsed by more than four fifths in three years. Decision pages took over. Homepage first touches fell by nearly two thirds in a single year. Buyers arrive late, decided, and deep-linked.

5. B2B trade publishers have been hit harder than vendors. Article first-touches fell from 79% to 17% of entries in two years, and paid campaigns now deliver nearly half of all tracked visits across the network.

The full report goes further: nine sections with seven takeaways you can use the same week, post-click benchmarks for bounce, dwell, depth and return rate to hold your own campaigns against, and a methods section listing the numbers we won’t fake.

Who it’s for

• B2B marketers and demand-gen leads who sign off programmatic or ABM budgets and want to know what they’re really buying

• Anyone who has been shown a reach or “accounts identified” number and wondered how much of it is real

• Media and agency teams planning on B2B trade publishers, who need to know where the publisher click has gone

• Founders and CMOs trying to work out how AI search has changed the way buyers reach their site

It’s not a pitch for any platform or data provider. Third-party intent, first-party signals and the publishers all have a part to play. The report is about measuring all of it at the level where the signal lives: the account.

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The B2B Stack is free. I write one practical piece a week on B2B measurement, advertising and ABM. Subscribe free.

What is B2B attribution?

B2B attribution is the set of rules you use to credit pipeline and revenue to the marketing that helped create it. Most B2B teams inherited their attribution from e-commerce rather than choosing it. Last-touch credits whatever happened just before a form fill. Multi-touch spreads the credit more fairly, but only across the touches it can see, and in B2B most of the journey is invisible. A better approach measures at the level of the account: engagement trends across the buying committee, high value actions, attention, and revenue linked back by cohort.

A precise, wrong number is more comfortable than an honest, probabilistic one. That comfort is the main reason bad attribution survives budget reviews.

I’ve worked in adtech since 2011, and at FunnelFuel we built B2B web analytics because the consumer tools didn’t answer B2B questions. This hub collects everything I’ve written on measurement, in reading order.

Where to start

1. The Last-Touch Attribution Lie: why the model you’re using was never designed for B2B.

2. The ABM Measurement Glossary: sourced vs influenced, MTA models, engagement scores and the rest, with definitions you can lift.

3. Why Optimising B2B Campaigns for Clicks Finds the Wrong Buyers: what click optimisation does to your targeting.

4. What Are High Value Actions (HVAs)?: what to measure instead.

5. Your Media Vendors Are Marking Their Own Homework — a four-question test before you trust vendor-reported performance on ChatGPT Ads, agentic buying and “performance CTV”.

— a four-question test before you trust vendor-reported performance on ChatGPT Ads, agentic buying and “performance CTV”.Why B2B attribution breaks

Three facts do most of the damage.

The buyer is a committee. Gartner’s figure for the core internal buying group is six to ten stakeholders across procurement, finance, technical evaluation and end users, and they rarely convert in the same session (The Last-Touch Attribution Lie). Forrester puts complex purchases at around 13 internal and 9 external stakeholders (There Is No B2B Funnel). Person-level attribution reads a fragmented version of what is really one coordinated decision.

Most of the journey is out of sight. Private Slack and WhatsApp groups, peer conversations, cookie rejections and AI chat windows all leave no trail. GA4 makes it worse. In some markets, up to 90% of sessions vanish unless users accept tracking. GA4’s data retention tops out at 14 months, while a buying cycle can generate 60–100 website sessions from different people over months or years (GA4 for B2B).

Very few accounts are both in market and visible. On the much-cited 95/5 rule, only around 5% of your accounts are in market today, and Gartner’s research puts 70–80% of the B2B buying journey in the dark funnel before any vendor contact. So only a sliver of your target list is both in market and showing observable signal (The Observer’s Paradox). If that sliver is all your attribution can see, your model will keep crediting the channels that catch it at the end.

Mike’s take: I’ve watched the same meeting play out for years. Marketing presents a pipeline number, sales presents a different one, the CFO trusts neither, and the CMO walks out with a smaller budget. It’s almost never a data problem. Usually it’s two teams using different definitions without saying so.

Attribution models compared

The models below are the standard set, as defined in the ABM Measurement Glossary. The last column is the part vendors skip.

• First-touch. How it gives credit: 100% to the first interaction. Good for: Demand creation questions. “Sourced” pipeline runs on this. What it misses in B2B: Everything after the first touch, including mid-funnel and committee breadth.

• Last-touch. How it gives credit: 100% to the final interaction before conversion. Good for: Demand capture questions. What it misses in B2B: Systematically over-credits bottom-funnel and branded search. AI assistants increasingly become the “last touch”.

• Linear. How it gives credit: Equal credit across every touch. Good for: A neutral default when you won’t make weighting calls. What it misses in B2B: Treats a banner view and a pricing-page session as equal.

• Time-decay. How it gives credit: More credit to touches closer to conversion. Good for: Long cycles where recency plausibly matters. What it misses in B2B: Under-credits the early brand work that put you on the shortlist.

• U-shaped. How it gives credit: Heavy credit to first touch and conversion touch. Good for: Balancing creation and capture. What it misses in B2B: The middle of a long committee process.

• W-shaped. How it gives credit: Heavy credit to first touch, lead creation and opportunity creation. Good for: Motions with a clear mid-funnel milestone. What it misses in B2B: Assumes a lead-based process that ABM motions often don’t have.

• Algorithmic. How it gives credit: Model-derived weights from your own data. Good for: Sounds the most sophisticated. What it misses in B2B: Hard to explain in a boardroom, and still limited to visible touches.

Two things to take from this. First, the model you pick materially changes the influenced number, so two teams can both “do multi-touch” and produce different figures that are both technically right. Second, every model only shares out credit across touches it can see. MTA is a precise account of the visible minority.

The B2B Stack is free. Subscribe free for the full glossary series and a new measurement piece most weeks.

Sourced vs influenced pipeline

This is the most important distinction in B2B measurement. It’s covered in full in the ABM Measurement Glossary.

• Definition. Pipeline sourced: Value of opportunities where marketing generated the first touch that led to the opportunity. Pipeline influenced: Value of opportunities where marketing touched any buying group member at any point in the cycle.

• Mechanic. Pipeline sourced: First-touch. Pipeline influenced: Multi-touch, over a lookback window.

• Question it answers. Pipeline sourced: Which activities create net-new pipeline that wouldn’t otherwise exist? Pipeline influenced: Where is marketing speeding up and supporting deals?

• Natural audience. Pipeline sourced: CFO and board (budget allocation). Pipeline influenced: CRO and programme leads (programme effectiveness).

• Failure mode. Pipeline sourced: Under-credits brand and mid-funnel work. Pipeline influenced: “Influenced” can mean one ad seen by one contact. An unlabelled influence claim can sink the whole report.

Report both, always together, always clearly labelled, and never blend them into one “marketing pipeline” figure. Pick one MTA model, write down why, and stop shopping for a model that flatters this quarter.

What to measure instead (or as well)

Account engagement, as a trend

An account engagement score is a composite, usually 0–100, across every buying group member and channel over a rolling window. Different platforms weight things differently, so the same account can score 85 in one and 40 in another without any data quality problem. The trend is the signal. An account moving from 40 to 70 over six weeks tells you something. A 70 on its own doesn’t (ABM Measurement Glossary).

High value actions (HVAs)

HVAs are the specific behaviours that tend to come before pipeline: pricing, integration and security page depth, repeat visits, and several roles from one account engaging in the same window. Most accounts seriously evaluating a vendor look, lurk and share internally long before anyone fills in a form, so form fills only measure the very end of the journey (HVA guide). See also How to Reverse-Map the Behaviour That Predicts Pipeline and B2B Account Scoring.

Attention, not clicks

Clickers are a small, unrepresentative group, and click-optimised campaigns learn to find more of them. A high CTR often comes with visitors who bounce within seconds, and the campaign that “wins” on CTR can be the one that drives no pipeline (HACCTR). The low-CTR audience is often the one that reads high-value product pages all the way to the bottom, which is why attention is the better signal (Attention as a Buying Signal).

Expected pipeline, not vanity stats

In Moneyball for B2B I propose replacing vanity metrics with one north star, Expected Pipeline Added: the sum, across touches, of the probability that a touch leads to an opportunity, multiplied by expected value.

• CTR → Attention, engaged accounts, HVA rate by account

• Form fills / MQLs → Account engagement trend, buying-committee breadth, sales-accepted accounts

• Last-touch revenue → Sourced and influenced pipeline, labelled and reported side by side

• Sessions in GA4 → Account-level sessions resolved to companies, over the full buying cycle

• Cost per lead → Cost per engaged account and pipeline per engaged account

Mike’s take: You will never get a perfect attribution model in B2B, and anyone selling you one is selling. What you can get is a set of honest, labelled measures that finance trusts and that point in the right direction. That’s enough to defend a budget.

Analytics built for accounts, not cookies

Consumer analytics answers “which session converted?”. B2B needs “which companies are engaging, how widely, and is it growing?”. GA4 measures cookies, not accounts, and that gap is the subject of a run of posts: - GA4 for B2B: Why It Breaks Account-Level Analytics - Why Consumer Analytics Fail B2B - B2B Marketing Measurement: Beyond Vanity Metrics - The Dark Social and Offline Channels Your Analytics Can’t See - Why Display Looks Broken in B2B Measurement (and Isn’t): early influence judged on late KPIs. - B2B Attribution in 2026: Why Certainty Is Gone - Why CTR Is the Wrong B2B Metric

For the models and frameworks behind all this, see the B2B Stack Reference Index.

Checklist: a measurement setup finance will trust

1. Write down definitions of sourced and influenced pipeline and agree them with finance before the quarter starts.

2. Choose one MTA model, record why, and don’t change it mid-year.

3. Resolve website traffic to accounts, not just sessions.

4. Define 10–20 HVAs from your own won deals and track them by account.

5. Track engagement trends and committee breadth for every tier-one account.

6. Swap CTR for attention and engaged-account measures in campaign reporting.

7. Put a one-line definitions footer on every board slide that shows a marketing number.

8. Review measures against closed-won and closed-lost deals every quarter.

Related hubs

• B2B intent data and buying signals

• ABM platforms and account-based advertising

• B2B programmatic advertising

FAQ

Why is last-touch attribution wrong for B2B? Last-touch credits only the final interaction before conversion. That over-credits bottom-funnel channels and ignores a buying committee that researched for months, mostly out of sight.

What is the difference between sourced and influenced pipeline? Sourced pipeline credits marketing only where it created the first touch that led to the opportunity. Influenced pipeline credits any marketing touch to any committee member during the deal. Report both, clearly labelled, and never blend them.

Is multi-touch attribution accurate for B2B? It’s fairer than last-touch, but it can only share out credit across touches it can see, and in B2B that’s the visible minority of the journey. The model you pick also changes the result materially.

Which attribution model is best for B2B? None is complete. Many teams report first-touch for sourced pipeline and one consistent multi-touch model for influenced pipeline, alongside account-level engagement measures.

Does GA4 work for B2B? Not on its own. GA4 measures cookies and sessions, not accounts. Its data retention tops out at 14 months, and consent rules can remove most sessions in some markets.

What should replace CTR as a B2B metric? Account-level measures: attention, engaged accounts, buying-committee breadth and high value actions tied to pipeline. High CTR often comes with very high bounce rates.

What are high value actions (HVAs)? HVAs are specific behaviours that have historically come before pipeline, such as deep reads of pricing or integration pages and repeat visits from several people at one account.

What is an account engagement score? A composite score, usually 0–100, summarising how engaged an account is across its buying group and channels. Watch the trend over weeks rather than the absolute number, because platforms weight things differently.

How do you prove marketing’s impact on pipeline in B2B? Agree clear definitions with finance, report sourced and influenced pipeline side by side, resolve engagement to accounts, and show how engagement trends connect to opportunities by cohort over time.

What is the dark funnel in B2B measurement? The research and conversations that happen where tracking can’t reach, such as private communities, peer conversations, cookie rejections and AI assistants. It’s permanent, so measurement has to work with it rather than pretend it isn’t there.

Can you trust media-vendor performance dashboards in B2B? Treat them as one input, not the verdict. Vendors optimise and report inside their own closed loops; without logs, independent attention or account-level outcomes you are letting them mark their own homework. Pair vendor numbers with sourced vs influenced pipeline and HVAs, and run a simple challenge before the money moves. See Your Media Vendors Are Marking Their Own Homework and The Last-Touch Attribution Lie.

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I’m Mike Harty. I’ve worked in adtech since 2011: I built PowerLinks, and I’m now Co-Founder and CPO of FunnelFuel, a B2B programmatic and analytics business. The B2B Stack is where I write up what I see on live campaigns, without the vendor spin.

Changelog

3 Nov 2026: added Your Media Vendors Are Marking Their Own Homework; FAQ on trusting vendor dashboards; Last updated line added.

The B2B Stack is free. I write one practical piece a week on B2B programmatic, ABM and measurement. Subscribe free.

What is B2B programmatic advertising?

B2B programmatic advertising is the automated buying of display, video, CTV, audio, native and digital out-of-home ads, aimed at the companies and buying committees you want to win rather than at consumers. When someone loads a page, opens a CTV app or streams a podcast, an auction runs in about a hundred milliseconds. Your DSP (demand-side platform, the buying software) decides whether to bid based on who the viewer appears to be, what the content is and your campaign rules. The SSP (supply-side platform, the publisher’s selling tool) collects the bids and awards the impression. That happens around a trillion times a day. The full primer is in the Deal IDs guide.

Programmatic was built for B2C, and out of the box it points at the wrong unit, which is the individual device. Rebuilt on an account graph, it becomes B2B’s most underused channel. It’s materially cheaper per impression than LinkedIn, where CPMs for tech and IT audiences commonly run at $30–75. It also reaches the committee members LinkedIn under-indexes on, like technical evaluators, legal, procurement and security (Programmatic Advertising Was Built for B2C).

I’ve worked in adtech since 2011: native advertising at PowerLinks, then B2B programmatic at FunnelFuel. This is my home turf, so treat this hub as the operator’s map.

Where to start

1. B2B Programmatic Advertising: The Complete 2026 Guide: the full stack, where the money flows and where it breaks for B2B.

2. What Is a Deal ID? The B2B Marketer’s Guide: the construct that now carries most B2B targeting.

3. Programmatic Advertising Was Built for B2C. Most of It Still Isn’t Built for B2B: the core argument.

4. B2B Programmatic Advertising Glossary: 115 Terms Defined: bookmark it for the jargon.

The buying routes compared

Every programmatic impression is bought through one of a handful of routes. The names confuse buyers, so here they are side by side. The table draws on the Deal IDs guide and Curation Is the Future of B2B Supply.

• Open auction (open exchange). How it works: Real-time bidding against everyone, across huge pools of supply. Pros for B2B: Scale and the lowest headline CPMs. Cons for B2B: Fraud and quality risk, little transparency, and B2B relevance comes down to the data you bring. “B2B audience” can end up next to a potty-training app.

• Private marketplace (PMP). How it works: An invite-only auction with selected buyers on a publisher’s or SSP’s inventory. Pros for B2B: More control and better quality than open. Cons for B2B: Still runs on much the same supply pipes. Quality varies by deal.

• Preferred deal. How it works: Fixed price with first look at inventory, no volume commitment. Pros for B2B: Predictable pricing and priority access. Cons for B2B: Less flexibility on price. Volume isn’t guaranteed.

• Programmatic guaranteed (PG). How it works: Fixed price and fixed volume, executed through programmatic pipes (automated IOs). Pros for B2B: Guaranteed delivery, first look and stable pricing. Cons for B2B: Rigid, with no auction dynamics, and commitments to manage.

• Curated deal (curated open auction / curated PMP). How it works: A curator packages supply paths, and often data, into a single deal ID you activate in your DSP. Pros for B2B: Account-level B2B data can reach the bid stream. You choose supply paths and manage them centrally. Cons for B2B: Fees can compound, and you need to know whose data and which supply sit under the ID.

Where curation fits. Curation brings together PG’s predictability, the quality potential of PMPs and the flexibility of open exchange, and it adds data-driven selection. In the curation piece I explain why B2B buyers are moving budget from open exchange into curated PMPs. In the Deal IDs guide I cover the Audigent and Dun & Bradstreet partnership announced in 2024, which made over four hundred D&B firmographic segments activatable as curated deal IDs across CTV, display, native, audio and online video.

Mike’s take: If you can’t say what’s under every deal ID in your campaign, whose data it uses, which supply path it takes and what each layer charges, you don’t really know what you’re buying. I ask those questions on day one of every audit, and the answers usually explain the results.

Where the money goes: fees

Programmatic’s dirty secret is fee compounding: the DSP fee, plus the data fee, plus the measurement fee, plus the SSP fee, plus the publisher’s take. In How to Build Your Own AI Bidder for B2B Programmatic I sketch a typical chain as a 17% DSP fee plus extra layers for ad serving, inventory and a $1.50 CPM data fee. In practice you can end up paying $1.50 to deliver $0.60 of media and still not own the data feedback loop.

Then there’s agency-side margin. What Is Principal Media? covers the whistleblower lawsuit against WPP. It alleged close to $1 billion a year in “non-product related income” and that 97.4% of GroupM’s proprietary inventory went unused by its largest clients. Those are allegations, not findings.

• DSP fee. What it pays for: The buying platform. Question to ask: What percentage of media, and is it bundled with data?

• Data fee. What it pays for: Audience or account data on each impression. Question to ask: Which provider, at what CPM, and can I see the delivery by account?

• Curation fee. What it pays for: Packaging supply and data into a deal. Question to ask: Who is the curator, and what is the fee per impression?

• SSP / exchange take. What it pays for: The sell-side platform. Question to ask: Which supply path, and is it the shortest one?

• Agency margin or principal media. What it pays for: Agency buying and inventory. Question to ask: Am I buying principal inventory, and at what mark-up?

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Channels

Programmatic is one set of plumbing serving many screens. The channel rule I keep coming back to is to respect the moment, then use the account graph to aim each channel at the accounts that matter.

CTV (connected TV)

CTV changed the instrument, not the mismatch. Identity matching means a DSP can, in principle, put an ad in front of a named contact’s TV. But hitting an executive on a 65-inch screen while their family is in the room can annoy the very account you’re trying to win. I call that the living room tax. CTV works for B2B through business-news inventory, content next to sponsorships, a tight exception list of hot accounts, and the more forgiving windows outside prime time. - Connected TV for B2B: When It Builds Brand Memory and When It Backfires - CTV for B2B Lead Generation

DOOH (digital out-of-home)

A study from OneScreen.ai and Wynter found 46% of B2B companies already use out-of-home, with adoption strongest among firms of 201–5,000 employees. Almost none buy it well. Programmatic buying now makes up over 30% of DOOH spend. The trick is to treat DOOH as a contributor to other channels, aimed with account data, not as a standalone KPI. - The DOOH Paradox: 46% of B2B Already Buys Billboards

Native

Native never went away. It went quiet, matured alongside display rather than trying to kill it, and is coming back onto B2B plans with a signal layer underneath it. I built PowerLinks around native, so I have history here. - Native Advertising for B2B in 2026

Display

Display has spent years as the unloved middle child of the media mix, and AI assistants moving into advertising may be its biggest moment in a decade. Display also tends to look broken in B2B measurement because it influences early and gets judged on late KPIs. - ChatGPT Ads and the Return of Display - Why Display Looks Broken in B2B Measurement (and Isn’t)

Audio and podcasts

• Podcast Advertising for B2B

Planning the mix

• CTV vs Audio vs Display vs Native: What Drives B2B Pipeline

• How to Combine LinkedIn Ads With Omnichannel Programmatic

• The Open Web Is B2B's Legacy Format — why the open web still works but stopped being where the media plan starts.

• Display. Best B2B job: Always-on reach and frequency across the committee, plus account-level reporting. Watch out for: Optimising for clicks. Measure attention and account engagement instead.

• Native. Best B2B job: Content-led engagement inside research environments. Watch out for: Low-quality placements that turn native back into display.

• CTV. Best B2B job: Brand memory with senior buyers in the right context. Watch out for: The living room tax: wrong moment, wrong screen.

• DOOH. Best B2B job: Local and event presence near target offices and conferences. Watch out for: Treating it as a standalone KPI.

• Audio / podcasts. Best B2B job: Reaching niche professional audiences. Watch out for: Thin measurement. Plan it as brand.

Mike’s take: Every new channel, whether that’s CTV, audio, retail media or ChatGPT’s ad surface, runs through the same plumbing. If your account data and measurement are wrong on display, they’ll be wrong on CTV too, only more expensive.

Data, attention and the account graph

• Context is the cookieless lever. Context was cookieless before cookieless was a category. Blend it with identity rather than choosing one (Your Firmographic Targeting Is Running On A 1937 Filing System).

• Attention, not impressions. Lumen’s research has found that only a small fraction of standard banners are ever looked at. A $30 CPM in the right environment can produce a far lower cost per attentive impression than a $3 CPM in the wrong one (Segment vs Signal).

• Match rates. These are covered in Programmatic Match Rates Explained.

• The account graph decides whether any of this reaches the right companies. See the ABM hub.

Privacy and consent

Cookie banners have become a consent tax that guts the publishers who fund the open web (The Consent Tax). In the UK, the ICO’s PECR changes may reopen some targeting of cookie rejectors (The ICO’s PECR Changes).

Checklist: is your B2B programmatic set up for B2B?

1. Targeting unit: are you targeting accounts and committees, or devices and consumer segments?

2. Deal transparency: can you name the data, supply path and fee behind every deal ID?

3. Supply quality: what share of spend goes to open exchange, and do you have an exclusion list?

4. Optimisation goal: are you optimising to attention, account engagement and HVAs rather than clicks?

5. Account reporting: can you see which accounts and roles engaged, by campaign?

6. Fees: do you know your working media percentage?

7. Channel fit: does each channel have a B2B job, in the right context?

8. Feedback loop: does engagement data flow back into targeting and scoring?

If you’re running consumer defaults, meaning device-level targeting, click optimisation and black-box segments, fix those first. Precision downstream of a blunt filter is still blunt.

Related hubs

• ABM platforms and account-based advertising

• B2B intent data and buying signals

• B2B attribution and measurement

More reading: Why B2B Programmatic Looks Broken and How to Fix It.

FAQ

What is B2B programmatic advertising? B2B programmatic advertising is automated, auction-based buying of digital ads aimed at target accounts and buying committees, using account-level data rather than consumer audiences.

Is programmatic cheaper than LinkedIn for B2B? Per impression, yes, materially, when it’s done properly. LinkedIn CPMs for tech and IT audiences commonly run at $30–75, and programmatic also reaches committee roles LinkedIn under-indexes on.

What is a deal ID? A deal ID is a code passed in the bid request that ties an impression to pre-agreed terms such as price, inventory, audience or data. In 2026 it’s also the activation token for most curated B2B data.

What is the difference between a PMP and programmatic guaranteed? A PMP is an invite-only auction, so price and volume aren’t fixed. Programmatic guaranteed fixes both price and volume, like an automated insertion order.

What is programmatic curation? Curation means packaging supply paths, and often data, into a single deal before the buyer sees it. For B2B it’s how account-level data reaches the auction without each advertiser rebuilding it.

Does CTV work for B2B? Yes, in the right context: business-news inventory, content next to sponsorships and tight lists of hot accounts. It backfires when it interrupts people’s downtime in the living room.

Should B2B brands use DOOH? Often, yes. 46% of B2B companies already use out-of-home, according to OneScreen.ai and Wynter. Aim it with account data and treat it as a contributor to other channels, not a standalone KPI.

What fees should I expect in programmatic? Expect a DSP fee, data fees, possibly curation fees, SSP or exchange takes, and any agency margin. Ask for your working media percentage and a breakdown by layer.

What is the biggest mistake in B2B programmatic? Running it on consumer defaults: device-level targeting, click optimisation and black-box segments. B2B buying is a committee decision made over months, so measure accounts and attention instead.

What is a DSP and an SSP? A DSP (demand-side platform) is the software buyers use to bid on ad impressions. An SSP (supply-side platform) is the software publishers use to sell them.

Does CTV or DOOH work for B2B advertising? Both can, with different jobs. CTV builds brand memory when you buy business-news and sponsorship-adjacent inventory against tight account lists; it backfires as living-room interruption. DOOH is already used by 46% of B2B companies (OneScreen.ai / Wynter): aim it with account data and treat it as a contributor to other channels, not a standalone KPI. Guides: Connected TV (CTV) for B2B and The DOOH Paradox.

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The B2B Stack is free. You get one practical piece a week on B2B programmatic, ABM and measurement, written from inside live campaigns. Subscribe free.

I’m Mike Harty. I’ve worked in adtech since 2011: I built PowerLinks, and I’m now Co-Founder and CPO of FunnelFuel, a B2B programmatic and analytics business. The B2B Stack is where I write up what I see on live campaigns, without the vendor spin.

Changelog

3 Nov 2026: added The Open Web Is B2B’s Legacy Format; combined CTV and DOOH FAQ; Last updated line added.

The standing complaint about intent data is that it’s noisy. It is, and it always will be. Intent data is a balancing act between scale and accuracy, and topic-level interest is a broad thing to measure, so the signal arrives with noise built in. That isn’t a defect in the product. It’s the nature of the product, and it’s on the operator to take that noise and turn it into a signal that means something for their business.

Which is why I think “noisy” is the wrong diagnosis, and the wrong diagnosis is why the complaint never goes away.

Teams treat noise as a sourcing problem: buy from a better provider, swap partners, raise the threshold, narrow the topics. Start the year with Bombora, finish the year with Dun & Bradstreet, begin next year with the next shiny new toy. All three are part of the operator’s job, and plenty of teams have done all three and still end up with a rep ringing an account that bought from a competitor in August, and a display campaign chasing the same account into November. The noise was handled. Something else wasn’t.

What I see when I look inside a B2B stack isn’t too many signals. It’s signals that never die. A topic surge from last quarter is still in the score, and it’s still being read as “this company is in market for our offering today”, which it wasn’t even on the day it fired. A job post that was filled in June still counts as a hiring signal. A closed-lost reason that was the easiest button for a rep to hit sits in the CRM as the account’s permanent verdict. The target account list is a photograph of a meeting from January, if you’re lucky, as it’s quite conceivable that, from some of the lists I’ve seen, it could have been a photo taken in January 2008. We’ve seen one that still carried Lehman Brothers, which dates it to 2008 at the latest hence that example. None of it carries a stamp that says when it stops being true, so all of it gets treated as true forever.

The programmatic stack assumed the opposite from day one. Every layer of it was built on the idea that data rots. B2B copied the signals and left the expiry dates behind. Today we will explore how to stop doing that.

What you’ll learn in this article:

• Why the ad stack treats every signal as perishable, and the exact point where that assumption gets stripped out on the way into a B2B CRM

• The three ways a buying signal dies: it ages out, it gets spent, or it gets overruled

• The expiry table: a starting TTL for every signal B2B teams buy and build, from intent surges to the target account list, as a graphic you can share

• Why combining signals for validation in numbers only works when every input is live

• What to change in the stack on Monday without buying anything new

• Why programmatic spend is where an expired signal costs the most

Plus Field Notes: three live examples this week of a signal’s expiry date being set by someone other than you, including a ChatGPT Ads change that switches on tomorrow.

The ad stack assumes everything rots

If you’ve ever built or run a bidder, which for my sins I have done 4 times, the thing you internalise early is that nothing in the system is allowed to stay true for long. A cookie has an expiry. A segment in a DMP has a membership window: a user who qualified on a site visit falls out after the lookback closes unless they do something to qualify again. A retargeting pool is weighted by recency, so a visit from this morning outbids a visit from three weeks ago. A frequency cap resets. A suppression list removes anyone who has already converted, so you stop paying to persuade someone who has already been persuaded.

None of that was done because of good manners, the programmatic auction mechanics forced it. A bid request arrives carrying the state of the world as of right now - one of the enormous value points of programmatic - and you have ~120 milliseconds to price it, and pricing it on what was true last month loses money every time. So the whole architecture evolved to make a signal re-earn its place every window or drop out. In engineering terms every signal carries a TTL, a time to live. When the TTL runs out the signal is gone. Not “still there but a bit old”. Gone.

The intent vendors mostly get this right inside their own platforms. A surge is computed over a research window, typically a week, and an account that stops researching drops off the surge list. The decay exists. In the stacks I’ve seen, it dies at the export. The surge gets pushed into a CRM field, a marketing automation score or a static audience list, and at that moment it stops being a measurement of a window and becomes a flag. Flags don’t decay. A field written in March is still true in October unless a human edits it, and editing it is nobody’s job. I guess all of this is to say that sometimes what we blame as bad signal is actually poor implementation of the signal.

The worst version of the export is the whole CRM. A team needs an audience for an account-based campaign, so the entire account table goes across to the ad platform or the ABM tool: defunct companies, closed-lost from three cycles ago, every record anyone ever imported. After all, more is always better, right. That’s a problem at the input layer, and a problem at the input layer becomes a problem with the relevancy and freshness of every signal attached downstream. No amount of scoring recovers a list that was stale before it arrived. I will argue this point when the cows come home. The missing piece for most account-based programmes is the quality of the list on the way in - lists which often get millions of dollars spent against them, lacking the most basic of hygiene, segmentation, and ICP fit analysis.

First-party signal goes the same way. A site visit from a known account becomes points in a lead score, and the lead score only goes up. A score that only goes up isn’t a score. It’s a counter.

A signal dies in a minimum of three ways

Two threads this week, in r/SalesOperations and r/revops, put it more plainly than most vendor decks: a buying signal should be cancelled the moment it’s acted on, because at that point “the signal is spent”. That’s one of three ways a signal stops being true. Naming all three matters, because each one needs a different mechanism, and most stacks have none of them.

It ages out. The signal measured attention over a window and the window closed. An intent surge, a page view, a webinar seat, a job post: each is evidence of something happening at a moment, and the moment passes. The job post is the one people get most wrong. The same threads call open roles a weak signal on their own, and I’d go further. A job post tells you a budget line was opened, not that anyone is buying tooling. It also has a visible end date, the day it’s filled or withdrawn, and most stacks ignore it. In general, this class of signal needs rapid pipelining to take advantage of, and that pipelining includes a minimum of daily refreshing: taking out stale signal and adding in fresh new signal. Anything less than that is leading you down the wrong path - the path that leads to wasted budget.

It gets spent. Sales acted on it. The rep called, the meeting happened or didn’t, the account said yes, no or not now. Whatever the answer, the signal has done its job and should stop scoring the account, and it should certainly stop feeding a media campaign that chases the account the week after the meeting. Programmatic has had the fix for years in the conversion suppression list. The B2B version, suppressing an account from the display campaign because sales touched it on Tuesday, is almost nobody’s default. I’m not sure, in all of our conversations, we’ve ever spoken to a client that’s wanted to execute this.

It gets overruled. A newer signal contradicts the older one. The champion left. The tech stack changed. The account recorded as “price sensitive” just hired a VP whose first move is replacing the incumbent. The closed-lost field is the clearest case. In a separate thread this week, sales ops people admitted that lost-deal reasons fail the same way “how did you hear about us?” does: price is the easiest button to hit when the form pops up. So the field holds a rep’s guess under time pressure, with no expiry, and eighteen months later it decides whether the account is worth a campaign.

A stack with no TTL can’t tell these three apart. It sees a signal, and a signal is a signal.

The expiry table

This is the part to save and argue with. The windows in the graphic are where I’d start, drawn from running account-based campaigns rather than from a benchmark study. Treat them as an opinion to be corrected by your own data, not a number to copy into a deck.

In words, for anyone reading this somewhere the image doesn’t load: a third-party intent surge gets one surge window, typically a week, and leaves the score after two misses in a row. A job post lives as long as the post does and works as a modifier, never a trigger. A technographic install holds until the next crawl, stamped with the crawl date rather than the import date. A first-party site visit is worth days to a few weeks, weighted by recency and never summed. A download or webinar seat lasts a few weeks, after which it’s a contact record. A closed-lost reason survives one buying cycle, then gets re-asked or discarded. A place on the target account list lasts a quarter, after which every account re-qualifies against live signal. And an engaged-account status resets the moment sales acts, and inherits the shortest TTL of whatever fed it.

Nothing in that table has a TTL of forever, including the list, which is the row that most often does.²

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What is an ABM platform?

An ABM (account-based marketing) platform is software that helps you choose the companies you want to win, spot which of them are showing buying behaviour, and put ads and outreach in front of the people inside those companies. 6sense, Demandbase, RollWorks (now unified with AdRoll) and StackAdapt are the names most B2B teams shortlist. They overlap more than their sales decks admit. In practice, what decides whether ABM works is rarely the logo you choose. It’s the target account list, the data underneath, how you define the buying committee, and whether anyone owns turning signals into pipeline.

This page brings together everything I’ve written on ABM tools and account-based advertising, in a sensible reading order. I’ve worked in adtech since 2011, and I run a B2B programmatic business that competes with some of these tools for budget, so I’ll flag that where it matters.

Where to start

If you only read three or four things, read them in this order:

1. 6sense vs Demandbase vs StackAdapt: Do ABM Tools Suck, or Do You Need Managed Service?. It covers what each tool actually is and why most ABM failures aren’t vendor failures.

2. The CIO You’re Targeting Might Not Exist. This is why job-title targeting quietly breaks account-based programmes.

3. The Account Graph Is Not an Identity Graph. It covers the data question you should ask every vendor before you sign.

4. The ABM Measurement Glossary: the terms you need when the CFO asks what ABM delivered.

The platforms at a glance

This summary is drawn from my ABM tools comparison and the mid-market vs enterprise stack piece. It’s a starting point for a shortlist, not a substitute for a trial on your own data.

• 6sense: A predictive GTM brain: intent, account scoring and AI-driven workflows. Usually strongest for: Teams with clean CRM data and RevOps who will act on predictions. Where it tends to break: Brilliant with the right data, poor when misconfigured or fed a messy CRM.

• Demandbase: A unified identity and account data spine. Usually strongest for: Enterprise teams that need account data joined up across systems. Where it tends to break: Powerful but heavy for mid-market. Cost and complexity can outrun the team.

• RollWorks / AdRoll: ABM as a product line inside a broader ad platform. Usually strongest for: Mid-market teams that want simple, fast activation. Where it tends to break: Lighter on enterprise orchestration and governance.

• StackAdapt: A mid-market DSP with ABM-style features. Usually strongest for: Programmatic activation at mid-market scale. Where it tends to break: Great for activation, but it isn’t a GTM operating system. Readers flag the CPMs.

• Expert-managed B2B programmatic (what FunnelFuel does): A service plus an account graph and curated supply. Usually strongest for: Enterprise teams without the in-house time or skills to run ABM media weekly. Where it tends to break: You give up some hands-on control, and it’s a service cost, not a licence.

In November 2025, Demandbase was named a Leader in Gartner’s Magic Quadrant for ABM platforms, and 6sense launched an agentic GTM product. I covered both in the RollWorks vs Demandbase vs 6sense news round-up. Treat analyst badges as one input. The Reddit threads I quote in the comparison post are often more honest about the day-to-day.

Mike’s take: I’ve sat in a lot of “should we switch ABM platforms?” meetings. Almost every time, the platform wasn’t the thing that was broken. The target account list hadn’t been touched in a year, Salesforce was a mess, and nobody owned what happened after an account “surged”. One reader comment I quoted sums it up: “We bought an ABM platform, and then realised we didn’t even have a TAL.” Fix that first. Then judge the tool.

Mid-market and enterprise need different stacks

The same tool can be right for one company and wrong for its neighbour, because mid-market and enterprise buying behave differently. In The Hidden Faultline in B2B I set out the split:

• Typical customer size: Mid-market: About 100–1,000 employees. Enterprise: About 1,000–100,000+ employees, global buying groups.

• Sales cycle: Mid-market: Roughly 30–120 days. Enterprise: Roughly 6–18 months, often non-linear.

• Stakeholders: Mid-market: Often 1–3 drive the deal. Enterprise: 10–20+ across roles, regions, procurement and InfoSec.

• Core systems: Mid-market: 5–10 tools, easy to buy and replace. Enterprise: 20–40 interconnected systems.

• Programmatic: Mid-market: Minimal (StackAdapt, RollWorks or built-in ABM bidders). Enterprise: Specialised, curated supply partners.

• What buyers want: Mid-market: “Show me it works fast”. Enterprise: “Integrate with our world, don’t break anything”.

If your segment is shifting upmarket, your stack needs to shift with it. Bolting enterprise architecture onto mid-market maturity is one of the most common reasons teams decide “ABM tools suck”.

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Target the buying committee, not a job title

Every account has a buying committee. Not every account has a CIO. Most ABM programmes still do a two-step lookup: build the account list, then find the CIO or CTO inside each account and point the media at them. It feels precise. But org charts differ from company to company and decay fast. In The CIO You’re Targeting Might Not Exist I note that average tenure in a US role is now around 18 months, and B2B email addresses decay at roughly 25–30% a year.

The committee is also far bigger than most media plans assume:

• Forrester’s State of Business Buying research, which I cover in There Is No B2B Funnel, puts the average complex purchase at 13 internal stakeholders plus 9 external participants: analysts, consultants, peers, references.

• Gartner puts the typical committee at 6–10 stakeholders for deals above $100,000, up from 4–6 as recently as 2017. Forrester’s research on deals above $1 million puts it at 14–23. I set out both in The Account Graph Is Not an Identity Graph.

• In our own enterprise tech work we often see committees of 20+, 60+ touchpoints and buying cycles of 11+ months (B2B Is Not Just ABM).

The better approach is to infer the buying group from signal: who is actually researching, from which offices, across which functions. A pre-canned title list misses the people who matter. That’s especially true of the “shadow” members, such as legal, security, procurement and outside advisers, who never appear on a TAL export. LinkedIn has the same blind spot, which I go into in Why LinkedIn Ads Miss Most of the Buying Committee.

Mike’s take: A job title tells you what someone’s badge says. It doesn’t tell you who’s in the room. In B2B the maths favours casting the net a bit wider across the committee: one missed influencer on a six-figure deal costs far more than a few wasted impressions.

The data underneath: account graphs, firmographics and TALs

An account graph is not an identity graph

An identity graph stitches identifiers together (IP addresses, cookies, device IDs, hashed emails) so a fact about one person can be reached across their devices. An account graph maps how real companies are structured (parents, subsidiaries, offices, roles) and then resolves identity onto that structure. Most of the market has the first. Very few have properly built the second, and a lot of what gets pitched as an “account graph” is an identity graph with a company name bolted on. When you talk to a vendor, ask whether it’s a reach graph (how many identifiers it can address) or a proof graph (whether it can show which accounts, and which people inside them, actually engaged). Read The Account Graph Is Not an Identity Graph and the earlier product perspective on the account graph.

Firmographic codes are older than you think

A lot of TAL building still leans on SIC and NAICS codes. SIC got its first real list of industries in 1938 and was last substantially revised in 1987. Microsoft’s SEC filings still carry SIC 7372, “Services, Prepackaged Software”, and Dell’s classification is 3571, “Electronic Computers”. If that’s the filter deciding who gets onto your target account list, everything downstream inherits its blind spots. The fix is to index what companies actually publish about themselves, and keep re-indexing. That’s in Your Firmographic Targeting Is Running On A 1937 Filing System.

TALs aren’t dead, but static ones are

Static target account lists can’t keep up with how quickly buying behaviour moves now. List hygiene has become an ongoing signal-governance job rather than an annual spreadsheet refresh. See TALs Are Dead. Long Live TALs.

Running ABM so it shows up in pipeline

• Score engagement, not activity. In the ABM Measurement Glossary I explain why the same account can legitimately score 85 in one platform and 40 in another. The trend matters more than the number. An account moving from 40 to 70 over six weeks is a signal. A 70 on its own is decoration.

• Separate sourced from influenced pipeline, and report both with clear labels. This is the source of most marketing-vs-finance arguments. There’s more on this in the measurement hub.

• Don’t confuse ABM with B2B. ABM still wins for named, high-value accounts. A lot of B2B growth also comes from signal-based marketing to a wider market that isn’t on any list yet. See B2B Is Not Just ABM, The Signals Era Needs to Deliver Pipeline, Not Just ABM and the ABM 2.0 playbook.

• Watch the match-rate claims. Vendors tout “95% match to your TAL” as a badge of honour. Ask what was matched, how, and at what confidence (B2B Is Not Just ABM).

• Plan by region. Account-based advertising doesn’t behave the same in every market. See Running Account-Based Advertising Globally.

Mike’s take: The ABM programmes I’ve seen work all had someone whose job was the bit after the dashboard: deciding which accounts got sales attention this week, and why. The ones that stalled had a great platform and nobody holding the pen.

Checklist: before you buy (or blame) an ABM platform

1. Is your TAL current? When was it last rebuilt from real signal rather than firmographic codes alone?

2. Is your CRM clean enough for predictive scoring to mean anything?

3. Mid-market or enterprise? Match the tool to the segment you actually sell into (see the table above).

4. Who owns the next step when an account surges? Name the person and the response time.

5. What sits under the “account graph”? Ask the vendor: identity resolution only, or real company structure?

6. How will you measure it? Agree sourced vs influenced definitions with finance before launch.

7. Who will run the media every week? If nobody in-house has the time and programmatic skills, factor in managed service or agency support. Otherwise you’ll end up with an expensive dashboard.

8. What does “match rate” mean in the vendor’s own words, and at what confidence?

If you can’t answer 1, 4 and 7, changing platform probably won’t fix the problem.

Related hubs

• B2B intent data and buying signals

• B2B programmatic advertising

• B2B attribution and measurement

FAQ

What is the difference between 6sense and Demandbase? 6sense is best understood as a predictive GTM engine, built around intent, scoring and AI workflows. Demandbase is a unified identity and account data spine. 6sense rewards teams with clean data that act on its predictions. Demandbase rewards enterprise teams that need joined-up account data, but it can be heavy for mid-market.

Is RollWorks good for mid-market ABM? Usually, yes. RollWorks, now unified with AdRoll, is built for simpler, faster activation. It’s lighter on the orchestration and governance that large enterprise programmes need.

Is StackAdapt an ABM platform? Not really. StackAdapt is a mid-market DSP with ABM-style features. It’s good for programmatic activation, but it isn’t a full GTM operating system.

Do ABM tools actually work? They work when the foundations are in place: a current target account list, clean CRM data, and someone accountable for acting on the signals. Most “ABM tools suck” complaints are really about running enterprise tools without those foundations.

Should we run ABM ourselves or use a managed service? Run it yourself if you have in-house programmatic skills and the time to manage it every week. Enterprise teams without that often get better results from expert-managed ABM. I run a managed service, so weigh that view accordingly.

How big is a B2B buying committee? It depends on deal size. Forrester puts complex purchases at about 13 internal stakeholders plus 9 external participants. Gartner cites 6–10 for deals above $100,000, and Forrester cites 14–23 for deals above $1 million.

What is an account graph? An account graph maps how real companies are structured, including parents, subsidiaries, offices and the roles inside them, and then resolves identity onto that structure. An identity graph only stitches devices, cookies and emails together.

Why is job-title targeting unreliable in ABM? Org charts differ between companies and decay quickly. US role tenure averages around 18 months, and B2B emails decay at roughly 25–30% a year. Many real decision-makers don’t hold the title you’re targeting.

What is a target account list (TAL)? A TAL is the set of companies your sales and marketing teams have agreed to prioritise. It only works if it’s rebuilt regularly from current signal, not left as a static spreadsheet.

How do you measure ABM success? Track account-level engagement trends, buying-committee breadth and pipeline. Report sourced and influenced pipeline separately, and treat the trend in an engagement score as the signal rather than the score itself.

6sense vs RollWorks: which suits mid-market? 6sense is a predictive GTM engine: intent, scoring and AI workflows. It rewards clean data and teams that act on its predictions. RollWorks (now unified with AdRoll) is built for faster, simpler activation and usually fits mid-market better; it is lighter on the orchestration large enterprise programmes need. Shortlist on operating model, not feature checklists. Detail: RollWorks vs Demandbase vs 6sense and Do ABM Tools Suck, or Do You Need Managed Service?.

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I’m Mike Harty. I’ve worked in adtech since 2011: I built PowerLinks, and I’m now Co-Founder and CPO of FunnelFuel, a B2B programmatic and analytics business. The B2B Stack is where I write up what I see on live campaigns, without the vendor spin.

Changelog

3 Nov 2026: added FAQ on 6sense vs RollWorks for mid-market; Last updated line added.

The crux of this piece is making sure that marketers are able to form a clear, fair, and truly attributable view of what’s going on behind their advertising in order to measure and ensure that you’re scaling the right activity and deprioritising the stuff that might be giving fluffy numbers on a dashboard that can’t be traced back to true source data and real biz value.

The key argument comes down to using raw data in order to form your own opinions of performance, and I’ll show you how to do that deeper down the piece. The example I gave is actually quite an interesting one of a TV platform materially underreporting its performance.

So, this goes both ways: to ensure that you’re not dealing with overinflated platform-marked homework (the easy assumption to make around what we’re talking about here!), through to making sure that a platform pixel’s inability to capture data in 2026 is not costing you the opportunity to scale a channel which is actually working well for you but which is doing a poor job of demonstrating that value.

So, diving in - measurement is the topic du jour. Every new ad channel that launched this year arrived with a measurement story.

ChatGPT Ads named 20 measurement partners on 5 October, which does a really good job of sounding like an open, measurable platform, but we’ll dive into the limitations of these partners shortly more specifically, where they sit in the measurement chain and what they can and cannot see. IAB Tech Lab’s agent-to-agent buying standard has a whole section headed Reconciliation. Performance CTV comes with a CPA column because it fancies a chunk of Meta’s over-inflated budgets. The belief underneath all three is that more reporting is the same thing as more accountability. But in the world of enterprise B2B, where contracts are usually signed over DocuSign rather than an e-commerce checkout: how do we work out which platform is really delivering to our marketing KPIs?

Having built an ad exchange, I’d put the question differently. When you spent $50,000 on that channel last month, who counted what you got? If the only answer is the company that sent the invoice, then your vendor has marked its own homework, and the one decision left to you is whether to believe the grade. As a side note, if the answer is GA4, you also need to be aware that that has a Google bias lens to it, potentially (I’m making no claims because I don’t want to get sued ;) )

What you’ll learn in this article:

• Why programmatic always kept two counts of every impression, and why that was the whole point

• How ChatGPT Ads can have 20 measurement partners - but do they truly see enough upstream data in order to form a proper measurement opinion?

• Where IAB Tech Lab’s AAMP 3.0 draft loses the receipt between the proposal and the auction and why the agentic tomorrow is not yet the agentic today

• What a CRM match found that a DSP pixel missed, by a factor of eleven! This is why we shouldn’t assume that platforms always over-report, and we should form our own unbiased opinions by looking across all the media activations that we run collectively.

• Why the join key is the other half of the problem, and what Apple said about it on 19 October

• The log test: four questions to ask any channel or vendor before the money moves

Plus Field Notes: three more vendors marking their own homework this week, including Google’s “optional” cap and a GEO dashboard with a 9% match rate.

Programmatic Uniquely Has Two/three impression counts

when you think about digital advertising, the vast majority of money flows through Walled Gardens - the biggest of whom include Google, Meta, and LinkedIn. These are singular platforms which run an end-to-end ecosystem.

Programmatic advertising is different because it’s addressing the open internet - it’s creating a join between publishers and advertisers using advertising technology as middleware. This middleware spans technology focused on the supply side of the ecosystem (SSP’s), representing publishers, and the demand side of the ecosystem (DSPs), representing advertisers.

The thing people forget about real-time bidding (AKA programmatic advertising) is that it was built by parties who didn’t trust each other. Publishers and their SSPs didn’t trust ad networks. Networks didn’t trust DSPs. Nobody trusted the ad server. So the protocol was designed so that every impression produced more than one record: the SSP logged what it offered, the exchange logged the auction, and the DSP logged what it bid, what it won and whether the creative rendered.

This creates a unique ecosystem which has multiple streams of log-level data, giving advertisers a deeper opportunity to really understand what’s happening behind their media buy, in a way that you can frankly never achieve if you’re utilising Ward Garden inventory.

When I was building an exchange, discrepancy was a daily conversation - frankly, there never is one perfectly right answer. The three counts never agreed to the exact unit for numerous genuine reasons. Timeouts, dropped win notices, creatives that won the auction and never rendered. We set thresholds, usually a few percent, and billed to a reconciled number.¹ Nobody thought this was elegant but it was the reason the market worked. Each side kept its own record, and the invoice was settled against both.

Log-level data is the buyer’s half of that arrangement, and now, more commonly because of curation, supply-side log-level data is also typically available. A proper DSP seat gives you a row per impression: timestamp, domain or app, the bid, the clearing price, which identifiers were present. It is the file you take to the seller when the numbers don’t match. It is also the file your analyst joins to the CRM to find the deals the pixel never saw. The only excuse for not getting towards full data, or at least very detailed reporting, is if you’ve booked on an outcomes-based business model. In that case, the transaction and the billable event are the outcome that was pre-agreed, as opposed to impressions batched into advertising units - so the right for this type of data is limited by the fact you didn’t pay for it, you paid instead for an outcome.

I’ve started calling this the log test. Before the attribution model, before the dashboard, before anyone argues about incrementality, there is one question: does anybody other than the seller hold a record of what was served and what it cost? Fail that, and every number downstream is a claim made with a vested interest in getting you to spend more money

Three exhibits from the last month fail it, each in a different place.

ChatGPT Ads: one log, twenty partners

On 5 October OpenAI announced a visual ad format inside image generation, US testing from later this month, and what it called its first set of third-party measurement partners. The list is long and familiar to those of us living in the weeds: ten attribution vendors from AppsFlyer and Adjust through to Triple Whale and DV Rockerbox; Hightouch, Tealium and LiveRamp to pipe conversion data in; Fospha, Measured and INCRMNTAL for full-funnel work; Haus, Measured and WorkMagic for geo experiments. DoubleVerify and IAS are building brand-suitability pilots. OpenAI also published early results: DV Rockerbox put WeightWatchers’ attributed CPA 15.3% below its blended paid-search benchmark, and Triple Whale found 93% of Portland Leather’s ChatGPT visitors were new to the brand.

On the practitioner side the week looked different, when you take a wander through the murky world of Reddit, X, and LinkedIn threads, which are brilliant at showing us what the people who actually execute all of this are seeing and thinking.

Threads on r/adops and r/PPC on Reddit around 13 and 14 October carried a set of figures from a vendor that sells click-fraud refunds: 923 billed clicks, 58% flagged as non-human, 665 from Azure IP ranges. The inference is that the plague of automated traffic and bot traffic has made its way into LLMs. I must admit, I, for one, had assumed these environments were clean because of the gated access and the payment models behind ChatGPT. However, we must also remember that the ad programme behind ChatGPT is currently running on the free tiers - the signup journey is actually potentially easy enough to be gamed through robotic traffic - but at this stage, I can’t see what the value of that will be and who the winners will be outside of OpenAI. In programmatic advertising, this plague was linked to publishers and website owners trying to generate false page impressions in order to get paid for them. There is no such gain in ChatGPT ads

And before we cast aspersions, we should treat those as vendor numbers, unaudited, from a company whose product is finding bots and frankly, we don’t know how good it is at identifying bots against humans. The pushback in the same threads was fair: ChatGPT itself runs on Azure, so an Azure address may simply be OpenAI’s own infrastructure fetching a page. All of this, though, does go to highlight that rumblings about the authenticity of traffic coming out of LLMs are gaining posture within practitioner threads, from those closest to the stats and the data that they’re outputting. We shouldn’t rush to assume that these new environments are as clean as we would like to believe - and again, this is why we need to get closer to log-level data in order to perform our own validations around the authenticity of the traffic

B2B testers reported a plainer problem: clicks arrived, demo bookings didn’t. The marketer is first left trying to work out if it was a poorly constructed ad campaign that caused the lack of performance, a messaging issue, or some other wider delivery problem. Or is the wider problem that they were partially buying non-human traffic. Verifying that without access to logs is incredibly difficult

Now apply the log test. Every one of those twenty partners sits downstream of OpenAI’s pixel and conversions API. They can tell you what happened on your site after a click OpenAI says it sent. None of them can truly tell you whether the click came from a person, because the only record of the click lives with OpenAI. The vendor with the refund product has an incentive to find bots. OpenAI has an incentive not to. In programmatic that argument gets settled by putting two files side by side. Here there is one file, and the 5 October announcement said nothing about invalid traffic. I also can’t find a public ad library, which means you can’t even see what your competitors are running.

There’s a B2B-specific wrinkle underneath. ChatGPT serves ads on the Free and Go plans. The people who sign six-figure software contracts tend to sit on Team or Enterprise seats their company pays for. So the person your ad can reach and the person who buys are separated by the pricing page. That’s an inventory gap, and no measurement partner can see it from where they sit. All of this should help us temper our expectations around what ChatGPT ads can realistically deliver for B2B buyers right now

AAMP 3.0: the agent reads 71 fields and still can’t find the receipt

In this section, we’re going to look at agentic advertising. The premise is taking humans out of the loop and allowing agents to negotiate between themselves to find the right placements at the right time for each advertiser that they represent. As a side note, there are a number of different rival protocols in play at the moment, and this is an emerging space within digital advertising, lacking the maturity to really jump into with two feet first. It’s also pretty complex and pretty new stuff, so hang on and bear with me if this is feeling a bit too technical - I’ll get to the wider point shortly

IAB Tech Lab published AAMP 3.0 on 22 September with a draft specification called OpenProposal, and the public comment window closed on 22 October. OpenProposal is the object a seller’s agent uses to describe what it is selling so a buyer’s agent can score it against a brief at machine speed: 71 fields under 11 headers, each field carrying one of seven mutability markers (immutable, seller-set, selectable, settable, supplied, requestable, derived), and only three of the 71 needing a round trip with the other side. All very complex, all very ad tech, and all very cutting edge.

The design claim, in the repository’s own words, is that composition is constraint satisfaction rather than negotiation. The lifecycle runs Publish, Discover, Compare, Qualify, Negotiate, Bind and Execute, Report, and execution lands in whichever existing standard the line item maps to: OpenDirect 2.1, the Deals API or OpenRTB 2.6.

I want this to work. FunnelFuel is building agent-facing tooling over our own account graph, so what follows is an argument about where the log lives. I’m for the agents.

The sharpest critique I’ve read came via a developer analysis that Deniz Genc summarised on 5 October. Once the accepted path is programmatic, execution is still an OpenRTB bid request and response on whatever exchange is already running. Nothing in that message pair identifies which proposal won. There is no proposal identifier and no pointer to the version of the brief that was scored. The worked example is CTV: a brief asks for 15 and 30 second spots, the proposal is scored on exactly those lengths, and the buyer’s agent then builds the auction call from a template that still carries a five-to-sixty second duration range. A DSP can legitimately return a 45 second creative. Every constraint agreed at the comparison stage disappears at the auction hop, and nothing in the protocol notices.

Reconciliation is a header in the specification and a gap in the protocol. The Report stage flows back through existing reporting standards, which is a polite way of saying it flows back through whoever already holds the log. If the buyer’s agent can’t prove the impression it won is the line item it accepted, the settlement reference points at the seller’s record by default.

@pesach_lattin put the economics in one line on X: agents don’t raise CPMs, they find the floor. I’d add that the floor gets found by whichever party holds the more complete record. Agentic buying, in this draft, keeps the black box and changes who writes the proposal. If the comment period didn’t produce a proposal handle in the bid request, the first revision needs one.

Performance CTV: the pixel counted 109, the CRM found 1,250

David Nyurenberg of InterMedia Advertising wrote an open letter on AdExchanger on 13 October asking performance CTV companies to stop promising that television now behaves like Meta.

First, InterMedia and Adstra tested IP-based ZIP code targeting and found only 23% of residential IPs reached the intended ZIP. Device-level CTV identifiers held up far better. Second, for a DTC advertiser where 63% of purchases happen on an iPhone, the DSP pixel recorded 109 completed checkouts on a recent campaign. When the agency matched impression IPs against the advertiser’s own CRM logs instead, it found 1,250.

That anecdote is the log test in miniature. The pixel is the platform’s count, built on whatever identity match the platform could make. The CRM match is the buyer’s count. The buyer’s count was eleven times larger. Whichever number you believe, the decision to keep or cut CTV was about to be made on the small one, and the small one measures the pixel, not the television. This is probably emblematic of the problem with measurement in 2026. Even when you’ve got a TV-based platform trying to present itself in the best light possible, it’s potentially underreporting its own impact by an order of magnitude - and therefore, you can see the value of running this exercise, both from an over- and under-reporting point of view. Based on the TV platform’s own data, you might be turning the campaign off, but based on the log-level testing, you might be scaling it up

For B2B the pixel catches even less. The conversion is a form fill three weeks later, on a laptop, from an office IP range that no household graph has ever seen, by a colleague of the person who watched the ad. The only count that means anything is the account-level one: did the companies we reached show up in pipeline at a higher rate than the companies we held out? That is a match you run yourself, against your own CRM, with your own list. It is also, in my experience, the only CTV number a CFO will accept twice.

Nyurenberg names the next problem himself: iCloud Private Relay already hid the iPhone IPs that connected a TV exposure to a mobile conversion, and Apple’s WebKit changes now reach LiveRamp, UID2 and ID5. Which brings us to the other half of the test.

The key is the other half

Two things make a log worth having: who holds it, and what identifier it’s keyed on. On 19 October Apple moved the second one.

The WebKit team published an explainer for the identity-resolution blocking that ships in iOS, iPadOS, macOS and visionOS 27. The mechanism it describes is the one the whole post-cookie industry runs on: a third-party script on a login or newsletter form captures the email address, sends it to a central server, mints an opaque ID bound to that email and hands it back to the page. Logging out doesn’t remove it. Then comes the sentence that matters for every “first-party” identity pitch you’ve sat through. Even when the graph is scoped to a single company’s data, WebKit says, the central resolver is a third party operating as a clearinghouse. Safari now blocks those domains at the network level.

Essentially, Apple is rug-pulling the whole identity fabric of the open internet.

The join key, the identifier that links a record on one site to a record on another, is what Apple has decided a tracker is. Server-side resolution was sold as the privacy-safe version of the same thing. Under this definition the location of the server is irrelevant. If a central resolver holds the key, it’s a third party, whoever’s data it holds.²

An account graph that resolves to companies sits on the other side of that line, which I argued at length in the account graph piece.

The key is a firmographic entity. Nobody’s email address is involved. Its source is public record and your own CRM. You can hold it yourself. I run a company whose core asset is exactly that kind of graph, so weigh my enthusiasm accordingly, but the structural point stands without me: a key you hold, resolved to a company, survives 19 October. A key a vendor holds, resolved to a person, doesn’t.

The same custody question applies to the signals you pay for, including intent. One cold-email operator on X this week wrote that “surging on CRM software” usually means one intern googled something.

The pushback from r/SalesOperations was that the real problem is data modelling and DataOps on the buyer’s side. Both are right, and they’re describing the same failure from two ends.

Meanwhile a sales-ops team on Reddit described paying Clay and ZoomInfo and still building its own job-change tracking in Claude, because so much enrichment traces back to the same handful of upstream providers. Three vendors, one underlying row, three invoices. Call that duplication, not diversification.

And this applies to managed service, mine included. If your programmatic partner won’t hand you the delivery file and an account-level match for you to map against your CRM, you’ve rebuilt the same problem with a friendlier logo on it. Ask for both. If you ask us, you’ll get both.

The log test

Here is the test in full. Put it to any channel, platform or data vendor before the money moves.

1. Who holds the count? Is there a record of what was served and what it cost that the seller doesn’t control? A DSP seat with log-level data passes.

2. What does it reconcile against? Can the outcome be matched to something you own, such as your CRM or your own server logs? A pixel reconciles against nothing. A CRM match does.

3. Does the paperwork travel? Do the terms you agreed survive into execution, and does every signal carry a source, a date and an expiry? If the proposal has no handle in the bid request, or the intent score has no timestamp, the answer is no.

4. Who holds the key? If an identifier links your records to someone else’s, who holds it, and is it a person’s email or a company? Since 19 October that question has an answer in Safari.

Two rules follow. A channel that fails questions one and two gets test budget to learn, never scale budget on the strength of its own CPA. A vendor that fails three and four goes into the CRM as a note, not a field.

Here’s where I’ll plant the flag. A channel that won’t give you a log you can reconcile isn’t a media buy. It’s a subscription with a dashboard. Most of the market will keep spending on those anyway, because the CPA looks good and the quarter is short, and I understand why. But if ChatGPT Ads showed you a CPA 15% below search and declined to show you the log, would you scale it? I wouldn’t. Tell me in the comments why I’m wrong, and what you’d need to see in the file before you did.

Field Notes

Three more cases from the week of 13 October where the vendor sets the exam, sits it and marks it.

Google’s “optional” cap, 14 October. Google Ads introduced channel prioritisation and a percentage budget cap for low-priority channels such as search partners and the Display Network. Both are described, in the blog post and the help page, as best-effort. From February 2027, Search campaigns using Display migrate automatically, and Google’s suggested way to keep today’s behaviour is to set Display to low priority at 50%. Practitioners on r/PPC summed it up as “sure google, very optional”. A cap that aims rather than binds is a target, and the platform grades whether it hit it. Read alongside August’s change that moves budget-limited campaigns toward their target CPA instead of letting them beat it, the pattern is clear enough: Google now decides what performance you’re allowed to have, and reports it to you.

GEO dashboards, threads from 16 to 19 October. Across r/GEO_optimization, r/aeo and r/DigitalMarketing, practitioners compared notes on AI-visibility tools. One agency found its roughly 200 tracked prompts matched real Search Console queries 9% of the time. Another worked out that appearing in four of ten runs gives a confidence interval of 17% to 69%. A third found 89% of URLs cited by ChatGPT were cited on a single day and never again. Much of the underlying data comes from the vendors themselves, so treat it as directional. The structure is the problem: the vendor chooses the prompts, then scores its own visibility against them. Before paying for AI visibility, ask for the prompt list and the Search Console overlap. If the overlap is near 9%, you’re buying a horoscope with a login.

LinkedIn’s suggested audiences, X, 19 and 20 October. @kaleighf posted that LinkedIn’s suggested audiences beat her own ICP targeting; the measure was impressions. @Simon_Akrouche replied that more impressions may not mean more conversions. @kamilrextin then did the arithmetic that should sit on every B2B media plan: 1,200 target accounts times eight people on the committee is 9,600 buyers, so $5,000 makes you faintly familiar to a few hundred of them. When a platform grades its own suggestion on its own inventory, the metric it picks is the one it sells. The useful number is that denominator, and LinkedIn is one channel against it. I wrote about why the rest of the committee lives elsewhere in [LINK: The Buying Committee Doesn’t Live on LinkedIn].

¹ Discrepancy thresholds were never standardised, but the working norm across the exchanges I dealt with was that anything under a few percent got billed to the seller’s number and anything over triggered an investigation. The point was never the threshold. The point was that both sides had a number to compare.

² The day after the WebKit explainer, Digiday reported that Apple’s own ads business is growing and counted dozens of open Apple Ads roles. I’ll let you draw your own conclusion about a company that defines the tracker and sells the alternative. It doesn’t change the definition, which is the part that will outlast the headlines.

Sources: WebKit, Blocking Identity Resolution Infrastructure, 19 October 2026; David Nyurenberg, AdExchanger, 13 October 2026; PPC Land on AAMP 3.0 and the 22 October comment deadline; IAB Tech Lab OpenProposal repository; Deniz Genc on the missing proposal handle, 5 October 2026; TechCrunch on OpenAI’s 5 October measurement announcement; PPC Land on Google’s best-effort cap, 14 October 2026; practitioner threads on r/adops, r/PPC, r/SalesOperations, r/GEO_optimization, r/aeo and r/DigitalMarketing, and posts from @pesach_lattin, @MrColdEmail, @kaleighf, @Simon_Akrouche and @kamilrextin on X, 13 to 20 October 2026.

Third-party cookies went, so we built UID2, ID5 and at once stage around 200 others. Safari started deleting first-party cookies, so we moved to server-set ones. Consent strings broke match rates, so we bought bigger identity graphs. Each fix was clever, and each one assumed the same thing underneath: that the web is still where B2B buyers do their research, so it’s worth the repair bill.

The launch of iOS 27 brought another repair bill, and to put this into a car analogy, the web is starting to feel like an old banger - asking for one too many expensive fixes.

Staring ahead into 2027 and beyond it is hard to remain bullish on the open web. In other words - I don’t think that assumption of the repair bill being worth it holds any more. Not because the web is dying. It isn’t, across the content web and the ultimate gateway to it, Google.com, traffic is still UP. So the scale remains, but I am fast growing to see it, from the B2B advertising POV, as a legacy format, in the same way print is a legacy format: still real, still capable of doing a job, no longer the place a serious plan begins.

That’s a different claim from “the web is dead”, and it leads somewhere more useful.

And I say all of this running a business that spends a lot of client money in the display web still, not least because the signal given by programmatic is so strong and differentiated. Now that signal has grown to cover the old world of big memory building audio, location and TV, my appetite is changing with it, as I will explore today

What you’ll learn in this article

• Why ten years of identity patches were fixing a format that was already losing its audience, and what Apple’s iOS 27 blocklist changes

• Where B2B buyer research actually went, and why third-party intent data is the first casualty (and how to fix that)

• My hollow middle theory: what the web looks like in 2031 and which parts of it survive

• The honest counterargument, that the buyer is still there and you just can’t see them, and why it leads to the same conclusion

• How the data models built for web programmatic now run television, audio and out-of-home, and why that’s where budget is going

• What the open web is still for, and why there’s no longer an excuse for the long tail

Plus Field Notes: the ID5 chief executive’s response to Apple, IAB Tech Lab’s answer, and the week’s two best Reddit arguments.

Ten years of patching a format that was losing its audience

Take the identity story first, because it’s the one the industry has spent the most money on. And it is a lot of money

In mid-September Apple shipped iOS 27, which on the surface of things, shouldn’t really matter for B2B advertisers. Within a week The Trade Desk could no longer serve ads in Safari on updated devices, because its core ad-delivery domain, adsrvr.org, sat on a WebKit list of domains Safari refuses to contact. UID2, ID5, LiveRamp, Permutive and Audigent were on the same list. Google’s ads on the same pages loaded fine. Then AdExchanger reported, citing two sources with direct knowledge of the WebKit changes, that the short list had been replaced by a private library of hundreds of CDPs, data sellers and ID-graph operators which Apple can update remotely, with no OS release and no public process. iOS 27 came armed with a kill switch for addressable digital advertising on Apple devices.

Most of the commentary has been about whether The Trade Desk was blocked by mistake. That’s the wrong question. Even if TTD’s delivery domain comes off the list, which looks likely given Apple asked them to test an iOS 27.2 beta, the identity vendors have no such argument to make. Apple has written itself a kill-switch for person-level matching in Safari, and by extension every WebKit browser on every Apple device. It can flip it for any vendor it chooses, whenever it chooses.

That person level targeting is the great win of programmatic, and the exact reason that makes it worth considering running an auction one at a time, 1 trillion times a day. Programmatic is all about end individual user targeting using data to inform the decision. The precision that still makes it exciting to target banners, because we can target and report on the company and job role behind the impression, is slowly eroding on display - whilst exploding in positive possibility on the old-new world of TV, audio and out of home.

For a B2B marketer, that lands on a specific thing: any account list that depends on an identity graph to find people on the web now inherits a fragility it didn’t have in August. I’ve argued before that the account graph and the identity graph are different objects. Here’s the sharpest version of why. The identity graph is what Apple can switch off. The account graph is less exposed, because it resolves to the company rather than the person, though it carries its own weakness, which I’ll come back to.

Identity is only the loudest example. Run the same exercise on format standardisation (a decade of IAB specs that the walled gardens ignore), on viewability (a metric made-for-advertising sites learned to game), or on measurement (more on that below). The pattern is the same: a lot of engineering effort to keep an environment addressable, which only makes sense if the audience is still there. If it isn’t, we need to move to where the audience is

The research moved to LLMs, and it took the signal with it, especially the ‘intent’ signal

Here’s the number that matters. G2 surveyed 1,076 B2B software buyers in March and found that 51% now start their research in an AI chatbot more often than in Google. Eleven months earlier that figure was 29%, wo what will it be in Q4 2027? 2028?

Seventy-one percent use a chatbot somewhere in the process, and 69% say chatbot guidance led them to a different vendor than the one they’d planned on. However upwards of 80% of purchases will still come from a day one consideration set. So I may start looking at DemandBase, 6Sense and FunnelFuel to run my digital ads, begin thinking I will use DemandBase and Claude persuades me that FunnelFuel is the better bet. You get the idea. CaliberMind’s 2026 attribution report, from Scott Brinker and Frans Riemersma, puts roughly 81% of the B2B journey outside anything the CRM can see.

Those are software-buyer samples, so hedge them for infrastructure, industrial or financial categories. But the direction is not in doubt, and it has a second-order effect that I don’t think the market has priced in.

Third-party intent data is a derivative of the open web. The co-op and bidstream feeds that tell you an account is “surging” are built from page visits on publisher and vendor sites. If the research moves into ChatGPT, Claude and Perplexity, the pages that fed those models go quiet, and the surge signal thins out. That isn’t my theory. It was the sharpest comment in a 17-reply X thread this week in which outbound practitioners variously called enterprise intent a tiebreaker at best and a scam at worst. The intent vendors’ own numbers aren’t kind either: a DemandScience survey cited by MarTech had 87% of marketers calling intent signals unreliable or inflated. Intent is the canary, and it’s in danger of stopping singing. Knowing some of the major players, like Bombora, I’m sure they are capturing a vaster array of signal now and if not already, are looking at what they can get out of podcasts, YouTube, newsletters, and non-traditional publishing sources to refill the tanks. I’m bullish on them.

Now look at what happens when the ad industry tries to follow the buyer into the new environment. OpenAI has been selling ChatGPT ads since February. Its own 5 October update named 20 measurement partners and an image-led format, which tells you how early it still is. I ran a test on ChatGPT which underwhelmed me enough to pause the 3 tactics; and one by one they automatically/bugged and turned back on. The whole system feels like ad platforms from at least 10-15 years ago, basic and buggy. It will improve but this is very far from the inner workings of the best of breed adtech platforms.

B2B advertisers in the pilot report they can’t see which prompts served their ad, their legal teams won’t sign the tracking terms, and the ads only run on the Free and Go tiers. I’d assume these tiers aren’t loaded with senior B2B decision makers, but could be wrong, but really we need ads talking to the top tier users and enterprises before I’m jumping with excitement. One B2B SaaS study put 74% of SaaS marketers on paid ChatGPT plans (Omni Lab, June), which would mean the exact buyer you want has paid to opt out of the one ad format inside the room. OpenAI’s line at Advertising Week was that knowing who the user is matters less than enriching the conversation. That’s an honest description of a closed environment. It’s also the opposite of what a decade of identity investment was for.

The hollow middle - what and which websites survive in the next 5 years?

So play it forward five years. Do we still have websites?

Yes, but the web gets hollowed out from the centre, and I’d put a name on the new shape: the hollow middle.

I believe the destinations survive. News brands with a daily app habit. Amazon. LinkedIn, Reddit and YouTube, which are more walled garden than open web at this point. Your own site survives assuming you are a vendor or agency, but its job changes from the place a buyer discovers you to the place a buyer arrives once a model has already shortlisted you, to validate what the machine said. One visit, late, decided in advance. The vendor website becomes a conversion endpoint and a machine-readable source of truth, and both of those are worth investing in.

I ran Claude over billions of page views that FunnelFuel has tracked across vendors in the last few years and found the above is happening today. 3+ years ago, we saw early account engagement on typical upper funnel pages, like blogs. Today, the first observed touchpoint is often as deep as pricing. Vendors keeping their pricing off their websites is creating a last mile that the agents can’t complete. We’ll see how that evolves

What thins out is the middle: the comparison sites, the review long tail, the second-tier trade press that lives on search traffic, the vendor blog that was really an SEO play, and the vast quantity of recycled content underneath all of it. That middle is where B2B contextual display grew up. It’s the inventory most B2B programmatic was built to buy, and it’s the inventory a buyer who starts in a chatbot has the least reason to ever touch.

The ANA’s 2023 supply-chain study found that participating advertisers’ programmatic ran across an average of 44,000 websites and apps, and that only 36 cents of each dollar entering a DSP reached a consumer on a viewable, measurable, non-MFA impression. I’d struggle to name 440 sites where a B2B decision-maker spends real time, let alone 44,000.

Or can you just not see them any more?

Here’s the counterargument, which of course deserves a proper hearing: maybe the buyer hasn’t left the web. Maybe your measurement has. There is no doubt that cookie banners, private browsing, VPS/VPNs and other such tools have made resolvable traffic thinner.

A practitioner audit doing the rounds this week claims that across 18.5 million events on technical B2B audiences, 41% of browser pixels failed to fire. One dataset, unverified, so treat it as directional. But it sits on top of a pattern that is well established. Safari’s tracking prevention caps first-party cookies at seven days, so a signup correctly attributed on day zero shows up as Direct when the contract is signed on day 14. PPC forums have been reporting 25 to 30% click loss and missing GCLIDs for months. An AdExchanger, Bombora and PrograMetrix study this spring found just 10% of B2B marketers very confident their audience data reaches the right accounts, and only half confident they reach even 50% of their target list. Vector’s October survey of 239 ABM marketers was blunter still: 91% not confident their ads reach the right people inside target accounts, 84% unable to show evidence of pipeline

So when LinkedIn, Reddit or display “doesn’t convert”, one honest reading is that the signal plumbing is broken, not the channel. I take that seriously. Some of what looks like channel death is measurement death. There’s a similar argument with content syndication and leads - tech and data has improved but leads conversion hasn’t. It could be that it isn’t the leads and is - at least partly - what the vendors do with them

But notice where it leads, no pun intended. Whether the buyer has left the browser or you’ve simply lost the ability to see them in it, the conclusion is the same: stop building the B2B plan around browser-level, person-level identity. Either the person isn’t there, or the identifier that would prove they’re there has been deleted by Apple, by ITP, or by the buyer’s own privacy shield. A planning model that only works when all three cooperate is a legacy model.

If you know someone still running a media plan that’s 90% web display because that’s what the DSP defaults to, this is the piece to send them.

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The data models outlived the format

This is the part that gets lost in the “web is dying” conversation, and it’s the part that matters most to how I think about FunnelFuel’s work.

The thing programmatic got right was never the banner. Heck as long ago as the first ‘year of the mobile’, we started evolving that with native advertising, which almost but never quite flew.

The bit that was and still is very very cool and powerful is the buying model: an auction per impression, audience data applied in real time, frequency controlled at the account rather than the campaign, performance read back into the bid. Those models are the best thing adtech built, and they were wasted on 300x250s.

What’s happened over the last three or four years is that the same models now run on screens that do something a banner never could. Television has data-driven buying now. So does out-of-home. So does audio, including the best B2B podcasts. And the creative executions on those screens are emotive, memory-building, delivered when someone is actually paying attention, which is exactly the brand-memory job the dark funnel demands.

The budgets have noticed. In the ANA’s Q3 2025 benchmark, CTV reached 45.6% of participants’ programmatic spend, up from 28% at the end of 2024. A Demand Gen Report survey found 98% of B2B brands planning to increase CTV budgets and only 4% with no CTV plans at all; a vendor-run survey, so discount it, but the direction matches what we see in client plans. Advertiser Perceptions’ March survey had 82% of marketers and agencies buying audio programmatically, up from 41% in early 2022, and Madison & Wall expects automation’s share of audio to roughly double by 2030. In the UK, IAB UK reports DOOH at 67% of out-of-home spend, from 32% a decade ago, with programmatic the fastest-growing route in. VIOOH’s 2026 survey found 95% of marketers increasing programmatic DOOH spend were funding it from other digital channels.

The B2B question is whether these can be bought by account rather than by household, and the answer now is yes. Resolve a target account to its sites, its IP ranges and its known device clusters, and CTV, audio and out-of-home become account-addressable with no browser in the loop. A finance director watching the same streaming service as everyone else can be reached because of where she works, not because of a cookie she cleared last Tuesday. Out-of-home around the offices, the stations and the conference venues people still physically go to. Podcasts people still listen to on the commute. None of it depends on an identifier Apple can revoke.

This is also where the account graph’s own weakness needs saying out loud. IP-to-company matching degrades when people work from home, and the Vector survey found confidence in reach nearly doubled when visitor de-anonymisation sat at the centre of the stack. The best account graphs, like the one we built, don’t rely on this signal, they map enrichment to more portable device IDs, which can be enriched by office attendance but travel home with the B2B decision maker, ready to inform targeting on the commute and afterwards. So the account graph isn’t a magic replacement and one valid signal class is in decline, but it stands firm as a portal of addressability into the best of the ad worlds formats. It’s the resolvable thing that survives, and it needs first-party evidence underneath it. But it is the thing the next five years of B2B media get planned on, because it’s the only addressable layer that works across every screen a buyer still looks at.

What the open web is still for

None of this means pulling the web out of the plan. Legacy formats are extremely useful. Print still sells to people who read print.

It means the web stops being the thing a plan is crowbarred into and becomes the thing you cherry-pick. Buy the best impressions in the most premium, trusted environments where a decision-maker actually spends time: the FT, the trade titles with real subscriber bases, the specialist publishers whose newsletters get opened on Monday morning. Buy them on an inclusion list, against the account graph, with frequency capped per account, and treat them as the premium layer they are.

And stop buying the long tail. There is no longer any argument for it. The ANA benchmark shows what happens when advertisers take this seriously: MFA spend fell from 15% in 2023 to 0.39% among participants by Q3 2025. That didn’t happen through better exclusion lists. It happened because buyers switched to inclusion lists and simply stopped funding the noise. The same discipline applies to the premium screens, incidentally: Pixalate put invalid traffic in open programmatic CTV at around 18% in Q3 2025, so “CTV” on an insertion order is not a quality guarantee either. Premium is a supply decision you make, not a channel you tick.

If the buyer spends their research time in a chatbot, their evenings on a streaming service, their commute in a podcast and their working day in an office, meeting or event, then the media plan should look like that, with a thin and deliberate web layer on top. Anything else is building the plan around the measurement rather than the buyer.

What to change on the plan

Four things, in rough order of how fast you can do them.

1. Reclassify the website. It’s a late-stage conversion endpoint and a source of truth for the models that now write your shortlists. Invest in it on those terms: clean structured content, fast, demo-first, every claim verifiable. Stop measuring it as a discovery channel.

2. Move identity out of the browser. First-party data, server-set cookies, hashed email where you have consent, and account resolution that doesn’t need a third-party identifier to function. Assume any browser-side signal can be switched off by someone who isn’t you.

3. Rebalance toward the persistent behaviours. CTV, audio, YouTube and out-of-home, bought against the account list, frequency-managed at the account. Fund it from the long tail you should have cut already.

4. Cherry-pick the web. Inclusion lists only, premium environments only, account-level frequency, and read it as a brand-memory layer, not a click source.

Field Notes

Three items from the week, two of which land squarely on the argument above.

ID5’s chief executive has responded to Apple. In the first public statement from a blocked vendor, Mathieu Roche argued Apple’s real target is ad-funded publishing and said regulators should look at it. Worth reading in full. The practitioner point: a vendor on that list cannot engineer its way off it, because the list isn’t in the open-source WebKit build and the block applies to a domain, not a behaviour. For anyone whose B2B audience matching runs through ID5 or UID2 on Apple devices, this is already live in your delivery mix, not a future risk.

IAB Tech Lab’s answer is to leave the browser. Anthony Katsur has spent the fortnight telling publishers to move the ad stack onto Trusted Server, publisher-controlled infrastructure that doesn’t depend on the browser contacting third parties. Note what that is: the open web’s own standards body conceding that browser-side identity is no longer a safe place to build. It corroborates the thesis more neatly than I’d have liked.

The two best Reddit arguments this week aren’t about the web at all. r/gtmengineering spent 20 comments on whether GTM engineering is a real role or a temporary premium for wiring Clay together, with one thread already replacing Clay with Claude Code [LINK: r/gtmengineering, 9 and 10 Oct]. And r/revops had 21 comments on the fact that most teams still can’t tell a parent company from its subsidiary, then buy identity graphs that assume their CRM is right. Both are the account graph argument in disguise: before you buy another matching layer, check whether you can say who the buying entity actually is.

So here’s where I land. The open web isn’t going anywhere, and neither is web display. But it’s a legacy format for B2B now, and the sooner a plan treats it that way, cherry-picked and premium rather than default and long-tail, the sooner the budget gets to the screens where the buying group actually is.

Is the open web still where your buyers are, or just where your measurement is? If you think I’ve got this wrong, the comments are open.

Is your media plan still 80% web display because that’s what the DSP defaults to?

FunnelFuel runs B2B programmatic three ways: as a full managed service across display, CTV, audio and out-of-home; as a licensed signal product if you want the account graph inside your own stack; or as a bespoke fit for teams building in-house.

Connect with me on LinkedIn, send me a Substack DM, or reply to this email (mike@funnelfuel.io). Or see how we build account-based plans across every screen.

No pitch, no obligation. If it’s useful, we’ll talk.

B2B intent data is evidence that a company is researching a topic related to what you sell. It comes from three places: content consumption tracked across a network of B2B publishers (third-party or offsite intent, with Bombora the best-known source), activity on review sites such as G2 and TrustRadius (review-site intent), and behaviour on your own website and campaigns (first-party intent). A buying signal is the wider idea: any behaviour that changes the odds an account will buy, including which pages a buying committee reads on your site, how often they come back, and how much attention they give your ads.

Intent data is the earliest signal you can buy that an account is moving. Most teams buy it as if it were a lead source, and that is where the trouble starts. Its value depends almost entirely on what you have built to act on it: the first-party signals you combine it with, the rules for each combination, and the person who owns the next step. Buy the data without that and you get what I have watched happen inside dozens of B2B teams. Dashboards go live, accounts “surge”, lists get exported, and six months later nobody can say what it did for pipeline.

I’ve worked in adtech since 2011, and FunnelFuel’s analytics and media business lives on this stuff every day. This hub pulls together everything I’ve written on intent, buying signals and the gap between buying data and getting pipeline from it. I’ll keep it current.

New here? I’m Mike Harty. I’ve worked in adtech since 2011 and built programmatic platforms (PowerLinks, now FunnelFuel) before most of the market had heard the phrase “B2B programmatic”. The B2B Stack is where I write up what I see on live campaigns, without the vendor spin. Two pieces that pair well with this hub: Segment vs Signal: How to Actually Run B2B Programmatic and What Are High Value Actions (HVAs)?.
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What this hub covers:

• What B2B intent data is, the three places it comes from, and how a “surge” is calculated

• Why most teams buy intent and never get pipeline from it

• What changed in 2026 (AI chat research, a thinning publisher signal, Apple’s iOS 27 block, agent traffic) and what I expect to change in 2027

• The B2B signal stack: nine signal types, what each one tells you, and how each gets misused

• High value actions and an account scoring model that beats fit or intent alone

• A decision guide and a checklist for turning a signal into action

What is B2B intent data, and where does it come from?

B2B intent data is a probabilistic, account-level signal that a company is researching a topic, built from behaviour observed somewhere you can see. It gives you an account and a topic with a probability attached. It does not give you a person, a lead or a decision, and those three confusions cause most of the disappointment with it.

Third-party, or offsite, intent is the version most people mean. A co-operative of B2B publishers shares anonymised content consumption with a provider such as Bombora. The provider resolves that consumption to companies, mostly through IP and other identity matching, and tracks it against a topic taxonomy (Bombora’s runs to 20,000+ topics in 2026). A surge is consumption on a topic that runs above that account’s own historical baseline. That last clause matters more than it looks, and I come back to it below.

Review-site intent is narrower and more specific: someone at an account viewed your category page or compared you with a competitor on G2 or TrustRadius. It tells you less about the market and more about you.

First-party intent is what accounts do on your own site, with your content and with your ads. It has the highest fidelity, because the interest is in you rather than in the topic, and the lowest reach, because only accounts that have already found you can produce it. It is only useful once it is resolved to accounts, which is harder than it was (see “What changed in 2026”).

Good intent data is one of the most powerful early signals in B2B. It shows you accounts researching your space long before they reach your site. It earns its keep when you combine it with your own first-party behaviour and someone has a clear job for acting on it. Most teams buy the data and skip that second part.

Where to start if you are new to this:

• B2B Intent Data: Why Most Teams Use It Wrong: the operating-model problem behind most intent failures.

• Segment vs Signal: How to Actually Run B2B Programmatic: why licensed segments disappoint, and what to use instead.

• What Are High Value Actions (HVAs)?: the first-party signals that tend to come before pipeline.

• B2B Account Scoring: A Model That Beats Fit and Intent Alone: how to put the signals together.

• Intent Data Needs an Expiry Date — why every B2B signal has a shelf life, and how to stop acting on expired surges.

Why most teams don’t get pipeline from intent data

Intent data fails in most organisations for the same reason: nobody built the model that turns a signal into an action. There is a story I have watched play out inside dozens of B2B teams. Marketing buys intent data. The dashboards go live and accounts start “surging”. The surges get exported as a static list or fired straight at SDRs as alerts, and nothing else changes. Six months later pipeline impact is unclear, sales says the data is noisy, and marketing says sales isn’t following up. The data was fine. Nobody built the plan for using it. I set it out in full in B2B Intent Data: Why Most Teams Use It Wrong.

The common mistakes:

A surge is not a sales alert. Treat a surging account as a hot lead, have an SDR call too early, and your domain gets mentally tagged as “too salesy”. The timing evidence is unambiguous. In 6sense’s 2025 Buyer Experience Report (nearly 4,000 buyers, November 2025) the average B2B buying cycle ran 10.1 months, buyers did not contact a seller until they were 61% of the way through it, and 95% of the time the vendor that won was already on the buyer’s Day One shortlist. Gartner’s buying-journey research puts the share of the whole journey that buyers spend meeting any supplier at 17%. A surge is a cue that a shortlist is forming. The phone call is for later.

Most of the market isn’t buying right now. The LinkedIn B2B Institute’s 95-5 rule, based on Professor John Dawes’s work at the Ehrenberg-Bass Institute, holds that only around 5% of a category’s buyers are in market at any given time. It is a heuristic drawn from category purchase frequency rather than a measured number, and it has held up well in practice. Intent is brilliant at spotting accounts moving into that 5% early, but only if you are already reaching them with always-on, account-level advertising so you are known when they start, and you keep presence with the 95% who will buy later. More on this in Segment vs Signal.

It gets bought on its own. Intent on its own tells you an account is researching your space. Combined with first-party high value actions and buying-committee breadth, it tells you who is researching, how seriously, and when to step up. Most teams never join those up.

Mike’s take: Intent data is some of the best early signal you can get in B2B. Where it goes wrong is how it is used. The teams that do well treat a surge as the moment to start showing up for that account, with ads, content and a close eye on their own site, and only pick up the phone once the account’s own behaviour says it is time. Surge to presence, not surge to call.

What changed in 2026, and what it means for intent data

Four things changed in 2026 that affect how much a third-party intent signal is worth and what you have to do to use it. None of them makes intent data obsolete. All of them make the first-party half of the model more important.

Research moved into the chat window. G2’s April 2026 research (1,076 B2B software buyers, surveyed in March) found 51% now start vendor research with an AI chatbot more often than with Google, up from 29% eleven months earlier. 69% chose a different vendor than they had planned because of what a chatbot told them, and a third bought from a vendor they had never heard of. 80% still use Google somewhere in the journey. Forrester’s State of Business Buying 2026 (January 2026) found generative AI was the most cited research interaction, and that buyers then validate what it tells them through a buying network of 13 internal stakeholders and 9 external influencers on an average purchase. A publisher co-op can only observe research that lands on a member site. A growing share of the earliest research now produces no pageview anywhere. Third-party intent is still the earliest signal you can buy; it now covers a smaller slice of the research than it did in 2023.

The publisher signal is thinning and concentrating. Chartbeat’s 2026 Publisher Playbook, reported by Search Engine Journal on 24 September 2026, shows Google Search traffic to publishers down 40.2% year on year to July 2026, after a 21.9% fall the year before. Chartbeat data published by Axios in March 2026 showed the damage is uneven: search referrals down 60% over two years for small publishers, 47% for medium, 22% for large, with AI chatbots sending under 1% of publisher referrals. Total pageviews across Chartbeat’s network barely moved; what changed is that the traffic is now direct, loyal and dark-social, and it sits with the biggest brands. That matters for intent because a surge is consumption above an account’s own baseline. When baselines fall and the remaining traffic concentrates on the loyal audiences of the largest trade publishers, you get fewer observations per account per topic, more week-to-week noise, and a skew toward whatever those publishers cover. That is my read of the mechanism rather than a published finding. The practical consequence is the same either way: a single week’s surge is a weaker standalone fact than it was, and first-party confirmation matters more.

Apple closed the open-web identity layer on its devices. Apple began rolling out iOS 27 on 14 September 2026, and on 19 October the WebKit team confirmed that Safari and WebKit on iOS, iPadOS, macOS and visionOS 27 now block network access to third-party identity resolution infrastructure, the services that turn an email address or a cookie into a cross-site identifier. The Trade Desk’s ad-serving domain was caught by the list and restored in a 27.2 beta; ID5, LiveRamp and others were reported affected. For B2B account resolution the direction is clear: less reliance on email-to-ID and cookie matching on Apple devices, more weight on first-party, server-side resolution, and more account signal packaged on the sell side into deal IDs before the auction rather than bolted on in the DSP. More in How AI Rewired the B2B Dark Funnel.

Half your visitors may be software. On 3 June 2026 Cloudflare’s CEO reported that automated requests had passed human traffic for the first time, 57.5% to 42.5% of HTML requests on Cloudflare Radar. HUMAN Security’s 2026 State of AI Traffic report put the growth in agentic AI traffic (agents browsing on a person’s behalf, not crawlers) at roughly 7,851% year on year. An AI agent reading your pricing page for a buyer is a signal: someone delegated the research to it. It is also a session that will not resolve to a company by IP, carries no attention, and will inflate repeat-visit HVAs unless you classify it. Agent sessions need to be their own signal class, counted and reported separately from humans.

One more, smaller shift: intent data is consolidating and getting cheaper to reach. Forrester’s Q1 2025 Wave on intent data providers evaluated 15 vendors and named Bombora, 6sense, Demandbase, Intentsify, Informa TechTarget and ZoomInfo as Leaders, describing Bombora as the gold standard for account-level feeds and noting that many of the others include Bombora data directly. ZoomInfo, Apollo and Cognism all resell Bombora signal. If the same co-op sits under most of the “intent” in your sales stack, the data is a shared, strong foundation. What sets you apart is the model you run on it.

Mike’s take: The direction of travel is clear. Third-party intent is getting earlier, because it sits upstream of your site, and thinner, because less research happens where it can be observed. That raises the value of everything you own: HVAs, committee breadth, attention, and the account graph that ties them together. If you are planning 2027 budgets, put the extra effort into the first-party side, so the intent you already buy has something to land on.

What changes in 2027: my view

This is my read of where it goes next, not a forecast with numbers on it. I’ll mark it against what happens when I review this hub.

The surge becomes a trigger, not a list. Third-party intent keeps its place as the earliest view of the market. What changes is what it plugs into. The teams that get value in 2027 will wire a surge straight into always-on account-level media and a first-party watch, and judge it on what the account does next. The ones exporting a weekly CSV to SDRs will keep wondering why it “doesn’t work”.

Committee breadth becomes the main buying signal. Buying groups are already big, and Forrester says they double when the purchase includes generative AI features. If several roles from one account are reading you in the same fortnight, that tells you more than any single score.

Agent traffic gets its own line in the report. By 2027 I’d expect any serious analytics or ABM setup to split human and agent sessions as standard. An agent on your pricing page is a signal that someone delegated the research. It is not a human visit, and scoring it as one will quietly wreck your HVA data.

Account resolution moves server-side and sell-side. With identity resolution blocked on Apple devices, more of the work moves to first-party, server-side resolution on your own site, and to account signal packaged into deals before the auction.

Being in the AI shortlist becomes a brand job. You won’t track it to the click. You’ll measure it like the dark funnel, through prompt panels, branded search lift and self-reported source, and you’ll get there the old way: steady presence with the accounts you care about, so you’re already known when someone asks the chatbot.

Mike’s take: None of this makes intent data less useful. It makes it more useful to the teams that built the first-party side properly, and less useful to anyone still treating a surge as a lead.

The B2B signal stack: nine signal types and how to use each one

Not all signals are equal, and the mistakes are specific to each type. This is my working view, built from the signal stack post, the HVA guide and the account scoring model. Value means what the signal is worth when it is used properly. The last line of each is where teams usually go wrong.

Fit (firmographic and technographic)

Examples: industry, size, HQ, tech stack.
What it tells you: whether the account could buy.
Value: medium. Necessary, but says nothing about timing.
How it gets misused: treated as the whole targeting plan, built on old classification codes. See the SIC code problem.

Third-party (offsite) intent

Examples: publisher co-op topic surges.
What it tells you: which accounts are researching your space, often weeks before they reach you.
Value: high, as an early signal. Still the earliest view of the market you can get; it now has to work harder alongside your own data because more early research happens in AI chat.
How it gets misused: fired at SDRs as a sales alert or frozen into a static list, instead of triggering always-on account-level ads and being combined with first-party signals.

Review-site intent

Examples: category page views, competitor comparisons and profile visits on G2 or TrustRadius.
What it tells you: an account is comparing options, and sometimes which ones.
Value: high specificity, narrow coverage. Strongest for software categories with an active review-site presence.
How it gets misused: treated as market coverage when it only covers accounts that use review sites, and treated as a lead when it is still research.

Licensed audience segments

Examples: pre-built “IT decision maker” segments in a DSP, with no account-level detail.
What it tells you: a rough profile match.
Value: low.
How it gets misused: bought because they are easy, even though you cannot break delivery down to named accounts. See Segment vs Signal.

IP-to-company resolution

Examples: reverse-IP identification of site visitors and ad exposures.
What it tells you: which company a visit probably came from.
Value: variable. Single-signal IP can be weak.
How it gets misused: used on its own. Hybrid work scattered buyers across home networks, iOS 27 removed a layer of corroborating identity on Apple devices, and agent traffic now arrives from data centres. My Reference Index notes IP agreement rates as low as 3% on some enterprise accounts (FunnelFuel data, see the Reference Index).

First-party high value actions (HVAs)

Examples: pricing, integration and security pages; repeat visits; spec downloads.
What it tells you: a specific account is evaluating you.
Value: high, especially when several people from one account do them.
How it gets misused: never defined, never resolved to accounts, and in 2026, never separated from agent sessions.

Attention

Examples: time in view, scroll depth, active engagement with ads and content.
What it tells you: whether anyone took in the message.
Value: medium to high as a quality filter. Humans only; an agent has no attention to measure.
How it gets misused: ignored in favour of clicks.

Buying-committee breadth

Examples: multiple roles or offices from one account engaging in the same window.
What it tells you: a group process is probably under way.
Value: high, and rising as buying groups grow (Forrester’s 2026 average is 13 internal stakeholders).
How it gets misused: not tracked, because nobody built the account view.

Presence in AI answers

Examples: whether your brand appears when a buyer asks a chatbot for a shortlist in your category.
What it tells you: whether you are in the pre-CRM shortlist at all.
Value: medium, as a diagnostic rather than a targeting signal.
How it gets misused: treated as a new SEO KPI with pixel-perfect attribution to pipeline. Measure it the way you measure the dark funnel: prompt panels, branded search lift and self-reported source, not a tracked click. See How AI Rewired the B2B Dark Funnel.

The pattern is simple. Third-party intent tells you early which accounts to start showing up for. First-party behaviour and committee breadth tell you when to step up. Used together, they are far stronger than either on its own.

Segments vs signals

A vague segment is a profile someone else guessed at. A signal is something an account did. Too often B2B buys generic DSP segments because they are easy. If you cannot break delivery down to named accounts or committee members, attribution and the board conversation both fall apart. In Segment vs Signal I also argue that a 50,000-account target list is really dozens of different audiences sharing one CSV, and that it should be tiered by deal size.

More on the same theme:

• Why Generic Pre-Built Segments Are Losing Ground

• Signals vs Audiences: Designing a B2B Advertising Operating System

• Why B2B Advertising Needs a Probability Mindset

Mike’s take: Whatever data you use, ask one question of your media: can you see which accounts, and which people in them, saw and engaged with the ads? If you can, the signal is doing its job. If you cannot, you have bought a list and you are guessing.

If there is one person on your team who still thinks a surge means “call them”, send them this page.

The signals that predict revenue: HVAs and account scoring

A high value action (HVA) is a specific behaviour that has historically come before pipeline, such as a deep read of pricing, integration or security pages, a spec-sheet download, or the same account returning three times in a fortnight. In our experience across B2B advertisers, the large majority of accounts seriously evaluating a vendor do not fill in a form before the decision is materially formed. The external evidence points the same way: 6sense found 94% of buying groups rank their shortlist before contacting any seller. That is why form fills are the wrong thing to optimise for (HVA guide).

HVAs work with third-party intent, not against it. Intent shows the research an account is doing across the market, often before it reaches you. HVAs show what it does once it gets there. My account scoring model blends three things:

Fit signals, up to 50 points: firmographics such as industry, size and HQ location, plus technographic fit.
Behavioural signals, up to 30 points: website HVAs such as pricing views, case study downloads and contact requests.
Multipliers, up to 20 points: recency (activity in the last 7 to 14 days counts more than anything 30+ days old), committee breadth and similar.

In that piece I describe seeing a “meh” fit account go from first touch to deal in under 30 days, because four buying committee members hit pricing pages, integration docs and high-intent content in a single week. That is why behaviour and breadth sit in the model alongside fit and intent.

Go deeper:

• 100+ High Value Actions That Predict B2B Revenue

• Attention as a Buying Signal. In one FunnelFuel test, a cohort with a 0.08% CTR showed 41% higher scroll completion on high-value product pages than the “high CTR” cohort.

• From Intent to Revenue: Why Speed Wins

Signals you can’t see: the dark funnel

A lot of B2B research happens where no pixel will ever reach: private Slack and WhatsApp groups, conversations over coffee, research behind a rejected cookie banner, and now the AI chat window that half of software buyers open before they open Google. In The Xerox Strategy I argue that this is permanent. The answer is to make sure your brand is already in the buyer’s head before the private conversation starts, rather than trying to light up every corner.

Some of it is recoverable, though:

• The B2B Dark Funnel Is a Data Problem, Not a Buyer Problem. Buyers still visit vendor websites before they buy. They are just harder to identify since they left the corporate office.

• The Dark Signal Layer: Capturing What DSPs Miss covers the micro-signals consumer DSPs never see, like a VP of IT abandoning a security assessment form and coming back three days later to finish it.

• The Account Reporting Layer Brand Advertisers Keep Throwing Away shows how brand campaigns already produce account-level intelligence that most advertisers ignore.

• How AI Rewired the B2B Dark Funnel

Mike’s take: You will never see all of it, and you do not need to. Intent and your own site data show you which accounts are moving. Always-on advertising makes sure the rest already know you when they start.

Decision guide: what to do with a signal

Each combination below is a signal pattern, what it probably means, and the action I would take. Agree the rules with sales before the first alert fires.

Third-party intent surge, nothing else.
Probably means: the account is researching your space, often early.
Do: move it into always-on account-level ads now and watch for first-party HVAs. Do not cold-call off the surge alone.

Intent surge plus first-party HVAs from one person.
Probably means: one researcher is interested.
Do: personalise ads and content. Alert the account owner, with a soft touch.

HVAs from several roles or offices in the same fortnight.
Probably means: a buying group is forming.
Do: prioritise for sales this week, tailor messaging to each role, and raise media pressure.

Agent session on pricing, security or integration pages.
Probably means: someone at an account delegated the research to an AI tool.
Do: log it as its own signal class. Do not count it as a human visit, do not score attention on it, and watch for the human follow-up visit that usually comes next.

High fit, no signal at all.
Probably means: not in market, or researching invisibly.
Do: keep brand presence and frequency sensible. Do not drop them.

Signal from a low-fit account.
Probably means: curiosity, a student, a competitor or a poor match.
Do: monitor, but do not spend sales time.

Checklist: getting value from intent data

1. Define a short list of HVAs from your own won deals, so you can combine them with your intent data from day one.

2. Resolve signals to accounts, not just to individuals or cookies, and know which resolution method each signal relies on.

3. Classify sessions as human or agent before they reach the scoring model.

4. Set rules for each signal combination (see the decision guide) and agree them with sales.

5. Name an owner and a response time for “act now” accounts.

6. Weight signals by what they tell you and combine them. Do not add them up as if they were all equal.

7. Record which of your intent sources are the same co-op resold, so you do not count one signal three times.

8. Review scoring against closed-won and closed-lost deals every quarter, and date the review.

9. Keep brand pressure on the high-fit accounts that are not showing signals yet.

Who sells B2B intent data?

The market has an upstream and a downstream. Upstream are the sources: Bombora’s publisher co-op for topic intent, G2 and TrustRadius for review-site intent, Informa TechTarget for intent from its own editorial network, LinkedIn for engagement signal inside its walls. Downstream are the platforms that package sources with scoring, advertising and orchestration: 6sense, Demandbase, ZoomInfo and the sales tools that bundle an intent feed. Forrester’s Q1 2025 Wave evaluated 15 providers and named Bombora, 6sense, Demandbase, Intentsify, Informa TechTarget and ZoomInfo as Leaders. Much of the downstream intent is built on Bombora signal, which tells you how good the underlying data is. Before you buy, ask three questions: where does the signal come from, do I get the signal or only a score, and can I resolve delivery back to named accounts and the people in them.

Related hubs

• ABM platforms and account-based advertising

• B2B programmatic advertising

• B2B attribution and measurement

FAQ

What is B2B intent data?
B2B intent data is evidence that a company is researching a topic related to what you sell. It is collected from publisher networks, review sites or your own website, resolved to accounts, and expressed as a probability rather than a fact. It is a powerful early signal, and it works best combined with your own first-party data.

What is the difference between first-party and third-party intent data?
First-party intent comes from your own website and campaigns, so it shows interest in you. Third-party (offsite) intent comes from other sites, such as a publisher co-op, so it shows which accounts are researching your space, often before they ever visit you. The two are strongest together.

What is offsite intent data?
Offsite intent data is research activity captured away from your own website, for example across a network of B2B publishers. It shows you research you would otherwise never see, which makes it one of the earliest signals you can buy. Pair it with first-party signals to judge how far along an account is.

How is a Bombora Company Surge calculated?
A surge flags an account whose content consumption on a topic, observed across Bombora’s publisher co-op, is running above that account’s own historical baseline. Because it is relative to the baseline, changes in how much research happens on co-op sites change what a surge means. It is an account-level, probabilistic signal, not a named person.

Is intent data still useful now that buyers research in AI chatbots?
Yes, with a caveat. G2 found in April 2026 that 51% of B2B software buyers start research in an AI chatbot more often than Google, and that research produces no pageview a co-op can observe. Third-party intent remains the earliest signal you can buy; it now covers less of the journey, so first-party confirmation matters more.

Why don’t some teams get pipeline from intent data?
Usually because nobody owns the step after the signal. Surges sit in a dashboard or a static list, sales calls them too early or not at all, and there is no always-on advertising to the accounts that surge. Clear rules, a named owner and first-party confirmation fix most of it.

How do you get the most from intent data?
Combine it with your own HVAs and committee breadth, put surging accounts into always-on account-level ads straight away, classify human and agent sessions, and act quickly on accounts where several people are engaging in the same window.

What are buying signals in B2B?
Buying signals are behaviours that change the odds an account will buy. They include pricing-page visits, repeat visits, several roles from one company engaging in the same window, and high attention on your ads and content. Intent data is one kind of buying signal.

What is a high value action (HVA)?
An HVA is a specific behaviour that has historically come before pipeline, such as a deep read of pricing or integration pages. HVAs are more predictive than clicks or form fills because most serious buyers never fill in a form early; 6sense found 94% of buying groups rank their shortlist before contacting any seller.

How do you score accounts using intent data?
Combine fit, behaviour and multipliers. In my model, fit is worth up to 50 points, behavioural HVAs up to 30, and recency and breadth multipliers up to 20. Review the weights against closed-won and closed-lost deals every quarter.

Is IP-based company identification reliable?
On its own, often not. Hybrid working scattered buyers across home networks, Apple’s iOS 27 removed a layer of corroborating identity on Safari, and AI agents now browse vendor sites from data centres. FunnelFuel’s Reference Index records IP agreement rates as low as 3% on some enterprise accounts. Use IP as one input alongside other signals.

What is the 95-5 rule in B2B marketing?
The 95-5 rule is the LinkedIn B2B Institute’s heuristic, based on Ehrenberg-Bass research by Professor John Dawes, that only around 5% of a category’s buyers are in market at any time. The other 95% will buy later, which is why always-on presence matters alongside intent targeting.

Does Apple’s iOS 27 affect B2B intent data?
Indirectly. iOS 27 blocks third-party identity resolution infrastructure in Safari and WebKit, which weakens cookie and email-based matching on Apple devices. Third-party topic intent is largely IP-resolved and less exposed; first-party resolution on your own site leans more on server-side methods and account graphs as a result.

Should an AI agent visiting my website count as intent?
Count it, but separately. An agent reading your pricing or security pages means someone at an account delegated research to it, which is a signal. It is not a human visit: it will not resolve by IP, has no measurable attention, and inflates repeat-visit counts if mixed in. Treat agent sessions as their own class.

How accurate is B2B intent data, and is it worth buying?
Intent data is a probability, not a fact: a surge means an account’s research on a topic is running above its own baseline, not that a named buyer is ready to talk. It is still worth buying as the earliest offsite signal you can get, and it works best combined with first-party signals (HVAs, committee breadth) rather than as a lead list. Most “it doesn’t work” stories are an operating-model failure, not a data failure. See B2B Intent Data: Why Most Teams Use It Wrong and Intent Data Needs an Expiry Date.

The question I would put to the comments

If third-party intent is getting earlier and thinner at the same time, is a surge still worth paying for on its own in 2027, or has it become a trigger whose entire value depends on the first-party model behind it? I think the second. Tell me where that is wrong.

Are you buying intent data and still not sure what it did for pipeline last quarter? FunnelFuel works three ways: a full managed service where we run account-level programmatic and the measurement behind it; a licensed signal product if you want the account graph and HVAs inside your own stack; or a bespoke fit for teams that run media in-house and need the intelligence layer. Connect with me on LinkedIn, message me here on Substack, or reply to this email (mike@funnelfuel.io). No pitch, no obligation. If I can tell you what a surge should trigger in your stack in fifteen minutes, I will.

Sources

• G2, “The Answer Economy: How AI Search Is Rewiring B2B Software Buying”, 15 April 2026 (March 2026 survey, n=1,076). learn.g2.com/g2-2026-ai-search-insight-report

• Forrester, “The State Of Business Buying, 2026”, press release 21 January 2026. forrester.com/press-newsroom/forrester-2026-the-state-of-business-buying/

• 6sense, “2025 B2B Buyer Experience Report”, 12 November 2025 (nearly 4,000 buyers). 6sense.com/report/buyer-experience/

• Gartner, B2B buying journey research (17% of journey spent with suppliers).

• Chartbeat data reported by Axios, “Small publishers hit hardest by search traffic declines”, 17 March 2026. axios.com/2026/03/17/chartbeat-search-traffic-ai-chatbots

• Chartbeat 2026 Publisher Playbook, reported by Search Engine Journal, 24 September 2026. searchenginejournal.com/google-search-decline-publishers-chartbeat/590681/

• WebKit, “Blocking Identity Resolution Infrastructure”, 19 October 2026 (Wilander, Wolfe, Finkel). webkit.org/blog

• AdExchanger, “Apple’s latest operating system blocks The Trade Desk from serving ads on Safari”, 29 September 2026. adexchanger.com/platforms/apples-latest-operating-system-blocks-the-trade-desk-from-serving-ads-on-safari

• Cloudflare Radar bot-traffic crossover, announced by Matthew Prince 3 June 2026; reported by Forbes (4 June 2026) and Fortune (23 July 2026). HUMAN Security, “2026 State of AI Traffic & Cyberthreat Benchmark Report”, March 2026.

• Forrester, “The Forrester Wave: Intent Data Providers For B2B, Q1 2025”, 27 February 2025. forrester.com/report/the-forrester-wave-tm-intent-data-providers-for-b2b-q1-2025/RES182002

• Bombora, Forrester Wave Leader announcement, 27 February 2025. bombora.com/company_press/bombora-recognized-as-a-leader-among-b2b-intent-data-providers-by-independent-research-firm/

• LinkedIn B2B Institute, “How B2B Brands Grow” (Dawes, Ehrenberg-Bass), 2021.

• FunnelFuel Reference Index and delivery data, 2024 to 2026.

I’m Mike Harty. I’ve worked in adtech since 2011: I built PowerLinks, and I’m now co-founder of FunnelFuel, a B2B programmatic and analytics business. The B2B Stack is where I write up what I see on live campaigns, without the vendor spin.

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Most B2B marketing still gets built on a drawing of a funnel.

I’m sure you have all seen what I am talking about. Awareness at the top, consideration in the middle, a tidy decision at the bottom, with one buyer walking down the stairs in an orderly fashion. Everyone in this industry knows the picture is wrong yet I do not see much evidence of B2B marketers changing what they build because of it, and I include my own campaign structures since 2011 in that.

The gap became impossible to ignore this week, not because of an analyst report, but because of a comment thread. A reader called Ayse Guvencer left a response on the CIO piece that was sharper than most of the paid research I’ve read this year, and it turned into a proper back and forth in the replies. What I didn’t expect, when I went looking for the data behind what she said, was that nearly every number she’d quoted from memory turned out to be conservative. This is that argument, attempting to be written properly, with the research she was remembering substantiated and stitched back in, the places I pushed back, and the mechanics of programmatic itself, which explain why this is structural rather than a failure of effort.

What you’ll learn in this article:

• Why “signalling” your way to demand is a category error, not a hard problem: the Day 1 list research, the 95:5 rule, and what a DSP’s optimisation loop actually does with an ABM segment

• What the committee really looks like across ~22 people, and why half its influence sits outside your addressable audience

• Why deals die to fear of messing up (FOMU is kinda the anti-FOMO) rather than to the wrong vendor, with the one dataset that finally measured it

• Why the dark funnel is a structural blind spot in what programmatic can observe, not a match-rate problem

• Why ICP by title and industry breaks against a company’s actual circumstances, and why quarterly-refreshed firmographics make it worse

• The case for brand as the only lever that reaches the whole committee, the correct B2B spend ratio (it isn’t 60:40), and what treating brand as CapEx would require of a CMO on a four-year clock

Plus, in Field Notes at the end: three pieces of research published in the last three weeks that land on exactly this argument.

You can’t signal your way onto the Day 1 list

Ayse’s opening point was blunt. ABM created the delusion that you can signal your way to the buyer. As nice as the concept is, the reality is you can’t. When an enterprise account starts engaging, it already arrived with a first-day consideration set, built from the category before your outbound ever reached it. Bombarding an account with touches doesn’t create demand, it gets noticed by people who were already going to buy something in this space.

The research on this is older than most ABM platforms and more damning than the vendors admit. Bain and Google surveyed 1,208 B2B buyers and found that 80 to 90 percent of them have a list of vendors in mind on day one, before any formal evaluation starts, and that roughly 90 percent of the time the eventual purchase comes off that list.¹ To me the takeaway is clear, our job has to be to build the awareness and trust to take a seat on the day one list, and not to get pre-occupied chasing too much signal in order to sniper a demand gen opp. This stuff is hardcore brand building work.

Bain’s newer work with LinkedIn, published this year, has turned that into a metric it calls Likelihood to Buy, and the first component of the score is simply whether you were on the list at all. If you weren’t, the rest of the model doesn’t bother with you. Layer on the 95:5 rule from the Ehrenberg-Bass Institute, that in any given quarter around 95 percent of your category is out of market, and the arithmetic of “find the in-market accounts and hit them hard” starts to look like what it is: a strategy for being fourth on a list of three.²

I don’t actually think this removes merit from using sensors like High Value Actions and account level visits, alongside intent in its broader sense, to run priortiy over accounts and to focus bidding into in-motion accounts. However, the bigger lesson is to not neglect the 95% who aren’t lighting up signal based dashboards.

Most ABM machinery, the intent data, the scoring models, the orchestration layers, was built to answer “who is in market” with more precision. Almost none of it was built to answer the harder question underneath, which is who gets included in that Day 1 list in the first place. And here’s the part that should worry anyone running paid media against these segments: a DSP’s optimisation loop doesn’t know the difference. Feed it an ABM audience and a conversion goal (assuming one can be captured in a transactional/commerce B2B sense) and it will spend the budget finding the accounts inside that list who were always going to buy, because that’s the shortest path to the number you told it to hit. The bid shading, the pacing, the lookalike expansion, every mechanism in the loop is tuned to find the cheapest conversion, and the cheapest conversion is always the one that was going to happen anyway. It looks like the campaign worked. What actually happened is you paid to confirm a decision made somewhere you weren’t measuring. Precision on a target account you’ve already lost the coin flip for isn’t precision, it’s expensive confirmation, and the reporting dashboard will never tell you which one you got.

The committee is 22, and half its influence is outside your audience

Once you count the economic buyer, the champion, direct and indirect users, legal, data privacy, InfoSec and procurement, you come out with ~22 people in the average buying group. My first reaction was that number likely hedged “towards the high end of what the research firms have measured”. I was wrong to hedge it. Forrester’s most recent State of Business Buying research, built on its 2025 Buyers’ Journey Survey, puts the average complex purchase at 13 internal stakeholders plus 9 external participants: analysts, consultants, peers, references, the people in the private Slack. That is 22.³ Gartner’s number for the internal group sits at 11, up from 7 in 2017, and its May 2025 survey found that 74 percent of buying teams show what it politely calls unhealthy conflict during the decision.⁴ Forrester’s 2024 edition found 86 percent of purchases stall at some point and 89 percent cross two or more departments.⁵

So to summerise; for all the evolution in buying journeys enabled with zero search and LLMs; the output is growing committee sizes, who are doing what humans do when they form big groups whilst making decisions in a state of fear (the often under-appreciated default position in a lot of buying processes - the fear of messing up and losing jobs). They bicker and disagree and often cases, agreement is never found and the process ends painfully with ‘no decision’

The number matters less than the changing shape. The assumption baked into most funnel models is that this group enters the journey together, at the same stage, moved by the same content, and that a lead score attached to one of them tells you something about the other 21. It doesn’t. They arrive at different times, from different starting points, carrying different reasons to say no.

Then there’s the finding I think should reorganise how every B2B media plan gets sized. Bain and LinkedIn split the committee into target buyers, the users and product owners every campaign is built around, and what they call hidden buyers: procurement, finance, IT, the fiduciaries whose job is to protect the company from a bad decision.

Hidden buyers hold half the influence over whether you make the Day 1 list.⁶ Half! And by construction they are the people least likely to fill in a form, click a retargeted ad, or show up in an intent feed, because they aren’t researching the category. They’re waiting to be asked whether the category is safe.

This is where I want to name something. In the CIO piece I called the people inside the account who do the deciding without carrying the title the shadow buying group. The ghost committee is the wider ring: the share of those 22 that no identity graph, no DSP and no CRM has ever resolved, because they emit nothing your stack is built to catch. The shadow buying group is a targeting problem, and signal can help with it. The ghost committee is a reach problem, and signal can’t.

There is no funnel, just a committee of people who have mostly never heard of you. A growing share of that committee now starts its research by asking an AI chat for a shortlist, and you cannot retarget your way onto that list after the fact.

The Committee Query Map, the citation audit and the paid-media work that feeds those answers are in The Advanced B2B AI Discovery Playbook. Get the playbook.

Deals die to FOMU, and someone finally measured it

FOMU, fear of messing up. Not fear of the wrong vendor, fear of being the person whose name is on the decision when it goes wrong. Sounds opaque and perhaps over-stated yet 70 percent of deals die here.

Read that against a 22-person committee with half its influence sitting in procurement and finance and FOMU stops being a strange soundbite. A single champion cannot absorb the risk of a room that size on their own, so the deal stalls until enough of the room feels covered. Gong’s analysis of 1.8 million opportunities found multi-threaded deals over $50,000 win at more than double the rate of single-threaded ones, and that closed-won deals carry about two-thirds more buyer contacts than closed-lost.⁸ Nobody in that room is being moved by a whitepaper at that point. They’re being protected, or they’re not.

Which is also why “nobody ever got fired for buying IBM” has outlived IBM’s relevance in half the categories it once dominated. The line traces back to 1970s enterprise sales culture, and it was never about the product.⁹ It was about IBM being the safe name in a room full of people trying not to be blamed. I still roll the phrase out and I wasn’t alive when it resonated hardest. Every category has one, and it’s rarely the vendor with the best intent-data match rate.

The dark funnel is a structural blind spot, not a data problem

There’s a second layer here that the industry still talks about as if it were a measurement gap waiting for better tooling.

CaliberMind’s 2026 attribution report, published last month, put roughly 81 percent of the B2B customer journey outside anything the CRM can track.¹¹ If your measurement only lights up once someone touches your website, you’re measuring the last stage of a process and calling it the whole thing. My own research, which involved pointing Claude at over 1 billion vendor web sessions over 3 years showed me the evidence; 3 years ago we (the Funnelfuel B2B web analytics) picked up first-account touches on higher order content like blogs and broad comparison pieces. Today we don’t, we pick them up much much deeper - often seeing first touches on pricing pages which is about the exact point where the LLM’s stop helping you in a B2B buying journey, coincidentally or not.

This is where the account graph conversation gets hard rather than merely fashionable to complain about. An identity graph resolves a device or a hashed email to a company. It has never once resolved a private Slack conversation between a champion and a sceptical CISO who is never going to click a retargeted ad, because that CISO isn’t in the addressable audience to begin with. No amount of match-rate improvement closes that gap, because the gap isn’t a data-quality problem. It’s a limit coming from the client or their agency on what gets input into the programmatic system, and resultantly by design, what it can observe, traget and measure. The bid request carries a device, a context and, if you’re lucky, a resolved account. It does not carry the nine external participants, and it never will.

Same job title, completely different buyer

Here’s where the ICP conversation usually goes wrong. A VP of Marketing at a Series B company isn’t a persona, it’s a shopping list that depends entirely on circumstance. One just closed a raise and is building a growth function from nothing. Another is twelve months past the raise, the product has stalled, and they’re now the person who has to explain why the last twelve months of spend didn’t move the number. Same title. Same industry. Same seniority. Different trigger, different pain, different willingness to take a meeting. This is before you get into how different the responsibilities behind one job title can be between companies.

Firmographic and title-based targeting treats those two people as interchangeable, and there’s a practitioner detail that rarely makes the deck: most account graphs and firmographic databases refresh company-level attributes on a quarterly cycle at best, and funding stage or headcount tier is exactly the field that goes stale fastest, because it changes the moment a company raises, stalls or restructures. The cheque lands and the cash starts draining, and the attitude on day one with a full bank account can be different just 3-6 months later - been there myself and seen it first hand. Therefore, you aren’t just targeting the wrong persona, you’re targeting a snapshot of a company that may no longer be true by the time the DSP fires the impression.

I wrote about the older, deeper version of this problem yesterday, the classification debt sitting under every industry code in your target account list, and the two compound: a stale category applied to a stale snapshot. This is a different failure mode to the demand-capture problem above and needs a different fix: not better matching, but data triggered by the event itself (a funding announcement, a hiring freeze, a leadership change) rather than refreshed on a calendar.

Put the pieces together, no signalling your way onto the Day 1 list, a committee of 22 with half its influence in the ghost ring, deals dying to fear rather than to the competitor, and an ICP that’s a shifting set of circumstances rather than a job title, and you arrive at an eye-opening [for those living in textbooks] conclusion - there is no funnel. What we’re looking at is open-ended, non-linear, probabilistic and time-lagged, and we should stop building measurement systems and content calendars as if it were anything else.

The only lever that reaches the whole committee, and the number isn’t 60:40

My instinct, and I said this in the thread on my CIO piece, is that all of it points hard at brand saliency.

If the committee is this large and this fragmented, and a meaningful share of the journey happens in a dark funnel you can’t instrument, then demand-capture tactics can only ever reach the people who’ve already surfaced. A strong brand is one of the only things that can touch the ghost committee at once, including the nine people who never engage with a single asset you produce, because reach media (video, CTV, audio, high-frequency display bought for coverage rather than click-through) is the one media type an account graph can size against total addressable roles rather than just the addressable ones with a cookie or a form fill. It’s also, I’d argue, the only real answer to FOMU. Nobody gets fired for buying the category leader, and the category leader got there by being familiar to the hidden buyers long before the deal opened.

I have previously called this the Xerox strategy - and dived into it deeply here

I want to be careful here, because this is the argument marketers have made for decades to justify spend nobody can attribute, and I’d rather give you the honest number. The famous Binet and Field split, roughly 60 percent of budget to long-term brand building and 40 percent to short-term activation, comes from their analysis of nearly a thousand IPA effectiveness cases, most of them consumer.¹² When they reran the work for the LinkedIn B2B Institute in 2019, the B2B optimum came out at 46 percent brand and 54 percent activation.¹³ B2B needs more activation than consumer categories do, and then shows that brands running almost no brand investment at all, which the same LinkedIn survey found was most of them, are a long way off even that. The same research found brand effects take at least six months to show up in the numbers. This long cycle reflects the time for procurement to start, the day one consideration set to be jotted down and the prompting to begin, which is where the next problem starts.

The tenure trap, and why brand keeps losing the internal argument

Marketing didn’t end up obsessed with promotion by accident, it got there because of incentives. CMO tenure is short, and you cannot build a multi-year brand case, let alone prove it, inside a short tenure. The soundbite version is “12 to 24 months”, but Spencer Stuart’s latest tenure study, which now tracks S&P 500 companies rather than the Fortune 500, puts average CMO tenure at 4.1 years against 5 years for the C-suite as a whole, the second-shortest seat at the table.¹⁴ That isn’t a two-year job. But almost nobody running B2B SaaS marketing works at an S&P 500 company. The fractional-CMO industry’s own data, which is directional rather than peer-reviewed, puts full-time SaaS CMO tenure at around 25 months, with growth-stage tenures frequently shorter.¹⁵ The 12-to-24-month reality is a B2B tech and growth-stage phenomenon, not a universal one, and it’s exactly the segment most readers of this newsletter sit inside. Put a six-month lag on brand effects against a 25-month tenure and you’ve explained the whole thing without needing a villain:

The deeper problem is financial literacy, or the lack of it, combined with a weak grip on causal measurement. Marketing at its best is a multiplier. It amplifies every other function in the business rather than producing revenue on its own in a clean, attributable line because it is not marketings job to close orders. The small number of CMOs who get this frame marketing as safeguarding an asset, tangible and intangible, which is a CapEx mindset rather than an on-off switch. That’s a real, falsifiable position to put in front of a CFO: a 46:54 benchmark, a six-month minimum before you judge it, a share-of-voice target, and a counterfactual measurement design over a time-lagged system, instead of demanding a same-quarter ROAS figure from something that was never built to produce one on that timeline.

You can’t protect a brand budget if the function running it treats itself as disposable the moment margins tighten. The CFO conversation is really a request for a different kind of contract, one where brand gets underwritten across several quarters against a proper benchmark and measurement framework, rather than getting cut first every time the board asks a hard question.

Where this leaves us

None of this is comfortable, because it removes the easy playbook. There’s no template for signalling your way into a committee of 22, no persona deck that survives contact with a Series B company’s actual circumstances, and no brand campaign that produces a next-quarter number a nervous CMO can defend. What’s left is harder and more honest: build for saliency across the whole committee, including the nine people your tracking stack has never once resolved; treat brand as an asset with a multi-year case backed by thirty years of IPA data rather than a lever you flip; and go find the people in the business who are actually willing to underwrite that case on those terms.

That last part is the real constraint. It was never really a marketing problem. It’s a governance and finance problem wearing a marketing team’s clothes.

If you sit closer to the CFO side of that conversation than the CMO side, I’d like to hear how you’d actually build the case for a multi-year brand investment inside a business that reviews budget every quarter. What would you need to see to underwrite it, and what would make you say no?

Field Notes

Three pieces of research in the last three weeks that land on this argument, with the practitioner read.

LinkedIn and Factors.ai, 24 September. LinkedIn published research claiming B2B buyers start researching around 124 days before they speak to a seller, and that vendors reaching more of the buying committee early win measurably more. It’s a LinkedIn Ads sales document and should be read as one, but the 124-day figure is the Day 1 list problem with a clock on it. Four months of category formation before a single seller conversation is four months in which the only thing you can buy is familiarity, and most B2B media plans have no line item for it.

CaliberMind’s 2026 State of Marketing Attribution, 24 September. Scott Brinker and Frans Riemersma’s report puts around 81 percent of the B2B journey outside CRM tracking. The useful part isn’t the headline, it’s the implication for how you read your own attribution. If four-fifths of the journey is dark, a model that assigns full credit across the fifth it can see isn’t measuring influence, it’s measuring surface area. Treat it as a map of where your tracking works, not a map of what worked.

G2’s 2026 Buyer Behavior Report. Just over half of B2B software buyers now start vendor research in an AI chatbot rather than a search engine. That is a new wing of the dark funnel opening in real time, and it sharpens the brand argument rather than weakening it: a model answering “who should I consider for X” is drawing on the same category familiarity a human buyer draws on when they write a Day 1 list, and it has never clicked a retargeted ad either.

Sources and further reading

1. Bain & Company and Google, B2B buyer survey of 1,208 US professionals: 80 to 90 percent of buyers hold a Day 1 vendor list; around 90 percent purchase from it. See Bain, “Raising the Odds on a Deal: How Likelihood to Buy Rewires B2B Growth” (2026) for the follow-on metric.

2. Ehrenberg-Bass Institute and the LinkedIn B2B Institute, the 95:5 rule (2021): roughly 95 percent of category buyers are out of market in any given period.

3. Forrester, The State of Business Buying 2026, drawing on the 2025 Buyers’ Journey Survey: 13 internal stakeholders and 9 external participants per complex purchase.

4. Gartner B2B buying research (2024): average enterprise buying group of 11, up from 7 in 2017; Gartner survey, May 2025, n=632: 74 percent of buying teams report unhealthy conflict.

5. Forrester, The State of Business Buying 2024 (press release, 4 December 2024): 13 internal stakeholders on average, 89 percent of purchases involving two or more departments, 86 percent of purchases stalling.

6. Bain & Company and LinkedIn, buying group research on “buyability” (2026): hidden buyers hold half the influence over Day 1 list inclusion.

7. Dixon, M. and McKenna, T., The JOLT Effect (2022), analysis of 2.5 million recorded sales conversations: 40 to 60 percent of qualified pipeline lost to no decision; 56 percent of those attributable to indecision rather than status-quo preference.

8. Gong Labs (2025), analysis of 1.8 million new-business opportunities closed in 2024: multi-threaded deals over $50,000 win at roughly 2.3 times the single-threaded rate; closed-won deals carry about 67 percent more buyer contacts. The widely quoted “6x” figure has no traceable primary source, so I haven’t used it.

9. “Nobody ever got fired for buying IBM” is generally traced to 1970s enterprise procurement culture; no single documented originator.

10. 6sense coined “dark funnel”; Chris Walker, Refine Labs, is widely credited with popularising it.

11. CaliberMind, 2026 State of Marketing Attribution Report, Scott Brinker and Frans Riemersma, 24 September 2026.

12. Binet, L. and Field, P., The Long and the Short of It (IPA, 2013), based on nearly 1,000 IPA Effectiveness Awards cases.

13. Binet, L. and Field, P., The 5 Principles of Growth in B2B Marketing (LinkedIn B2B Institute, 2019): optimum B2B split of 46 percent brand to 54 percent activation; brand effects take at least six months to appear.

14. Spencer Stuart, CMO Tenure Study (January 2026 edition, S&P 500): 4.1 years average versus 5 years for the C-suite; prior edition (Fortune 500, 2024 data) was 4.3 years.

15. Fractional-CMO industry estimates (GrowTal, Behind the CMO): roughly 25 months for full-time SaaS CMOs. Industry estimates, not peer-reviewed.

Underneath - and arguably propping up a lot of the above - and its precision sits a filter almost nobody questions, the SIC or NAICS code that decides whether a company even makes it onto the list in the first place. The SIC filter that brands are using to find new prospects and create TAL-lookalikes. The filter used to validate ICP match against the TAL. That filter is 89 years old. It was built for an economy that still ran on assembly lines and freight yards, and it is still deciding which accounts your creative, your budget and your sales team ever get introduced to.

Precision downstream of a blunt filter is still blunt. You can build the most sophisticated account graph in the category and still be feeding it a target account list assembled by a code invented for a different century.

What you’ll learn in this issue:

• Why SIC and NAICS codes still gatekeep most B2B target account lists, and why that’s ageing badly

• What I’m calling classification debt, and why it compounds quietly on every list nobody revisits

• The real, verified SIC codes behind Dell, Microsoft and FunnelFuel itself, and what each one misses

• Why indexing a brand’s own website beats waiting for a better government filing

• How that indexing turns into a reverse account-based marketing programme, without manual tagging

• Why context is the cookieless targeting lever most competitors aren’t using yet

• What tonality and phrasing in a podcast or video reveal about a buying committee, and how to act on it before they fill in a form

A filing system built for factories is still deciding your target account list

The Standard Industrial Classification code was built in 1937 by an interdepartmental committee in Washington trying to make government statistics comparable across agencies. The very name, with industrial in it, speaks to an older age and the use case has absolutely nothing to do with accurate advertising in 2026.

It got its first real list of industries in 1938, extended to non-manufacturing activity in 1939, and was last substantially revised in 1987. The UK adopted its own version in 1948, and Companies House still runs on it today, on SIC 2007, with a 2026 update from the ONS now working its way through the system. NAICS replaced SIC for official US government statistics in 1997, jointly developed with Canada and Mexico specifically because SIC had aged out of a service-and-technology economy.

None of that killed SIC off. Dun & Bradstreet still assigns it. Every major ABM and intent platform, ZoomInfo, 6sense, Demandbase, still lets you build a target account list by filtering on it, because it’s the one firmographic key everyone’s data happens to share. A code drafted for an economy of foundries and freight yards is still, in 2026, deciding whether a company makes your buying committee’s radar.

Call it classification debt: the gap between what a SIC or NAICS code says a company does and what it actually sells, compounding quietly every year nobody revisits the filing.

Three real filings, zero nuance

Pull three real filings and the gap stops being theoretical.

Dell’s SIC code, per its SEC and Dun & Bradstreet classification, is 3571, “Electronic Computers.” Today Dell sells hybrid cloud infrastructure, edge computing and AI servers to hyperscalers, alongside enterprise services that have nothing to do with a desktop box.

Microsoft’s own SEC EDGAR filings still carry SIC 7372, “Services, Prepackaged Software.” Azure, Copilot, LinkedIn, Xbox and Surface hardware sit underneath a filing bucket built for shrink-wrapped floppy disks.

And I’ll declare the closest thing to a conflict of interest this newsletter carries: FunnelFuel’s own SIC code, filed at UK Companies House, is 62090, “Other information technology service activities.” It’s the classification equivalent of a shrug. It says nothing about programmatic media, nothing about a 75 million company account graph, nothing about contextual or identity data. Filter a prospect list by SIC code looking for a B2B advertising infrastructure business and you will not find us. You’ll find us filed next to a company that fixes printers.

The fix isn’t a better code. It’s indexing the thing itself

Waiting for a better filing system is not a strategy. Governments revise these things on a decade-plus cycle, and even the platforms that still lean on SIC and NAICS as a firmographic key aren’t about to rebuild their schema around a code that’s barely a year old.

And frankly, the technologist in me has absolutely zero interest in sitting around waiting for the government to fix something, and least of all when their fix will have no consideration for the accuracy of paid digital B2B advertising. This needs fixing differently

The fix that actually works right now is indexing the thing itself: a company’s own website, read directly, in its own language, rather than through a filing that’s years, sometimes decades, out of date. And then re-reading it, re-indexing it and re-analysing it periodically because companies evolve, and launch new solutions.

Done properly, that means aligning taxonomy and keywords to the precise nuance of what a brand’s product lines actually do, then classifying that against the pages themselves rather than a static four or five digit bucket. It’s self-updating in a way no filing has ever been. The site changes when the business changes; the SIC code changes when someone remembers to file a confirmation statement, which in most companies is never.

This is the exact problem FunnelFuel has been building against with its alignment with Contxt Media’s contextual URL-classification technology. Rather than reading a SIC filing that’s decades out of date, you read the pages a company actually publishes, updated every time the site changes rather than every time a director files paperwork. It’s the difference between asking a company what it filed in 1994 and asking it what it shipped last quarter.

This sort of contextual understanding opens up an array of wider use cases which I’ll explore below.

Turning your own website into a reverse account-based marketing programme

Once taxonomy and keywords are properly aligned to a brand’s product-line nuance, the same classification layer can run in the opposite direction. Instead of only indexing your own site, you index the wider internet for pages that align to each specific product nuance, not the brand in general, the exact feature, the exact use case, the exact language a buyer would recognise.

Bid to win every opportunity you get on those precisely aligned pages. The account graph then does the second half of the job: revealing which named companies, and which named contacts inside them, actually saw those placements. That’s a product-level account-based marketing programme built automatically, aligning high-value actions and on-site behavioural intent against specific product lines algorithmically, without a person manually tagging a single page.

Context is the cookieless lever most competitors are sleeping on

Alignment has always been one of the most reliable levers in digital advertising: the right message, to the right person, in the right context. What’s changed is that context now does that job without needing a cookie or an identifier to get there. You’re buying against the page itself, which means you can win impressions your competitors are sleeping on, because they’ve built their entire targeting stack around identity and have nothing left to bid with once an identifier goes missing from the auction.

Context was cookieless before cookieless was a category anyone had to plan around.

Let’s not sleep on the reality of digital advertising and identity in 2026 and beyond. The blunt reality is not every impression is going to carry useful identifiers, and we have two options:

1. We build our entire targeting strategy around identity alone and miss out on upwards of 70% of available impressions.

2. We take the smart approach, which is blending cookieless targeting capabilities like contextual alongside identity in order to fill in the missing jigsaw puzzles and open up deeper addressability on the open internet.

What tonality tells you about a buying committee

Contextual technology is also starting to read tonality, inference and phrasing well enough to tell whether a piece of audio or video is talking about a brand favourably or not. That’s a new input for scoring models at both the persona and account level, not just a trending topic.

Picture the scenario directly. FunnelFuel targets advertising agencies as well as brands. Say a procurement-side decision maker at an agency like WaveMaker sits inside one of my priority buying groups, and twenty-seven minutes into a podcast they’re listening to, a guest says FunnelFuel is a no-brainer to be looking at, one of the reasons the vendors already winning in this category route their programmatic spend through us. That’s a positive signal worth feeding straight into that account’s score. A negative mention is exactly as useful, just pointed the other way.

In B2C, that signal barely matters. Lose a handful of listeners to a lukewarm mention and you shrug it off against a total addressable market in the millions. In B2B, a target account list might be a couple of dozen companies, or a few hundred, or a few thousand at the outside. If one of those specific accounts has just been exposed to something unflattering about your brand or your category, that’s worth fighting, not absorbing.

Say the mention is that FunnelFuel is expensive. Fine, that’s the account to serve creative that meets the objection directly rather than pretending it wasn’t said: yes, the CPM is higher, because the account graph underneath it is built by stacking scores of data partners who all need paying, rather than trusting one vendor’s guess. That’s the difference between spray and pray, where “B2B audience” quietly turns into an impression next to a potty-training app, and paying a few dollars more in CPM for something closer to deterministic. I’m comfortable being the expensive option in that specific comparison. This is an objection I can handle, but it only needs handling when you're aware of the objection and the fact a prospect has been exposed to it. This is the power of contextual in 2026

The manifesto, shortened

Classification debt is real, and it’s compounding on every target account list built from SIC or NAICS alone. Fixing it isn’t about waiting for a better government code, committees move slower than product roadmaps ever will. It’s about indexing the thing itself: a brand’s own site, a prospect’s own site, the actual pages people read, watch and listen to. That gives you a live, self-updating account graph instead of a filing that’s decades behind, a cookieless targeting lever most competitors aren’t using, and a sentiment signal you can act on before a buying committee ever fills in a form.

A code from 1937 was never going to describe a company that didn’t exist in 1937. Context was always going to have to do that job instead.

If you’re weighing how much classification debt your own target account list is carrying, or you want a second opinion on what your specific SIC or NAICS filter is quietly missing, reply to this email and tell me the code. I read every reply myself.

If you’ve got a colleague on a growth or demand gen team who’s still filtering target account lists by SIC code alone, send this their way, the share button’s right there.

It won't.

Last-touch attribution isn't an incomplete picture of a B2B deal. It's the wrong shape of picture entirely, built for a buyer who doesn't exist in B2B and never did. For all the areas that B2B diverges from its B2C cousin, attribution is quite possibly the greatest of them all

What you'll learn in this piece:

• Why last-touch survived so long after it stopped fitting the buyer it was measuring

• The specific mechanical reason a buying committee breaks single-touch credit, not just a statistical one

• Why the dark funnel is the other half of the model, not a mystery outside it

• What actually changes when you move from attribution to outcome modelling

• A working framework for what good outcome modelling looks like at account level

Why last-touch survived this long

Last-touch attribution was built for e-commerce: one buyer, one session, one decision, made in a window short enough that the last thing they clicked plausibly was the thing that swung it. In e-com it can stand true, and arguably the extension of the old adage of adtech keeping the web free since ninety-three extends deeper to commerce keeping the web free, because the transactional ad ecosystem is what has always driven digital advertising and it’s where B2B has always been left behind

Last touch survived the jump into B2B not because anyone tested whether it fit, but because it was already sitting in the analytics stack when B2B marketing started needing to report a number to finance. It was the inherited standard which needed retrofitting or bludgeoning into a board pack

That's the honest history. It wasn't chosen for B2B. It was inherited.

The problem is structural, not a measurement gap you close by adding more tracking. A single buyer clicking through a funnel and a buying committee working through a purchase are different objects entirely. You cannot bolt more instrumentation onto a single-buyer model and get a committee-shaped answer out the other end. The model is built to join a single persons dots not to join those alongside their fellow buying committees

The buying committee breaks the maths, not just the story

Forrester's State of Business Buying research puts the average B2B purchase at around thirteen people involved, including roughly nine external influencers most vendors never see directly. Gartner's figure for the core internal buying group, typically six to ten stakeholders spanning procurement, finance, technical evaluation and end users, tells a consistent story from a different angle: these people rarely speak to each other directly, let alone convert in the same session.

The rapidly expanding buying committee which is growing to include significant numbers of external stakeholders is threatening to break the whole B2B became ABM targeting model let alone attribution - because these 9 external stakeholders are almost a dark buying committee in themselves - separated from account level signal that were use to target and frankly unlikely to be in the attribution lens at all. They’re not going to hold the pen on the deal but their signal, if capturable, would be a significant piece of dark matter in the buying journey - one for another day

Last-touch attribution asks a much simpler single question: what was the last thing this person did before converting?

That question only means something when there's one person and one conversion event to anchor it to. In a buying committee, there isn't. The finance stakeholder who never clicked anything but killed the deal in a budget meeting doesn't exist in the model at all. Or conversely the same person may hold the pen when it comes to contract and they’re equally invisible. The model sees a ghost buying

The technical evaluator who saw four pieces of content over three months and became the internal champion gets collapsed into whichever touch happened to be temporally last, regardless of whether it mattered.

The model misses all kinds of intricacy that links to organisational structure, who does the work, who signs the paper and how different that is to someone choosing a new pair of jeans for themselves

This is where I'd push back on how most martech vendors sell multi-touch attribution as the fix. Honest hand-raise - when we started FunnelFuel we built this into our analytics. It was born from the idea that programmatic advertising would always be statistically underweighted in demo requests, lead captures and asset downloads, all the kinds of stuff we were tasked with helping drive.

Multi-touch still has one - the very same - Achilles heel for B2B.

Multi-touch still assumes you can see all the touches and just need a fairer way to split credit between them. Frankly a lot of it was developed to overcome Google Analytics bias towards Google channels like paid search and to help give a clearer view beyond the person shooting the fish in the barrel and trying to help understand which participants got said fish into the barrel to start with

That's a real improvement over last-touch, but it doesn't solve the deeper issue: a meaningful share of the touches that move a B2B deal never happen anywhere you can instrument.

If this is matching what you're seeing in your own pipeline data, reply to this email and tell me what your dashboard would need to show to make room for a stakeholder who never clicked anything. I read every reply, and it's usually the fastest way I find the next piece worth writing.

Dark funnel isn't the mystery, it's the missing half of the model

I've written before about the dark funnel: the research, conversations, and internal debate that happen with zero digital footprint. A peer recommendation in Slack. A conversation at a conference. An internal champion pitching a shortlist in a meeting nobody logged and sure as hell didn’t share externally.

The mistake is treating that as an unfortunate gap in an otherwise sound model. It isn't a gap. For a lot of B2B categories it's the majority of the actual decision-making, happening in parallel with, not downstream of, the touches you can see. Throwing in the ever increasing LLM impact on buying journeys and its 24% conversion rate vs search and we can see that LLMs would Invariably become the last touch. So is LLM advertising the whole demand gen game? I’d say evidently not

Attribution models built on visible touches alone aren't measuring most of a committee's decision and guessing at the rest. They're building a confident, precisely-numbered story out of the fraction that happened to leave a cookie behind, and treating the omission as noise rather than the main signal.

That reframes what "better measurement" has to mean. You're not trying to see more of the funnel. You're trying to build a model that's honest about how much of the real decision it can't see, and stops pretending the visible slice is representative of the whole.

A quick caveat, because I don't want to overstate the case: none of this means visible touches are worthless. They correlate with outcomes, they influence the invisible conversations, and a strong programmatic and content strategy visibly moves pipeline. The argument isn't that instrumented touches don't matter. It's that crediting a single one of them with causing a multi-stakeholder decision is a category error dressed up as precision.

What "upgrading" measurement actually means

Most attempts to fix this reach for the same lever: more data. A CDP, a few more integrations, an intent signal layered on top, a slightly more sophisticated weighting model across the touches you already had. That's addition within the existing frame, not a change of frame. It gives you a more detailed version of the wrong question.

The upgrade that actually matters is moving from attribution to outcome modelling, and the distinction is worth being precise about because the two get used interchangeably and shouldn't be. A new feature on an attribution tool doesn’t catapult it to an outcome model

Attribution asks: which touch gets credit for this conversion? It's forensic and backward-looking, built around a single event.

Outcome modelling asks: given everything we know about this account's engagement pattern, what's the probability this account converts, and on what timeline? It's probabilistic and forward-looking, built around an account, not an event.

That single shift changes what you optimise for. Attribution optimises the last mile, whichever channel happened to sit adjacent to a form fill.

Outcome modelling optimises account-level engagement velocity across the whole buying group, which is a genuinely different media plan, not just a different reporting dashboard bolted onto the same one.

We've had to make this shift in our own delivery work at FunnelFuel more than once, usually when a client's existing attribution setup was actively steering budget toward the channel that happened to sit last in the journey rather than the channels doing the earlier, harder work of building committee-wide awareness. The fix wasn't a new attribution tool. It was accepting that per-touch credit was the wrong unit of analysis for what they were trying to buy. If you're mid-way through that same argument internally, that's a conversation I have most weeks, drop me a line at mike@funnelfuel.io.

What good outcome modelling looks like

In practice, this means building around four things instead of one.

Account-level engagement, not lead-level conversion. The unit of measurement is the account and everyone in the buying group touching it, not any single lead's path to a form fill. A model that can't tell you three different job titles at the same company engaged with your content in the same fortnight is still measuring individuals, not committees.

Engagement breadth as a leading indicator, not a vanity metric. How many distinct people at an in-market account engaged, not how many times one person did. A single champion clicking ten times is a weaker signal than four different stakeholders each engaging once, and most attribution setups can't tell the difference between those two accounts at all.

Propensity and uplift over precise credit. Rather than asking which touch caused the conversion, ask which accounts are showing the engagement pattern that historically precedes a deal (this is where account level High Value Action tracking and scoring is golden), and which media investments measurably shift that propensity upward. This is a genuinely different modelling problem, closer to what a data science team would recognise as uplift modelling than to what a marketing attribution vendor sells.

Cohort-based revenue linkage instead of single-touch ROI. Compare pipeline and revenue outcomes across cohorts of accounts with meaningfully different exposure to a campaign, rather than trying to trace a straight line from one ad impression to one closed-won deal. It's a noisier-looking number in the short term. It's a far more honest one over a quarter.

None of this is exotic. Every piece of it exists in adtech and data science today. What's missing in most B2B marketing organisations isn't the technology, it's the willingness to stop reporting a single-touch number that finance finds reassuringly precise in favour of a probabilistic one that's actually true.

I'll say the awkward part plainly, the elephant in the room: most B2B teams keep last-touch attribution not because they believe in it, but because the alternative is harder to explain in a board deck. A precise, wrong number is more comfortable than an honest, probabilistic one.

That's a real organisational problem, not just a modelling one, and it's worth naming before you try to fix the maths underneath it.

If this is the first piece of mine you've read, subscribe below. I write one single-thesis issue a week from inside actual B2B programmatic delivery, not from the sidelines watching the industry from a content calendar.

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So here's the question I'd actually put to you: if your attribution model can't name a single stakeholder who never clicked anything but killed your last deal, what exactly is it measuring?

Share The B2B Stack

What you’ll learn in this article:

• Why a large account running brand advertising told us account-level reporting doesn’t matter to them, and why that reaction is more common than our own echo chamber likes to admit

• What “insight-first” reporting actually means in programmatic, and why it only exists because of how impression-by-impression bidding works

• The two classes of signal sitting inside your brand campaign’s exhaust: audience enrichment and environment

• Why context reads as research intent in B2B, not entertainment, and what that means for connecting brand to demand

• Why a buying process that increasingly happens inside AI chat windows makes this exhaust more valuable, not less

• What to ask your vendor for before your next brand campaign goes live

Plus, in Field Notes: a fresh, named number on how much of B2B buying is invisible to a CRM, and an honest caveat about how shaky that number actually is across the industry.

A large account said they don’t care who engaged

The meeting itself was good. Strong relationship, the agency does a lot of work with us, no complaints about the media, nothing unusual in how the campaign was proposed to run. Then the conversation turned to reporting, and I got a reaction that’s stuck with me since: “The client doesn’t care about account level reporting when they run brand campaigns”. Reach, frequency, maybe a completion rate. That was it.

It immediately struck me as quite bizarre. In truth, I was close to disbelief. A proper B2B advertiser not having any interest in which prospects had seen their ads

Then it reminded me that we can spend so long selling capabilities that we can forget to talk about the ‘so what’, the whole why would a client need this and what would they actually do with it piece.

Reach and frequency. That was the entire ceiling of what they wanted to see.

Spend long enough inside an account graph, inside an identity spine built specifically to resolve impressions back to named buying groups at named companies, and it’s easy to assume everyone buying B2B media wants that view by default. Most don’t, and the longer I’ve sat with this one, the less I think it’s an outlier account and the more I think it’s a gap between what our side of the industry has built and what most brand buyers have actually been shown is possible.

So this piece isn’t about that account, or what they do or don’t run next quarter. It’s about what account-level, insight-first reporting actually is, why it only exists in programmatic specifically, and why discarding it on a brand campaign is a considerably more expensive decision than it looks from the media-plan level.

What insight-first reporting actually is, mechanically

Start with the plumbing, because it’s easy to lose sight of once you’re a few layers up in a dashboard. To get to why this is possible, we need to understand the innate differences between programmatic advertising and other forms of digital, from spot buys, to tenancies, and other ‘block’ or segment booked activity.

Programmatic differs because it transacts impression by impression. Every single ad placement is sold of on an eBay like auction, 1 trillion times a day - its an internet scale auction infrastructure where each placement is valued by each bidder, taking factors like time of day, host environment (e.g. which website or app is the ad on), visibility of the placement, device and many other such factors.

That granularity is gross overkill for most of what it gets used for, and it’s also the entire reason the data exhaust exists at all. Every bid, every win, every served impression leaves a line-level record. A direct buy or an upfront insertion order never produces that. It’s a function of the mechanism, not a feature someone bolted on.

Insight-first reporting is the impression-by-impression bid record a campaign already produces, showing which accounts engaged and in what environment. It isn’t a dashboard feature added afterwards. It’s the record itself, left there whether anyone chooses to look at it or not.

Pair that line-level record with an account graph, and the exhaust stops being an anonymous impression count and becomes a resolvable view of which accounts, and which buying groups within those accounts, are showing up against your targeting. That’s the whole premise behind insight-first reporting. It’s the natural by-product of buying this way. Its the exact thing my prospect said they had no interest in.

Two classes of signal hiding in your brand spend

Once you’re looking, two distinct things are worth separating, because they tell different stories and drive different insights.

The first is audience enrichment: who the person works for, what function they sit in, and whether the exhaust shows any indication they’re further along a purchase journey than a cold prospect would be. This is the layer most people associate with account-based targeting, and it gets most of the attention in vendor decks. This is intent mapped out of advertising signal.

The second gets talked about far less, and it’s arguably the more interesting one: environment. For digital out-of-home, environment means location and venue type. For connected TV, it’s daypart and show genre, and increasingly whether someone’s using the big screen for something work-adjacent rather than pure leisure viewing. For display, video, native, and in-app, environment becomes content context: the actual editorial or topic someone is engaging with alongside the ad.

Neither requires anything exotic to extract. Both come straight out of the same line-level record the bidding mechanism already produces for every single impression.

In B2B, context isn’t entertainment. It’s research.

Here’s the part of the argument I think gets underweighted because general advertising folks think B2C and not B2B.

In consumer advertising (B2C), a contextually relevant environment is a reasonable proxy for interest or mood, and not much more than that. Many consumer goods get sold with no further research needed. If I need a new bathroom cleaning spray, I am not going to spend hours researching the different brands.

In B2B, it’s very different and contextual environment gets you closer to a direct behavioural signal.

Nobody reads a dense piece on vendor architecture, or sits through a technical explainer on a category problem, for fun and pleasure. People do that because they’re trying to close a knowledge gap, get better at their job, or avoid the specific professional fear of getting a decision wrong in front of a committee that will remember it (Fear of Messing Up - FOMU). FOMU is close to the opposite of the FOMO driving a lot of consumer engagement. Call it anti-FOMO: the emotional driver is risk aversion, not excitement, and the content someone chooses to sit with reflects that. Human psychology says the fear of loss always outweighs the chance to gain. We’re tapping into powerful innate human psychology here.

So when an account shows up engaging with a relevant contextual environment, that’s not incidental. It’s a reasonable proxy for research happening, and research happening is a reasonable proxy for some stage of a buying journey underway, however early or informal. That’s the thread connecting a brand impression to a demand signal, and it only exists because programmatic’s exhaust lets you see the environment an engagement happened in, not merely that an impression was served.

Why this matters more now, not less

There’s a reason to care about this beyond general tidiness, and it’s a live one. G2’s 2026 Buyer Behavior Report put the figure at just over half of B2B software buyers now starting vendor research inside an AI chatbot more often than a search engine, a share that has moved sharply across a short window. Forrester’s own 2026 research into business buying reaches a similar conclusion: generative AI tools have become a leading influence on buyer shortlists, ahead of review sites and vendor websites. Buyers are doing more of their early thinking somewhere a vendor cannot observe at all. Fewer clicks, fewer form fills, fewer visible footprints than even two or three years ago. Some are even calling the LLMs the most important buying committee member, and in-SERP results are masking the layers of addressable signal further

When observable signal gets scarcer, the signal you do get becomes more valuable, not less. An account showing up against a relevant contextual environment inside a brand campaign is exactly the kind of breadcrumb that’s harder to come by anywhere else right now, precisely because so much of the research that used to leave a visible trail on the open web now happens inside a chat window instead. Treat that breadcrumb as reportable waste and you’re discarding one of the few remaining markers of research behaviour your buyers are still leaving in the open. Treat it properly and you’ve got the start of a nurture track connecting a brand impression to a demand motion, built entirely from data the campaign was already producing at no extra cost.

The actual cost of waving this off

Go back to that meeting I had. The account wasn’t wrong that their campaign would run fine without account-level reporting. The media gets delivered either way, the reach numbers still land, nobody downstream notices anything missing. What they were wrong about, I think, is the value of what they chose not to look at. Refusing insight-first reporting on a brand campaign isn’t a neutral simplification. It’s discarding, for free, the clearest available evidence of which accounts are quietly researching you, at the exact moment most of the rest of that evidence has gone dark.

Sometimes agencies like to avoid over-complication and creating a rod for their own back when it comes to reporting. This is valid and fair - human nature after all - but even then they should take the reporting and leverage it for demand campaigns, and create the uptick downstream on the report the client will read.

If you’re running brand programmatic today, it’s worth asking a direct question internally: does your team actually see this layer, or has somebody quietly decided it isn’t worth looking at?

What does your reporting actually tell you about who engaged, and in what context, the last time you ran a brand campaign?

Field Notes

CaliberMind’s 2026 State of Marketing Attribution Report, published 24 September 2026 by Scott Brinker and Frans Riemersma, puts roughly 81% of the B2B customer journey outside anything a CRM ever records, with the large majority of attributable revenue concentrated in a small share of journeys, data sources and tools. That’s a stark figure from two well-known, independent martech researchers, and it corroborates the argument above directly. If four-fifths of the journey is already invisible to a CRM, the sliver of it that does surface inside a brand campaign’s own exhaust is worth a great deal more scrutiny than most reporting setups currently give it.

Worth a caveat alongside the cheer: nobody in the industry agrees on the actual size of the dark funnel. Depending on which named study you read, estimates for how much of B2B buying happens outside tracked channels range from roughly a third to over four-fifths, swinging heavily on methodology and vertical. That spread is itself a useful, honest data point. No single reporting layer, including the one this piece is arguing for, closes that gap on its own. It’s one additional, verifiable slice of a considerably larger problem nobody has fully solved, not a complete fix for it.

That first part of the belief is correct. The conclusion drawn from it is not. Point the same infrastructure at an account graph and an identity spine built to resolve real companies and real committee roles, and the channel that was supposedly wrong for B2B becomes the only one that can reach a full buying committee across the open internet at scale, for anyone willing to do the building.

This piece is the case for that second half: why the gap exists, what closes it, and what it is costing the media plans that have written the channel off.

What you’ll learn in this article:

• Why programmatic’s data hierarchy stops one level too low for B2B, and what the hierarchy gap actually is

• How large the buying committee has become, and why “the account” was never the right unit to plan media against

• What the open web gives you at the individual URL level that LinkedIn structurally cannot

• Why the data coming back out of a programmatic buy is the more valuable half of the trade, and the half most buyers ignore

• Where programmatic beats the walled gardens on cost and on the committee roles LinkedIn under-indexes, and why that gap has survived a decade

What is the hierarchy gap, and why does it make programmatic look wrong for B2B?

The reasoning behind writing programmatic off isn’t unreasonable. Cookie and device graphs, individual consumer segments, one-to-many optimisation and session-based conversion windows were all designed for selling to one person at a time, so set against a sale involving a dozen stakeholders the channel looks like a segment-of-one tool bolted onto a committee-level decision.

Here is the flip. The auction infrastructure underneath, the real-time bidding, the exchange connectivity, the contextual crawling, the pacing and the optimisation, is some of the best decisioning technology in advertising. It is aimed at the wrong unit by default, and that is a very different problem from being the wrong technology.

I see the gap as a data hierarchy problem. Programmatic thrived because it targeted the end user, or really the end device as measured by a cookie footprint, and that was ideal for selling fast-moving consumer goods. What it never carried were the two levels above the device: the account, and the buying group inside it. I call the distance between those levels the hierarchy gap, and it is the whole reason the channel underperforms for B2B out of the box. The moment you can transpose account and buying group into the auction, and carry them through targeting, pacing, optimisation and reporting, you have weaponised some of the best optimisation AI in the industry to work for the buying group within an account currency. Same pipes, different channel.

How big is the committee you’re actually planning against?

Gartner’s longstanding figure put the typical buying group for a complex B2B purchase at six to ten stakeholders, up from 5.4 when CEB measured it in 2015. Forrester’s State of Business Buying, 2026, drawn from a survey of nearly 18,000 business buyers, now puts the typical decision at 13 internal stakeholders and nine external influencers, and finds the group doubles, from seven to 14, when the purchase includes generative AI features.

The window has moved as well. 6sense’s 2026 Global B2B Buyer Experience Report, published this month from a survey of 4,611 recent buyers, has buying groups spending roughly 60% of a 10.3-month cycle in the selection phase before anyone contacts a seller. By the time the first call happens the group has a favourite, and that vendor wins 82% of the time. A vendor that misses the initial shortlist has less than a one-in-ten chance of winning.

So the people a vendor needs to reach, and the window in which they can be reached, have both expanded and moved earlier, largely out of sight of the channels built around a form fill or a sales call. It also means “the account” was never the right unit to plan media against. The committee is, and a committee of thirteen spread across procurement, security, legal, technical evaluation and the executive sponsor is not a group any single channel reaches on its own.

Those six-to-twenty person clusters, which need targeting in unison rather than in isolation, are the precise gap I have spent millions of dollars engineering to close.

This is where an account graph, tying together tens of millions of businesses, their firmographics, their technology stack, their inferred size and revenue, against an identity spine that makes the individual committee member targetable, stops being a nice-to-have and becomes the precondition for doing this properly. Without it, “reach the buying committee” is a slogan. With it, it is a targeting spec, and it is the specific piece of infrastructure the B2C version of programmatic was never built to include.

What does the open web give you that LinkedIn structurally can’t?

Traditional programmatic formats, display, video and native delivered against premium content sites and apps, come with one advantage that gets massively underrated: the full URL of the page carrying the ad, both as a targetable signal going in and a reportable one coming out. That lets a business crawl and analyse the page itself, its text, its imagery, its video and audio, and understand semantically what it is actually about, not just which segment the visitor supposedly belongs to.

It is easy to fall into the fallacy of believing the whole value sits in what I call apex-fit pages, the ones where a committee member is doing visible, deep research. Those pages are rarer than the pitch decks imply, and getting rarer. SparkToro’s 2026 study found 68% of US Google searches ended without a click in the first four months of this year, up from 49% in 2019, as AI Overviews absorb the queries that used to end in a page visit. The UK figure is higher still, at 69.5%.

However, rarer is not rare, and rare does not mean never. The open web still carries an enormous footprint of contextually relevant page views. Every published estimate of daily programmatic bid volume disagrees with the next by an order of magnitude, depending on whose infrastructure is being counted, but even the conservative end dwarfs total impression volume across every walled garden combined.

That is the opening. While budget keeps piling into Facebook, Instagram, Reddit and LinkedIn, the open programmatic ecosystem remains the only channel that can, at scale and with real ad tech behind it, go and find the pages where your committee’s research is happening, upstream of the point where they know they are running a formal process at all. That is the chance to build brand memory inside the selection phase 6sense describes, to form your own view of intent rather than renting someone else’s, and to align a brand contextually with what a prospect is actually reading, whether that is a technical deep-dive or a sponsorship context, a company backing Formula One and then buying against the content a fan of the sport is reading.

If someone on your team still files programmatic under “remnant”, this is the section to forward them.

Why is the data coming back the more valuable half of the trade?

The placement is only half the trade, and it is the only half most buyers seem to value.

The depth of data returned from a programmatic buy, the raw event logs and bid-level detail sitting behind platforms like The Trade Desk, isn’t matched by anything a walled garden reports back. Tie that observable signal to the right account graph and a media buy turns into a data asset: knowledge of which named accounts, and ideally which individuals within them, engaged with which contextually aligned placements.

That is an underused opportunity in its own right, because it lets a B2B marketer build a proprietary read on intent rather than depending entirely on third-party intent providers whose methodology is, to varying degrees, a black box. Feed that signal into your own model and you can push it back out into every other channel you run: sync it against YouTube buys, sync it against social, use it to prioritise content syndication, and let your own observed behaviour, not a vendor’s inferred score, set the plan.

The same principle extends to owned-property signal. An individual mobile ID, IP address or cookie behind a visit to your own site or a partner’s, tied back to a named account and ideally a named committee member, is recognisable well before that person raises a hand. Put together, that is the 360-degree loop the B2C version of this channel was never designed to close, and that most B2B media plans still don’t.

The placement is what you paid for. The data coming back is what you were actually buying.

Where does programmatic beat the walled gardens, and where doesn’t it?

LinkedIn earns its premium. It reaches named accounts and named individuals with precision, and its reporting is good. But it is expensive, and the 2026 benchmarks show it: Metadata’s benchmark across 138 B2B advertisers’ 2025 spend averaged a $63 CPM, and tech and IT audiences commonly run $35 to $75, higher again for senior or C-suite targeting.

Open-web programmatic display trades at a fraction of that, and the data which transforms a B2C generalist impression into a B2B one is what changes the economics, not LinkedIn’s pricing structure.

Cost is one thing. The more serious problem is coverage. LinkedIn structurally under-indexes on exactly the roles that don’t spend their working day in the feed: technical evaluators, legal, procurement, security. These are people who are central to whether a deal closes, and who are considerably easier to find in a technically aligned podcast or a premium trade publication than in a LinkedIn auction built around front-of-house job titles. Forrester’s finding that procurement now sits in the decision-maker seat in more than half of buying cycles makes that coverage problem harder to ignore.

That is the blunt version of the coverage argument. LinkedIn is strong where it is strong. Programmatic, built out properly, rounds out the committee LinkedIn was never built to reach, at a materially lower cost per impression, with a deeper and more individually attributable signal coming back. Put those facts together and the case stops being a nice-to-have and becomes close to a cost-of-capital one: less waste, cheaper reach and deeper penetration into a wider committee, all at once.

Why hasn’t B2B fixed this already?

If the case is this strong, the obvious question is why B2B programmatic spend has stayed this underweight for a decade. Surely not every major vendor has missed the memo?

The answer is infrastructure, not appetite. Purpose-built B2B programmatic capability, anchored to account-level data pipelines that can target and then report and attribute all the way through to a website visit, hasn’t existed as a category until recently. Most of the ecosystem is still running the B2C version underneath a B2B media plan and calling it done. This is the specific gap we built FunnelFuel to close, and it has taken real investment, not a repackaged consumer DSP with a B2B label stuck on it, to make the targeting-to-attribution loop actually close.

It would be a mistake, though, to assume the big B2B tech vendors aren’t materially running B2B programmatic. Individual vendors we know and work with spend tens of millions of dollars a year on it. The structural miss sits outside that land of giants, in the mid-market and the long tail of enterprise that makes up a huge chunk of the B2B industry. That is also where the opportunity still stands for anyone willing to bring in the expertise, whether built in-house over time or borrowed while the programme stands up.

The position

Programmatic was built for B2C. On its own, unmodified, it is exactly the channel most marketers still think it is: cheap filler, a segment-of-one tool bolted onto a committee-level sale, easy to deprioritise next budget cycle. Rebuilt on an account graph and an identity spine that can resolve a real buying committee, the same auction gives you committee-level targeting an account list alone doesn’t, contextual reach LinkedIn structurally can’t replicate, a deeper and more attributable data return than any walled garden hands back, and a materially better cost basis for reaching the parts of the committee that never open LinkedIn.

The hierarchy gap between those two versions of the same channel is, in my view, one of the more expensive things sitting inside a lot of 2026 media plans heading into 2027. So here is the question I’d put to anyone still running programmatic as an afterthought: if your buyers’ shortlist is formed in the first six months of a ten-month cycle, before your sales team hears about it, and the channel was never built to reach that committee out of the box, what is actually in your plan to close that gap, and what is it costing you that it isn’t?

If you want to go further, Segment vs Signal is how to run programmatic against the whole committee, and Your Media Vendors Are Marking Their Own Homework is the piece on checking the data that comes back.

Is your buying committee bigger than your media plan? If this is a live problem for you, I’m happy to talk it through. FunnelFuel runs three ways: as a full managed service, as a licensed signal product inside your own stack, or as a bespoke fit around what your in-house team already has.

Three ways to reach me: connect on LinkedIn, message me on Substack, or email mike@funnelfuel.io. No pitch attached, no obligation.

It feels like precision because it’s specific. It isn’t precision. It’s arbitrary and often self constraining, and in a meaningful share of the accounts on any real target list, the title you’re searching for doesn’t exist on that org chart at all or doesn’t map into the accounts organisational structure. This approach totally disregards how organisations all set up differently; with different decision making processes and procedures, and assumes they are all the same.

Average tenure in a US role is now around 18 months, and B2B email addresses decay at roughly 25 to 30% a year. Even where the title does exist, the person holding it by the time your campaign goes live is frequently not the person your list was built against.

The key point is, we can not guess how each of our target accounts runs its procurement, who is involved at what stage and why, where job titles can diverge from common expected norms around their roles and a host of other factors. We can though so observable signal from the wider internet and infer buying groups out of behaviours to make a more scientific approach that is bespoke to each vendor.

There’s a radically better way which lets you find each companies buying groups and then target them, bringing the value of a more 1:1 ABM with the scale of 1:many

What you’ll learn in this issue:

• Why targeting fixed titles like CIO or CTO breaks the moment an account doesn’t have one

• What I’m calling the shadow buying group, and why it matters as much as the formal committee

• How device-level signal, first and third party, infers who’s actually in a buying group instead of assuming it from a title

• Why some accounts decide horizontally and others are run by one dominant voice, and how signal tells you which is which

• Why B2B’s economics, high transaction value against a low marginal cost per person, make a wider net the rational move rather than the wasteful one

• How signal-orchestrated buying groups become the account graph’s next frontier, beyond company and persona resolution

• What this actually unlocks for a paid ABM programme built to drive pipeline influence, not just impressions

Targeting job titles assumes every company is organised the same way

Job-title targeting has one structural flaw underneath it: it assumes an org chart that’s the same shape everywhere. It isn’t. Some businesses are horizontal and democratic, where a purchase like a new CRM gets input from six or eight people across ops, sales enablement and finance, none of them especially senior. Others are autocratic and founder-led, where a founder CEO carries a disproportionate share of the final call and the rest of the room is there to execute, not decide. Neither shape shows up reliably in a CRM field, and a title-first target list has no way of telling the two apart before it’s already spent budget on the wrong three people.

That’s the real cost of premeditated targeting: deciding who matters before you’ve looked at the account, rather than letting the account tell you.

This creates great wasted impressions (targeting irrelevant people) and even greater opportunity cost - the latter is significantly worse in a world of digital media where the unit cost of reaching a handful more people is low but the cost of missing them out could cost you a 7/8/9 figure deal.

What I’m calling the shadow buying group

Call it the shadow buying group: the sphere of influence operating and orbiting around the formal buying committee, made up of people who never show up on an org chart or a CRM record but who shape the decision anyway.

Every procurement process has a committee you could name if pushed, and a wider ring around it you couldn’t. The person the CFO quietly asks before signing off. The engineer whose opinion the VP always checks before committing to a vendor. None of that shows up in a lookup by title. All of it shows up in signal, if you’re reading the right layer of it.

Forester also indicates that the growing complexity behind the ever growing buying committee has extended the sphere to an average of 13 internal and 9 external consultant aids. The buying group can now extend beyond the FTE’s in a business, and the only hope we have of capturing these influential extras is to be open to letting signals shape our targeting even when they can’t reliably be connected to an account.

The CIO on your targeting list might not be the person who matters, and the people who do matter may already be asking an AI chat questions your ads never reach.

The Committee Query Map in The Advanced B2B AI Discovery Playbook is built around those roles, not around the job title in your DSP. Get the playbook.

How signal actually finds the buying group

This is the part that moves the whole discipline forward from where the original account graph argument left it. First-party signal, someone completing a high-value action on a brand’s own site, researching pricing, downloading a spec sheet, returning three times in a fortnight, self-declares that device as plausibly inside the buying group or its shadow. Third-party signal, a device reading procurement-adjacent content across the open web, or watching a specific run of YouTube videos on the category, adds topic-level and individual-level intent from outside the brand’s own walls. Stack the two together at the account level and you’re no longer looking up a title. You’re watching a buying group and its sphere of influence assemble themselves in the data, procurement process by procurement process, for something specific like a Salesforce CRM renewal rather than for the account in the abstract.

That’s the shift worth sitting with: not “find this list of accounts, then find the CIO inside each one,” but “find the buying group inside each account, built from what its members are actually reading, watching and doing, for this specific procurement process.” Persona and job title become an output of that signal, worked backward, rather than the filter applied before any signal gets read at all.

Why B2B’s economics make the wider net the rational move

In B2C, casting a wide net is expensive relative to what you get back: most of the audience will never buy, so precision earns its keep by trimming waste. I remember back around 2018 when the programmatic world was obsessed with reducing waste, and movements like viewability were driven by this agenda.

B2B inverts that maths. The value of a single won account can run into hundreds of thousands of pounds, while the marginal cost of including one more plausible buying-group member in the media plan is small change by comparison.

That asymmetry is the argument for casting the net wide enough to capture the full committee and its shadow group, rather than narrowing to two or three titles and hoping they’re still the right ones. Missing a genuine influencer costs far more than reaching one extra person who turns out not to matter.

The account graph’s next frontier

The original version of this argument, back in July 2025, was built around two upgrades: account over person, and company over domain. Both hold up. This is the third: buying group over persona. Not just resolving which company is active and in-market, but which specific group of individuals inside it is running a specific procurement process, reconstructed from signal rather than assumed from a title list, refreshed continuously rather than looked up once and left to decay for 18 months.

That’s what a paid account-based media programme actually needs to drive pipeline influence rather than reach: not a bigger list of the same three titles, but a live, per-account, per-procurement-process reconstruction of who’s actually deciding.

The manifesto, shortened

Job titles are a lagging indicator of who decides, in an org chart that decays within 18 months and was never guaranteed to have the title you’re looking for in the first place. Signal, first party and third party, device by device, is a leading indicator not a lagging one. Stack the two together and you stop targeting a guessed committee and start targeting the one that’s actually assembling itself around a specific procurement process, shadow group included.

If you want a second opinion on whether your current target account list is still built around titles that have already changed, reply to this email and tell me the account. I read every reply myself.

If a colleague on your ABM or demand gen team is still buying media against a fixed persona list, send this their way, the share button’s right there.

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This is exactly what FunnelFuel built to do. Its TAL Intelligence module and buying-committee scoring exist precisely to reconstruct a shadow buying group from first- and third-party signal, account by account, rather than hand you a persona filter and hope it’s still accurate. If you want someone who has built this rather than sold a slide about it, get in touch or see how it fits together at funnelfuel.io.

Is your own target account list built around a job title that might already be out of date, or a signal-derived group you’ve actually watched assemble itself? Drop your answer in the comments.

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