B2B has been negated for years when it comes to advertising technology, but the tides are changing and there are signs of increasing maturity around B2B and adtech

LinkedIn are making off platform moves galore and they just became the universal B2B identity layer that every major walled garden has agreed to wire into. Google previewed its first Smart Bidding strategy designed specifically for long B2B sales cycles. Meta used its Performance Marketing Summit to concede that targeting is no longer the hard part. And the Digiday Programmatic Marketing Summit revealed what brands and agencies are actually doing with agentic buying behind closed doors, which is rather less dramatic than the keynote-stage version.

If you’ve spent the last decade running B2B media plans where the choice was always “LinkedIn’s audience or the open web’s reach”, this is the week the false choice started to disappear.

What you’ll learn in this issue:

1. Why the Amazon and LinkedIn CTV deal is bigger than another DSP partnership announcement, and what it means for B2B media plans that have run on LinkedIn alone

2. Why Google’s quiet pre-GML drop of journey-aware bidding matters more for B2B lead gen than the headline Performance Max updates

3. What Meta’s “targeting is solved” position at its Performance Marketing Summit tells us about the next eighteen months of platform competition

4. How real brands and agencies are running agentic ad buying right now, and the guardrails worth stealing

5. A separate read on what The Trade Desk’s Q1 earnings reveal beyond the share price

Four stories that actually move the road in front of you.

1. Amazon hardwires LinkedIn into its DSP. The B2B identity layer is now official.

The story. On 7 May Amazon Ads and LinkedIn announced that advertisers can target LinkedIn’s professional audience signals inside Amazon DSP for connected TV campaigns. Job title, industry, seniority, company data, all available to buy as deal-based CTV through Microsoft Monetize, LinkedIn’s parent SSP. The integration sits alongside Amazon DSP’s existing supply partnerships with Netflix, Roku, Disney, Spotify and SiriusXM, which together cover ninety per cent of US households via authenticated graph technology. Amazon led its third annual upfront at the Beacon Theatre four days later. The Trade Desk became LinkedIn’s first programmatic CTV partner earlier this year. Amazon DSP is the second. No other major DSP currently offers LinkedIn professional data at CTV scale.

Why it matters. For fifteen years the B2B media plan has been a forced choice. You bought LinkedIn for the audience and paid the LinkedIn tax. Or you bought the open web for the reach and lost most of the B2B identity at the door.

The Trade Desk and Amazon DSP moves end that choice for CTV specifically. LinkedIn is no longer a destination buy. It is a portable audience signal that the largest non-Google DSPs have now agreed to honour as a first-class targeting layer.

That changes the maths on a B2B media plan immediately. LinkedIn’s reach inside its own feed has always been capped by feed inventory and dwell. Their attempts to extend off platform with a checkbox for their audience network (LAN) was always clunky and they seem to be ceding that they need partnerships to scale this, letting them focus on being the B2B identity layer

LinkedIn audiences on a ninety-per-cent-household CTV graph are not capped by the same thing. They are capped by your willingness to pay LinkedIn the data premium across more inventory.

The B2B-specific read. Two structural shifts are happening at once. First, LinkedIn is becoming the de facto identity layer of B2B, the way Google’s logged-in graph became the identity layer of consumer addressable advertising a decade ago. Second, Microsoft Monetize is doing the unglamorous SSP work that makes this portable. The combination has been telegraphed in plain sight since Microsoft shut down Microsoft Invest in 2025 and named Amazon DSP as the preferred transition partner. This week was the productisation moment.

For B2B buyers, the strategic question is not whether to use this. It’s how to layer your own signal on top. LinkedIn data is professional identity. It’s not buying intent. Stacking high-quality intent and engagement signal underneath the LinkedIn audience, through partners that operate at the raw signal layer rather than at processed scores, is where the next round of competitive advantage lives. The teams that win 2026 won’t be the ones with the most LinkedIn budget. They’ll be the ones that understand LinkedIn audiences are now a commodity layer everyone has access to, and that the differentiation has moved one floor up the stack.

2. Google’s quiet B2B move: journey-aware bidding lands in beta.

The story. On 7 May, two weeks ahead of Google Marketing Live, Google’s Ads team dropped three bidding and budgeting changes via the Ads and Commerce Blog.

The headline-grabber is journey-aware bidding, a beta Smart Bidding strategy for Target CPA Search lead gen campaigns that values the entire customer path, not just last-click events.

Smart Bidding Exploration is also being expanded to Shopping and Performance Max. And demand-led budget pacing is coming to Search and Shopping. The lead gen change is the first Smart Bidding strategy Google has designed explicitly with multi-touch B2B sales cycles in mind and is long overdue

Why it matters. Anyone running B2B paid search has known the dirty secret of Smart Bidding for years. The Target CPA models optimise for the last clicked event before a conversion, which in B2B almost always under-credits the long-tail mid-funnel research queries that actually shape the deal. The result was Google preferentially spending against branded search and bottom-of-funnel intent, which is also the cheapest stuff to win organically. The forecasts looked great. The incremental pipeline rarely did.

Journey-aware bidding rebalances that. By giving algorithmic weight to earlier touchpoints in the path, the model can theoretically value a “what is X” query the same way a sharp human buyer would, as part of an opportunity that closes nine months later.

The B2B-specific read. Two cautions. First, this is still Google’s model fed by Google’s view of the customer path and will almost definitely bias to Google touch points, as they always do. The deeper your CRM feedback through enhanced conversions, the more accurate the journey reconstruction. Teams that haven’t invested in offline conversion uploads or enhanced conversions API integration won’t get the benefit. Second, this is a Search product, not a programmatic one. It tightens the loop on Google’s lead gen channels. It doesn’t help anywhere your audience is researching outside Google’s properties, which in B2B is most of the buying journey.

The bigger story is what this signals. Google is now openly designing for B2B as a distinct customer with distinct measurement needs. That hasn’t been true for most of the last decade, where B2B advertisers have made do with consumer-grade Smart Bidding logic and worked around it with manual rules. Expect more from this thread at GML on Wednesday.

3. Meta admits targeting is solved. Creative is the new bottleneck.

The story. At Meta’s Performance Marketing Summit in San Jose this week, the dominant message across nearly every customer session was a quiet retreat from the targeting-as-product narrative.

Andromeda, Meta’s retrieval and sequence-learning system, is being framed as a settled capability. The new infrastructure investment is in creative.

AI-generated assets, creator-content remixing, dynamic format adaptation. The tension Meta couldn’t resolve on stage: AI-generated creative is being defaulted for smaller advertisers, while every session positioned authenticity and creator trust as the competitive moat. The same AI face available to your scaling DTC brand is available to every dropshipper in the same vertical.

AI is enabling platforms to target significantly more granular and small micro segments because of the power of its data mining - but creative limitations, which have always been a major issue even before AI, are all the more obvious now. Performance is being held back by a lack of granular messaging in the creative layer

Why it matters. For most of the platform era, the targeting layer was the moat. Match the user, deliver the impression, take the margin.

Meta is now publicly conceding that the targeting layer is commoditising, partly through their own Andromeda investment, partly through the broader agentic shift across the industry. What’s left to compete on is the creative input quality. That’s a much harder, much less defensible position to take if you’re Meta.

The B2B-specific read. It would be easy to read this as a consumer-only story. It isn’t. The same dynamic is arriving in B2B but on a delay.

LinkedIn’s Thought Leader Ads and the broader creator-as-credibility integration are the B2B equivalent of “real face, real voice, scaled by AI”.

this is why we are seeing B2B influence expand; trust, micro message segmentation across hundreds of very specific experts, and LLM farming

B2B platforms are heading to the same place.

The targeting is going to converge across the major channels (see story 1), and the differentiation will come from the creative input.

For B2B teams that have outsourced creative to brand agencies on long cycles, that is a real structural problem. The teams already building creative-as-infrastructure, point of view, repeatable formats, native testing capacity inside the platforms, are pulling ahead. The teams still running quarterly creative refresh cycles are going to find their CPMs walking up.

The deeper point is that “AI creative” isn’t the answer for B2B either. The same dropshipper-face problem applies to generic AI-generated B2B stock.

Sleek dashboards, faceless office workers, an “innovate confidently” pull-quote. Buyers tune it out faster than they tune out a banner. The teams winning will be the ones with proprietary point of view, named human authors, and a creative engine built to spin variants of that, not generate yet more average from a prompt.

4. The agentic buying mood shift: from FOMO to guardrails.

The story. The Digiday Programmatic Marketing Summit took place 6 to 8 May in Palm Springs, with the editorial recaps landing through this week.

The keynote-stage version had everyone deploying agents end-to-end. The behind-closed-door reality, captured in the recap pieces, is more grounded.

OMD sees a path to letting AI agents make optimisation decisions in H2 2026, but with mandatory human sign-off. Bayer is running agentic optimisation under hard spend caps and human approval on any recommendation. Kelly Scott Madison Media’s SVP of analytics framed the real concern plainly: an agent that hallucinates a CPM could blow a quarter’s budget in a weekend, and the agency, not the agent, carries the bag.

Separately, brand and agency execs aired live frustrations with Performance Max, The Trade Desk, and Meta’s Advantage+, with the line “Google doesn’t care that it’s terrible” doing the rounds in summit coverage.

Why it matters. The narrative that agentic buying is “fully here” was a January-through-March story. With the usual hyperbolic adtech swagger and the reality being way behind the narrative

The May reality is that it is here in narrow, supervised pockets. Reporting and QA workflows, audience research, pacing checks, supply-side QA.

Agents are doing real work in those tasks. End-to-end campaign autonomy is still being held back by exactly the right questions: hallucination risk, contractual liability, measurement validity.

The B2B-specific read. For B2B teams running through agency partners, this is the moment to write the guardrails into your contracts, not after a budget blow-up. The shortlist worth stealing from the brand-side rules in play: hard spend caps per agent, human sign-off on any recommendation above a threshold, no agent autonomy on creative approval, and an audit trail on every change to bidding or pacing logic. None of this is hard. It’s a one-page addendum to your IO or MSA. Most B2B teams haven’t asked for it yet because most B2B agency relationships are still slightly behind the agentic curve.

Worth noting: the IAB Tech Lab’s Agentic RTB Framework and the Ad Context Protocol that came out in late 2025 are the rails this whole conversation is running on. Both are worth understanding before your agency tells you “we’ve turned the agent on”.

Worth a separate read: TTD’s Q1, and what Samantha Jacobson’s departure to OpenAI actually signals

The Trade Desk’s Q1 numbers hit on 7 May. $689 million revenue, twelve per cent year on year, beating consensus. EBITDA margin compressed from thirty-four per cent to thirty per cent. Gross margin at seventy-four per cent, an eight-quarter low. The stock dropped 1.75 per cent on the day, capping a decline of more than seventy-seven per cent from its 52-week high.

The number that mattered less than the headline was the timing of CSO Samantha Jacobson’s exit to OpenAI as VP of Partnerships for Monetisation, confirmed to Adweek hours before the print. She stays on TTD’s board. That follows the earlier exits of CFO Alexander Kayyal and CMO Ian Colley.

The Trade Desk’s response was the right one. Jeff Green spoke warmly about Jacobson personally, talked up the “biggest buyer’s market in the history of advertising” thesis, and pointed to JBP momentum and ninety-five-per-cent-plus client retention. The fundamentals beneath the share price are still very real.

But the talent flow is the story. Senior adtech leadership moving into OpenAI’s monetisation team in the same week OpenAI opened its self-serve Ads Manager with CPC bidding to all US advertisers is the most concrete signal yet that the AI platforms are building real ad businesses with real adtech operators inside them.

The Open Internet’s leading independent platform has just lost its strategy lead to the platform most likely to fragment the open internet’s intent-formation layer over the next three years. That is not a coincidence, and it isn’t going to be a one-off. Watch the next senior move.

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The B2B Stack lands every Friday. No recaps, no five-things-to-watch slideware, no vendor press releases dressed up as insight. Just the implication layer of what actually moved in B2B that week, written by someone who has been operating in B2B programmatic for fifteen years and has watched most of these cycles play out once before.

If you’re rethinking how signals turn into pipeline, that’s the problem we built FunnelFuel to solve. → Talk to us.

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Sources

• Campaign US — Amazon Ads and LinkedIn partner to expand B2B reach for 2026 upfront, 5 May 2026

• MediaPost — Amazon, LinkedIn Team For B2B Inventory Ahead Of Upfront, 8 May 2026

• ppc.land — Amazon DSP integrates LinkedIn targeting for B2B streaming TV advertising, 8 May 2026

• Google Ads & Commerce Blog — Bidding and budgeting announcements, 7 May 2026

• ppc.land — Google targets hidden conversions with new bidding and budgeting tools, 8 May 2026

• GroAS — Today In Google Ads: May 14, 2026, Google Marketing Live 2026 Is Tomorrow, 14 May 2026

• Billo — Meta Performance Marketing Summit 2026: Targeting is solved. Meta is coming for creative next., 14 May 2026

• Digiday — Digiday Programmatic Marketing Summit May Recap: How marketers are navigating agentic ad buying, 15 May 2026

• Digiday — Marketers put up guardrails as AI agents reshape programmatic buying, 14 May 2026

• Digiday — The Trade Desk’s $689M revenue beat is blunted by the departure of CSO Jacobson to OpenAI, 8 May 2026

• The Motley Fool — The Trade Desk (TTD) Q1 2026 Earnings Transcript, 7 May 2026

• OpenAI — New ways to buy ChatGPT ads, 5 May 2026

• Deadline — Upfronts 2026 Report Card: Disney & Netflix Sizzle, NBCUni Fizzles, 14 May 2026

Got asked at an event a few weeks ago, “WTF is a HVA really?”

I’m guilty of using the term as if everyone’s clear on it. Let’s face it, that’s a solid adtech trait in itself, one that predates my 15+ years building solutions in the space. The industry has a habit of adopting acronyms before defining them, then making confused colleagues feel like the slow ones in the room.

So here’s the definition that should have been agreed years ago, properly framed for how B2B actually works.

Below is a video of me talking about HVA’s recently

A High Value Action is a behaviour on your site that indicates real buying intent, but is not a conversion in the traditional sense.

It is what happens before the prospect decides to put their hand up. It is the invisible majority of buying activity that most B2B reporting frameworks can’t see, can’t score, and can’t act on.

If you’re running B2B advertising in 2026 and you’re still measuring success in form fills, you’re optimising against the last few percent of the funnel. Everything in front of it is invisible. Most of what actually matters is invisible.

This piece is the field guide I would have wanted on day one in B2B adtech, written for the practitioner who has heard the term, used it confidently in a meeting, and at some point quietly realised they couldn’t define it precisely.

This is the second in a sixteen-week series unpacking the B2B programmatic stack, the way it’s actually operated by people who run it. Not the version vendors pitch.

What you’ll learn in this newsletter

• Why the traditional conversion model is structurally broken for B2B, and what a High Value Action actually is when properly defined

• The pressure-sensor framework for thinking about HVAs, how individual signals cluster into account-level momentum, and why a single visit is noise but four across three people is signal

• The full taxonomy of HVAs B2B teams should be capturing, organised by intent type, and the obvious ones most stacks miss

• How HVAs combine with third-party intent providers like Bombora, 6sense and TechTarget to produce a complete in-market picture, rather than competing with them

• The account-stitching problem, why GA4 alone can’t solve it, and what it takes to read a buying committee’s behaviour as a coordinated signal

• Five diagnostic questions to audit your own HVA architecture, and what most B2B teams will discover when they answer them honestly

I was talking about this on LinkedIn this week

The B2B conversion problem (skip if you’ve felt this for years)

If you’ve run B2B marketing for any length of time, you’ve watched the same scene play out: a six-figure deal closes, the SDR team gets the credit, the marketing attribution report shows the source as “direct” or “unknown inbound,” and nobody can really explain how the account moved from cold to ready.

This is not because attribution is broken in B2B, though it is. It’s because the entire conversion measurement framework that consumer marketing taught us to use was built for a different problem.

In B2C, the conversion event almost always happens on the website. Someone sees an ad, lands on a product page, adds to basket, checks out, done. The transaction is the conversion. The site is the funnel. The funnel is measurable. Click attribution mostly holds up because the buyer is a single person making a single decision in a single session, often within the same hour.

B2B doesn’t work like any of that.

The decision-maker rarely buys on the website. The buying committee rarely visits as a single coordinated group. The sales cycle rarely fits inside a single browser session. The final commit usually happens in a contract sent over email, sometimes negotiated for weeks, often involving procurement, legal, security review, and a champion internally building the case across departments that never touch the website at all.

The website is a research environment. It is where the buying committee learns about you, evaluates you, and works out whether you can survive their procurement process. The decision happens elsewhere.

This is the structural problem the form-fill conversion model can’t solve. When the actual buying event happens outside the measurement environment, every conversion-led metric becomes a proxy. And most proxies are weak.

Lead form fills are a proxy for interest, and a weak one. They capture the moment a prospect decided to identify themselves, which is almost always after they’ve already decided you’re worth a conversation. Demo bookings are a proxy for serious consideration, but they sit even further down the funnel, often after months of unobserved evaluation. Webinar registrations capture topic interest, but they don’t capture vendor interest. None of these are buying intent. They are commitments to a conversation, made after the buying intent has already formed.

What B2B teams actually need is a way to read intent before the commitment to a conversation is made. That’s where High Value Actions step in.

What an HVA actually is, properly defined

Strip the jargon and a High Value Action is a behavioural signal that captures the cost a prospect is willing to pay, in time and attention, to evaluate you before they’re ready to engage.

The cost matters. That’s what separates an HVA from any other page view.

A homepage visit costs the visitor almost nothing. They might have arrived by accident, clicked a paid ad without conscious intent, or be a competitor doing reconnaissance. There’s no way to score that single event meaningfully.

I like to use a mental ‘cost’ model, putting myself into the decision makers shoes.

A return visit to the pricing page costs more. The visitor has navigated back, deliberately, to a piece of information that matters. A side-by-side comparison page read costs more still. They are now in active evaluation. A security and compliance documentation download requires that the visitor has progressed in their internal stakeholder management to the point where IT or procurement is asking questions they need answers for.

Each of these behaviours has a price the visitor paid to perform it. That price is what makes the behaviour a signal rather than a noise event.

I think of HVAs like pressure sensors. A single sensor firing tells you almost nothing. A cluster of sensors firing in the same area, within a short time window, from devices that resolve back to the same organisation, tells you something specific is happening. That cluster is the signal. The individual readings are the components.

The job of the HVA framework is two-fold: define the behaviours that count, and build the scoring system that turns clustered behaviour into actionable account-level intelligence.

Obvious enough examples could be form fills, demos booked or a webinar registration. But those are the moments where the prospect has decided to talk to you. By the time those events fire, the real buying activity has already happened. The HVA framework lives upstream of that, or at least it does if you want to derive maximal value form it, and leap ahead of competitors who are in the same buying cycle as you.

The HVAs worth capturing are everything that happens before the commitment to a conversation:

• Repeat pricing page visits, especially across different sessions and different people resolving to the same account

• Comparison page reads, particularly the bottom-of-funnel “vs competitor” content

• Security, compliance and procurement documentation downloads

• API documentation engagement, which signals technical evaluation by an engineering buyer

• Customer story and case study reads, especially when the prospect’s industry matches the story

• Repeat returns to the product or platform overview pages

• Multiple people from the same domain landing in the same week

• Engagement with soft-gated content (no form, but tracks reading depth and dwell), particularly long-form analyst-style material

This is the buying committee researching you, costing themselves time and attention to do it, but not yet ready to put their hand up.

Most B2B teams treat these events as ambient website data. Useful, perhaps, for understanding traffic patterns. Not useful for measuring intent. That’s the inversion most stacks need to fix. These events are the intent signal. Form fills are the post-intent commitment to engagement.

In our experience working across B2B advertisers, somewhere around 95% of accounts seriously evaluating a vendor won’t fill in a form before the buying decision is materially formed. They look, lurk, share internally, and either go quiet or surface six weeks later with a decision already three-quarters made. If you’re measuring conversions only, you’re seeing the last few percent of the funnel. The HVA framework is how you make the rest of it visible.

The taxonomy: organising HVAs into a scoring framework

Defining HVAs is the easy part. Organising them into a scoring framework that activates against ad spend is where most teams get lost.

The framework needs to do three things: classify the behaviour by the type of intent it signals, weight the behaviour by the cost the visitor paid to perform it, and resolve the behaviour back to an account-level identity. Let’s take each in turn.

Classification by intent type. Different HVAs signal different things. A pricing page visit is a commercial qualification signal: the prospect is checking whether you fit their budget. An API documentation read is a technical fit signal: an engineer is checking whether you’ll integrate. A security documentation download is a procurement readiness signal: the prospect is moving you toward formal evaluation. A comparison page read is a competitive signal: they’re benchmarking you against alternatives. These are not interchangeable. A high score driven by commercial signals tells a different story than a high score driven by technical signals, and both deserve different sales follow-up.

Weighting by cost paid. Not all behaviours are equal. A single pricing page visit might score one point. A return visit within seven days scores three. A pricing page visit followed by a customer story read in the same session scores five. The progression matters more than the count. The weighting should reward depth of evaluation, not breadth of curiosity.

Account-level resolution. This is the part that breaks most stacks. Browser-level analytics tools like GA4 are user-centric by design. Each session is a user, each user is a cookie, and the cookie is increasingly unreliable as identity infrastructure decays. To make HVAs work, you need to resolve sessions back to organisations using IP-to-business graphs, reverse-DNS lookups, identity graph providers, or first-party login data where you have it. With six to ten people per buying committee on a typical enterprise B2B purchase (a figure Gartner has cited consistently for years), without account-level resolution you are reading a fragmented version of a coordinated signal. The committee is doing one thing. Your analytics layer is showing six unrelated things.

When all three layers work together, the HVA score becomes a meaningful operating metric. A score that crosses a threshold is a buying signal. A score that has crossed and held is a momentum signal. A score that has crossed, held, and broadened to new contacts within the same account is the kind of signal you want sales tracking actively, and the kind of signal you want your paid media leaning into.

This is where the language of “lead scoring” most teams already have falls short. Most lead scoring systems score individuals, not accounts. They score known contacts, not anonymous visitors. They score against demographic and firmographic fit, not behavioural change. HVA scoring is account-first, behaviour-first, and change-state aware. It’s a different operating model.

How HVAs combine with third-party intent (not against)

A common misread, when teams first encounter HVAs, is to position them in opposition to third-party intent data. The argument runs: third-party intent is bought, generic, probabilistic, and increasingly commoditised. HVAs are first-party, deterministic, and proprietary. So drop the third-party tools.

This is wrong. Or at least, it’s a shortcut that misses what third-party intent does well.

Bombora, 6sense, TechTarget and the wider B2B intent ecosystem all work by aggregating consumption signals across publisher networks, content syndication estates, review sites, and B2B-specific data sources. This is scale and depth of signal casting that no first party data stack can. match, so do not rush to discount the value of it. When an account is researching a topic across the open web, before they ever land on your site, that activity is captured in the third-party intent layer. By the time they show up to your domain, they have already done weeks of category-level research that you had no direct visibility into.

That category-level research is what third-party intent tells you. It tells you the account is in-market. It tells you what topics they care about. It tells you whether the surge in interest is new or sustained. It tells you, in aggregate, that something is happening at the organisation. None of it is relevant to JUST YOU

What it doesn’t tell you is whether they have started researching you specifically. That’s what HVAs tell you. The handoff is precise. Are. you on this accounts radar or not? this is the question that HVA’s should be tasked with answering

Third-party intent is the upstream layer: probabilistic, broad, predictive. It tells you the account is in-market for your category.

HVAs are the downstream layer: deterministic, narrow, confirmatory. They tell you the account is now researching your solution.

Stack them together and you get a complete in-market picture. An account that’s surging on Bombora topics relevant to your category, that hasn’t yet visited your site, is a top-of-funnel paid media target. The same account, now firing HVAs on your pricing page, is a mid-funnel acceleration target. The same account, with HVAs broadening across six contacts in a week, is a sales-ready notification.

Neither layer substitutes for the other. Teams that drop third-party intent because they’ve discovered HVAs lose the early-warning system. Teams that lean only on third-party intent miss the confirmation that the account is now researching them specifically, not just the category.

The teams winning at this in 2026 use both, with deliberate handoff logic between them.

The account-stitching problem most B2B teams won’t talk about

Here is the part where most B2B marketing stacks fall over, while everyone pretends the data is working.

Capturing HVAs at the individual user level is straightforward. Most analytics platforms can track pricing visits, document downloads, repeat engagement. You can build a basic score in an afternoon. What most stacks cannot do, even after years of investment, is stitch those individual behaviours back to a coherent account-level picture. This is the million dollar figurative unlock, the game-changer which lifts a nice data point and weaponises it

The reasons are infrastructural, and they’re worth naming clearly. You cannot Claude Code this in an afternoon because you won’t get the signal out of GA that you need to execute

GA4 is user-centric. The entire data model is built around user IDs, sessions, and events. There is no native “account” concept. To get account-level views, you need to enrich the user identity with firmographic data, and most teams either don’t do this, or do it in batch processing that creates a delay between the behaviour and the actionable intelligence.

Cookie-based identity is degrading. Safari blocks third-party cookies by default. Firefox does the same. Chrome’s Privacy Sandbox has been pushed back repeatedly but the direction of travel is one-way. First-party cookies are more resilient but still get cleared, blocked, and reset. By the time a buying committee has engaged across two months and six devices, the cookie identity layer has fragmented several times.

Identity resolution providers help, but they’re partial. The best deterministic identity graphs cover maybe sixty to seventy percent of business traffic. The rest is probabilistic, which means the account stitching is inferred, not confirmed. That’s fine for analytics and reporting, but it’s a different conversation when you’re activating against the data for paid media bidding or sales notifications.

Buying committees compound the problem. When six to ten people from the same company touch your site across a six-week buying cycle, they will use multiple devices, multiple browsers, sometimes different VPNs, sometimes their home networks. Without organisational stitching, you see fragments. Person A in the morning on the office network. Person B in the afternoon from home. Person C on a phone, on cellular, three weeks later. To you, that’s three unrelated visitors. To the buying committee, that’s one coordinated evaluation effort.

This is the gap where activation breaks down. The HVAs are firing. The data is being logged. Nobody is reading the firing pattern as a coordinated buying signal because the analytics layer cannot stitch the individual events to the account that’s actually doing the buying.

The fix is structural, not tactical. It requires an identity resolution layer that runs at the visit, not in batch. It requires the firmographic enrichment to be live, not nightly. It requires the activation layer, whether that’s sales notifications or paid media bidding, to consume account-level signal rather than user-level signal. And it requires the reporting layer to surface account-level momentum, not user-level page views.

Without this stitching, HVAs are a useful concept that produces a slightly enriched analytics report. With it, HVAs become the operating layer of B2B paid media.

Activating HVAs in advertising, and the dark funnel link

Once HVAs are scored, stitched to accounts, and surfacing in real time, the question becomes what to do with them.

Three activation patterns matter, and each requires the upstream HVA architecture to be in place to work at all.

Dynamic media allocation. Most B2B advertising runs against static account lists with static budgets. The marketing team uploads a target account list to the DSP, sets a budget, and the campaign runs until the budget is spent. Spend is even across accounts, irrespective of whether any particular account is showing intent. This is fundamentally inefficient. Accounts that are surging deserve more spend, not less. Accounts that are silent deserve less, not the same. HVA-driven activation flips the budget logic: spend follows signal in real time. An account crossing an HVA scoring threshold gets a budget increase, more impressions, more frequency. An account dropping below a threshold gets budget pulled. The campaign becomes responsive to the buying committee’s actual behaviour rather than a static plan.

Account-level frequency capping. Standard programmatic frequency capping runs at the cookie level. If you cap at three impressions per user per day, that’s three impressions for the user, not three for the account. With six people in a buying committee, you might serve eighteen impressions to that organisation in a single day, hitting some users harder than others, creating no coordinated pressure. Account-level capping flips this: cap at a per-account threshold, and frequency is distributed across the buying committee rather than stacking on a single user. The signal-to-spend ratio improves dramatically.

One point to note; a fast read makes this sound like you should cap all accounts equally, like programmatic doctrine works on users. However you are not here engineering account level first and third party intent collusion to then abstractly treat all accounts as equals. The game here is to use this data to inform a cap, but the cap should be set per account or account cluster based on the signals we’re weaponising. A small but significant point. Treating a TAL as a collection of equals is one of the biggest single flaws I see everyday

Dark funnel illumination. This is the use case that often gets overlooked, and is arguably the most strategically interesting.

The dark funnel is the part of the buying journey you can’t see directly. Slack conversations between buyers and their peers. Reddit threads and LinkedIn DMs about your product. Conversations at industry events. Internal champions building the case for you in meetings you never attend. None of this activity is observable to your analytics layer.

HVAs are the lit-up trace of the dark funnel surfacing into your environment. When an account that has had no observable interaction suddenly starts firing HVAs across three or four people in a week, the dark funnel is working. Someone inside that organisation has surfaced you. The conversation has happened. The buying committee is now evaluating you, and you have your first visible read of it.

That read is the signal you want to amplify. Not by retargeting the user who landed, but by ensuring the broader buying committee sees you across their preferred channels. CTV in the evening, audio on the commute, native in the trade publications they read. The HVA scored you a meeting with the dark funnel. The activation layer is how you make sure they remember you when the committee meets next week.

The five questions to ask about your own HVA stack

These are the questions I work through when auditing a B2B marketing stack. They will surface most of what’s broken, fast.

1. What HVAs are you capturing, and how are they classified? Can you list, by name, the specific behaviours you score as HVAs? Are they classified by intent type (commercial, technical, procurement, competitive)? If the answer is “we track pricing page visits and demo requests,” you have two signals, not an HVA framework. You should have ten or more distinct behaviours, organised into categories, with weights attached.

2. Is your scoring account-level, or user-level? When the score crosses a threshold, are you alerted to the account, or to a single user identifier? If it’s user-level, you’re missing the buying-committee signal entirely. A score of seventy from one user is interesting. A combined score of forty across four users in the same account is more interesting. The framework should always surface the latter.

3. What’s your identity stitching coverage, and is it deterministic or probabilistic? What percentage of your site traffic resolves to an organisation, and of that, what percentage is matched deterministically versus inferred? If you don’t know the answer, ask your analytics or RevOps team. Industry-typical for un-augmented B2B sites is somewhere between thirty and fifty percent firmographic match coverage. Augmenting with an identity resolution provider can push that to seventy or eighty, but the gap matters for what you can activate against.

4. Is the HVA signal flowing into ad activation in real time, or in batch? When an account crosses an HVA threshold, how quickly does that signal reach the DSP, the LinkedIn campaign manager, the CTV buy? If it’s a weekly export from analytics, you’ve already lost the moment. If it’s real-time via an integration or audience pipeline, you can act on the buying-committee surge while it’s still active. Real-time changes what HVAs are worth.

5. Are you measuring HVA impact on pipeline, or just on traffic? The HVA scoring framework needs to be tied back to pipeline outcomes to demonstrate value. Which HVAs, in which combinations, most strongly predict opportunity creation? Which scoring thresholds best correlate with sales engagement? Without this measurement loop, you are running a sophisticated behaviour-tracking system that doesn’t tell you whether it’s working. With it, you can refine the scoring weights, prune the noisy HVAs, and tune the framework to your actual buying cycle.

If you ask these five questions and your team can’t answer them cleanly, you have a stack problem. Not a strategy problem. Not a budget problem. A stack problem, fixable in weeks rather than quarters, once the diagnostic is honest.

Where this is heading

Three shifts are reshaping how HVAs operate in B2B paid media.

HVA scoring is becoming the default unit of B2B account targeting. The shift from static account lists to dynamic, signal-fuelled account targeting is well under way at the more sophisticated B2B marketing teams. Over the next two years this becomes the default. Static account lists will feel as outdated as buying audience segments by demographic.

Identity resolution is consolidating into a few dominant graphs. The B2B identity layer has been fragmented for years, with each vendor maintaining its own graph and each integration creating its own translation layer. The market is consolidating, with a handful of identity providers becoming the de facto graphs. This makes account-level HVA stitching cheaper and more reliable, but also more concentrated, with attendant risks for any vendor that ends up locked into a single graph.

The dark funnel layer is becoming addressable. HVAs combined with the right identity infrastructure are increasingly able to surface dark funnel activity into the activation layer. Not perfectly. Not for every account. But the trajectory is clear: the gap between what the buying committee is actually doing and what the B2B marketing team can see is closing.

The B2B marketing teams that operate against this trajectory will pull away from the ones still measuring success in form fills. The gap is widening, even if the budgets look similar.

The takeaway

A High Value Action is not a website analytics event. It’s the structural alternative to a conversion measurement framework that doesn’t fit B2B’s reality. It’s how you read buying committee intent before the buying committee is ready to identify itself.

If you’re running B2B marketing in 2026 and you cannot articulate which HVAs you’re capturing, how you score them, how you stitch them to accounts, and how you activate against them, you are operating with the wrong tooling. The good news is the diagnosis is cheap, and the fix is mostly infrastructural rather than strategic.

The five questions above are the fastest route to honesty. If you can’t answer them cleanly, that’s your roadmap.

Next in this series: the dark funnel playbook, how to activate B2B paid media against the buying activity you can’t directly observe, and what changes when the activation layer learns to read what the analytics layer can’t see.

The B2B Stack is written by Mike Harty, founder of FunnelFuel, a B2B-native programmatic managed service operating across London, New York and Singapore. If you’re auditing your own HVA architecture and want a second pair of eyes, reply to this email and we can compare notes. Want to connect with me, book a 30 minute meeting https://calendar.app.google/PDZ36wk1Yci7PuDf6 here

No fresh agency-vs-DSP fight. No earnings shocker, exactly. But pull this week’s stories together and you can see something specific happening: every layer of the stack made an agentic move inside five days.

Omnicom is gunning for the adtech middlemen. Yahoo and Kochava formalised a workflow that treats the DSP as a backend service rather than the front door. Meta opened its ad ecosystem to third-party AI tools. And on Thursday, OpenAI confirmed ChatGPT ads are landing in the UK in the next few weeks.

If you sit on the demand side of B2B media, this is the week to stop reading “agentic” as a slide deck term and start treating it as a budget reallocation question.

The orchestration and planning layer — the thing that decides which DSP, which inventory, which audience, which creative, against which signal — is where the next decade of margin sits. Everyone in this week’s news flow is racing to own it.

There is also a quieter story running underneath: a protocol layer is being built in real-time across the industry. Omnicom is using AdCP. Meta is opening through MCP. Yahoo and Kochava are connecting via MCP. The IAB Tech Lab has named the umbrella standard AAMP. The plumbing is being agreed in front of our eyes, and the timeline for “this gets settled” is months, not years.

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Four stories that actually move the road in front of you.

1. OpenAI’s ChatGPT ads land in the UK

The story. On 7 May, OpenAI confirmed it would start testing ChatGPT ads in the UK, Brazil, Japan, South Korea and Mexico over the next few weeks (Digiday, Reuters). Earlier in the week, the self-serve ads manager opened to US advertisers of all sizes — restricted categories aside — and OpenAI promised third-party measurement and CPA bidding to address the “is this actually working” question (Digiday, AdExchanger). The ad business is reportedly tracking around $100M annualised after roughly three months live, against a $2.5B 2026 internal target and $100B by 2030 (Axios). For UK context, Black Lab Digital’s analysis last week is one of the better practitioner reads on what UK marketers should be doing now.

My take. Two weeks ago this was a US story with directional UK relevance. Now it is a UK media plan question. Most British B2B marketers I speak to are still treating ChatGPT as a dark funnel they cannot see into. From later this month, that funnel has paid placements in it, in your buyers’ language, with self-serve access and CPA bidding.

The “we will revisit it once it scales” position is now actively expensive — both because your competitors are training the relevance model against your category, and because ChatGPT’s CPC range ($3 to $5 in early reporting) sits materially below LinkedIn for comparable B2B intent. I’d argue it’s at BARGAIN levels right now and it won’t last because it doesn’t direct many clicks, so competition is going to heat up fast.

The bigger structural point: OpenAI just turned its first market expansion into a measurement story rather than a reach story. That is the right move for an ad platform that wants budget rather than experimentation spend. The programmatic testing, which started at an expensive but should-be-defensible $60 CPM, crashed to $25 CPM, and then dissolved into self-service CPC. The testing is a $100m ad business car crash which will win despite the best efforts of those paid to make it fly.

As we were talking about on LinkedIn this week, there is a split growing between Google AI (direct response) and ChatGPT (brand) and where they see the main leaning in this tech.

Programmatic display and CPM bidding backed up brand. CPC into CPA backs up performance/DR.

It baffles me that they are so confused around what they are selling. For me, in B2B, this meets every tickbox for brand advertising — see LinkedIn post below.

OpenAI ChatGPT ads in the UK

• Digiday, Krystal Scanlon, ‘Expand thoughtfully’: OpenAI offers ChatGPT ads to new markets including the U.K., Brazil and Japan, 7 May 2026 — link

• Digiday, OpenAI opens up ChatGPT ads manager to the U.S. while promising third-party measurement, CPA bidding, May 2026

• AdExchanger, POSSIBLE 2026: OpenAI announces CAPI and self-serve ads manager launch, May 2026 — link

• Reuters Institute for the Study of Journalism, Felix Simon, Advertising was always going to come for AI chatbots. The real question is how — link

• Black Lab Digital, James Starkey, ChatGPT Ads Are Coming to the UK: What Brands Need to Know Now, 6 May 2026 — link

• Axios, OpenAI projecting $100bn in advertising revenue by 2030, April 2026

2. Omnicom is testing AI agents to cut out the adtech middlemen

The story. On Omnicom’s Q1 2026 earnings call (28 April), CTO Paolo Yuvienco confirmed the holdco has already executed live agent-to-agent media buys for several clients, using AdCP — the Ad Context Protocol, an open standard that lets AI agents discover inventory, transact, build creative and manage accounts directly with publishers (AdExchanger, Digiday, ExchangeWire, BestMediaInfo). CEO John Wren framed adtech intermediaries as extracting a “toll” paid by clients, and said shortening the path to publishers is now a strategic priority.

The under-reported half of the story: Omnicom is plugging Acxiom — acquired as part of the IPG deal — directly into Omni, the holdco’s AI sales and marketing platform, to build unified customer IDs that feed the agents (AdExchanger). Wren told investors his 2018 decision to walk away from buying Acxiom for $2bn was vindicated; seven years later, he’s bought all of IPG (including Acxiom) for $9bn and the data layer is now the engine for agentic buying.

My take. This is the most consequential story of the week, and it is being read too narrowly.

The headline is “Omnicom builds agents,” and that is the definition of “meh, so what.”

The actual story is: the world’s largest media holdco has decided that the right operational unit for the next era is “holdco plus agent plus first-party data spine” — not “holdco plus DSP plus SSP plus verification plus identity plus ABM platform plus attribution.” Each of those middle-layer point solutions becomes optional — useful where the agent decides it is useful, replaceable where it isn’t.

The DSP market is now getting plundered by SSPs bringing a media + curation + [increasingly an] activation layer — see Index Exchange and Bedrock Platform, who shipped the world’s first containerised DSP bidder running inside the exchange itself on 21 April (AdWeek, ExchangeWire, Index Exchange, Bedrock). This is supply chain compression at its finest.

Now the source of the majority of ad spend, big holding co agency groups, are seeking to use protocol-level tech (AdCP, MCP) to bypass the old world line up of adtech full stop. This is the compression from the cheque book side of the market, and goes even further.

For B2B, this is going to land harder than it does for B2C, and faster. B2B media plans already lean on a super fragmented stack — DSPs, identity vendors, intent providers which are irrelevant in B2C, ABM platforms to score, verification, attribution. Most of that stack exists because no single platform handles the buying-committee-and-intent layer cleanly. Keep your eyes peeled, because we are building something at FunnelFuel which will compact this whole stack and fix this issue — an advertising OS for B2B which upgrades the existing best infrastructure to work for B2B.

An agentic orchestrator that can stitch firmographic, intent and engagement signals across that fragmented set is, structurally, what every ABM tool has been pitching for ten years and never quite delivered. If Omnicom builds it credibly, the rest of the holdcos follow within twelve months, and the independent middle layer has eighteen months to prove it is not worth bypassing.

The B2B implication: if you are running a 2026 stack review, the question is not “which DSP” or “which ABM platform” — it is “which of these tools survive a holdco putting an agent in front of them, and which become commodity execution pipes.”

Omnicom + AdCP + Acxiom

• AdExchanger, Omnicom Has An AI-Powered Plan To Cut Out Ad Tech Middlemen, 30 April 2026 — link

• Digiday, ‘That is an objective’: Omnicom tests AI agents to cut out the ad tech middlemen, late April 2026 — link

• ExchangeWire, Digest: Omnicom Tests AI Agent Media Buying, 30 April 2026 — link

• BestMediaInfo, Omnicom says it has executed real client media buys using agent-to-agent AI framework — link

• AdExchanger, Media In 2026: From Managed Decline To Ruthless Independence (analyst context on holdco agentic strategy) — link

3. Yahoo + Kochava put a name on the new shape: the Unified Ad Platform

The story. On 5 May, Yahoo and Kochava announced a partnership embedding a Yahoo DSP workspace inside Kochava’s StationOne platform — pitching agentic workflows that operate across multiple DSPs from a single orchestration layer (Digiday, AdExchanger, GlobeNewswire). The technical detail matters: the Yahoo workspace ships pre-defined Skills, Agents, and an MCP Connection between StationOne and Yahoo DSP. Kochava CEO Charles Manning compared StationOne to Slack — “instead of connecting people on your team, the way that Slack does, StationOne connects all the tools that ad ops, media buying teams, or traders use.” On 8 May, Digiday published a “WTF is a unified ad platform” explainer formalising the term coined by W Media Research’s Karsten Weide. Yahoo’s framing is telling: the DSP becomes the “system of record” for performance and brand safety, but campaign logic moves up into the orchestration layer.

My take. The terminology matters because it is the first time the industry has named what has been quietly happening for two years — DSPs and SSPs converging, point solutions getting absorbed, the stack reorganising around fewer, broader players. This is the reality of a long-awaited and long-overdue cleansing of an adtech supply chain which got too caught in its ways for too long, and supported bloated re-selling and arbitrage long past an acceptable end date for a new technology ecosystem. “Unified Advertising Platform” is the box every vendor will now spend 2026 trying to fit themselves into.

Read together with the Omnicom story, this is the same shift seen from two angles. The holdcos want to own the orchestration layer with their own agents. The independent adtech sector wants to own it by merging the middle into a unified product. Whoever wins, the standalone DSP and standalone SSP both lose narrative gravity. They become components, not categories.

For B2B buyers, the pragmatic question is supplier risk. Most B2B media plans are running through partners who built their value proposition in the OpenRTB era — quality of audience, breadth of supply, attribution layer, data layer. Those propositions still hold operationally. They are just losing the narrative to the unified orchestration story. That gap between operational reality and narrative direction is where bad procurement decisions get made over the next twelve months. Don’t let your stack be reorganised by a slide deck before the underlying products have actually changed.

Right now, the reality is that the agentic landscape and the Omnicom way would be stuck trying to navigate and procure messy, small, B2B first party data. B2B data changes over faster than B2C, whether it’s the work email linked to the job that you change every 1–3 years; titles, locations, and everything that goes with it — versus B2C data which remains intact longer. Personal emails that last on average 5x longer than work ones, age ranges, genders, income bands, and the underlying foundations of consumer marketing are significantly more static. Don’t expect agents to be able to locate and find inventory packages for B2B easily. But equally, back AI to find the answers faster than we expect it to.

Yahoo + Kochava + StationOne (the Unified Ad Platform thesis)

• Digiday, Ronan Shields, Ad Tech Briefing: Yahoo pairs with Kochava to pitch ‘agentic’ DSP workflows, 5 May 2026 — link

• AdExchanger, AI Creates More Ad Tech Frenemies (Yahoo/Kochava alliance context), 6 May 2026 — link

• GlobeNewswire (Kochava press release), Kochava Announces New Yahoo DSP Workspace Now Available on StationOne, 5 May 2026 — link

• Digiday, WTF is a unified ad platform?, 8 May 2026 — link

• W Media Research, Karsten Weide — original UAP framing

Bedrock + Index Cloud (containerised DSP / supply-chain compression)

• AdWeek, Index Exchange Welcomes DSPs into New Cloud Infra, Bringing Bidders Closer to Ad Inventory, 21 April 2026 — link

• ExchangeWire, Bedrock Debuts Containerised DSP Deployment on Index Cloud, 24 April 2026 — link

• Index Exchange press release, Introducing Index Cloud, 21 April 2026 — link

• Bedrock Platform, Bedrock Platform Launches the First Containerized DSP in Index Cloud — link

• PPC Land, Bedrock becomes first DSP to run its bidder inside an exchange — link

4. Meta opens its ad ecosystem to third-party AI tools

The story. On 5 May, Meta confirmed it would open its ad ecosystem to third-party AI tools (Digiday, Mobile Marketing Reads, Performance Marketing World). The technical detail is the headline that most of the trades buried: the new “Meta Ads AI Connectors” are built on Model Context Protocol (MCP) — the same open standard used by Anthropic’s Claude and OpenAI’s ChatGPT — meaning advertisers can run Meta campaigns through external AI assistants using natural language, without needing API integration or developer credentials. eMarketer’s Jacob Bourne called it “an opening up, but it’s also a subtle lock-in move.” Sonata Insights’ Debra Aho Williamson read it as Meta getting ahead of “concerns about being too closed or controlling.”

My take. Meta is the canary, not the headline. If Meta is opening to third-party AI tools, Google and LinkedIn are next, and the timeline is shorter than “next year.” The reason is that the largest advertisers will not run agentic buying selectively — they will run it across their full media footprint, or not at all. Walled gardens that don’t make their inventory agent-addressable get bypassed in the agent’s recommendation set. That is a structural threat the walled gardens cannot ignore.

The protocol layer is the bigger story. Meta’s connectors run on MCP. Yahoo+Kochava connects via MCP. Omnicom uses AdCP. The IAB Tech Lab named its umbrella initiative AAMP (Agentic Advertising Management Protocols) in February. What looked like a fragmented set of vendor announcements is actually the early phase of a protocol war — and unlike the OpenRTB era, the protocols are being hammered out in months rather than years. Whoever owns the dominant standard owns the rails the next decade of programmatic runs on.

The B2B angle: LinkedIn is the one to watch. LinkedIn’s value proposition for B2B is its account graph plus its inventory access. If LinkedIn goes the Meta route — opening up to third-party agents — then LinkedIn becomes a data layer that any agent can call, not just a destination platform you have to log into. That changes how LinkedIn budget gets allocated, who controls the planning, and how layered targeting actually gets executed across LinkedIn plus open web plus CTV. It is also, by some distance, the most important strategic question for British B2B marketers in the second half of 2026.

LinkedIn will resist longer than Meta did, because its walled-garden margin is more defensible. But the direction is the direction.

What ties this week together

Four moves, one shape. The orchestration layer — the part of the stack that decides what runs where, against which signal, for which outcome — is being claimed by everyone at once. Holdcos want to own it (Omnicom, with Acxiom underneath). Independent adtech wants to consolidate into it (Yahoo+Kochava, the UAP framing, Bedrock running inside Index). Walled gardens want to gate it on their terms (Meta this week, the others soon). And the new ad platforms — OpenAI being the obvious one — want to be a destination inside it.

The middle of the stack — the standalone DSPs, the standalone SSPs, the point-solution intent vendors, the attribution-only tools — is being asked to justify why it shouldn’t be a feature of someone else’s agent.

For B2B specifically, this is genuinely good news, but only if you read it correctly. The 2026 B2B stack has more friction than any other category — buying committees, account-level resolution, intent layering across firmographic and behavioural signal, multi-touch attribution across channels that don’t share identifiers. Agentic orchestration is genuinely the right tool for that problem. The risk is buying the orchestration story before the underlying components have caught up — paying for a unified narrative on top of a fragmented reality, and discovering the seams six months in.

The operators who will do well are the ones running their 2026 stack review on the components first, the orchestration second. Audience quality, signal depth, supply path, identity stitching — those still decide whether your media works. The agent is only as good as what it has to call.

Meta opens to third-party AI tools (MCP)

• Digiday, Meta opens its ad ecosystem to third-party AI tools, 5 May 2026 — link

• Mobile Marketing Reads, Meta opens ad platform to third-party AI tools in shift toward integrated workflows, May 2026 — link

• Performance Marketing World, Meta opens ad accounts to third-party AI tools, May 2026 — link

• Marketing Brew, How Meta’s AI push is changing ad creation (April 2026 context) — link

If this landed, forward it to one person who is in the middle of a 2026 stack review. That is who needs to read it.

Reply with what you’re seeing in your own buying — the practitioner threads in this newsletter come from readers, and the more honest the conversation gets, the better the next edition.

Need some help piecing together running addressable programmatic advertising in 2026 - reply to this email or message me at mike [at] funnelfuel [dot] io

Mike

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References & further reading

OpenAI ChatGPT ads in the UK

• Digiday, Krystal Scanlon, ‘Expand thoughtfully’: OpenAI offers ChatGPT ads to new markets including the U.K., Brazil and Japan, 7 May 2026 — link

• Digiday, OpenAI opens up ChatGPT ads manager to the U.S. while promising third-party measurement, CPA bidding, May 2026

• AdExchanger, POSSIBLE 2026: OpenAI announces CAPI and self-serve ads manager launch, May 2026 — link

• Reuters Institute for the Study of Journalism, Felix Simon, Advertising was always going to come for AI chatbots. The real question is how — link

• Black Lab Digital, James Starkey, ChatGPT Ads Are Coming to the UK: What Brands Need to Know Now, 6 May 2026 — link

• Axios, OpenAI projecting $100bn in advertising revenue by 2030, April 2026

Omnicom + AdCP + Acxiom

• AdExchanger, Omnicom Has An AI-Powered Plan To Cut Out Ad Tech Middlemen, 30 April 2026 — link

• Digiday, ‘That is an objective’: Omnicom tests AI agents to cut out the ad tech middlemen, late April 2026 — link

• ExchangeWire, Digest: Omnicom Tests AI Agent Media Buying, 30 April 2026 — link

• BestMediaInfo, Omnicom says it has executed real client media buys using agent-to-agent AI framework — link

• AdExchanger, Media In 2026: From Managed Decline To Ruthless Independence (analyst context on holdco agentic strategy) — link

Yahoo + Kochava + StationOne (the Unified Ad Platform thesis)

• Digiday, Ronan Shields, Ad Tech Briefing: Yahoo pairs with Kochava to pitch ‘agentic’ DSP workflows, 5 May 2026 — link

• AdExchanger, AI Creates More Ad Tech Frenemies (Yahoo/Kochava alliance context), 6 May 2026 — link

• GlobeNewswire (Kochava press release), Kochava Announces New Yahoo DSP Workspace Now Available on StationOne, 5 May 2026 — link

• Digiday, WTF is a unified ad platform?, 8 May 2026 — link

• W Media Research, Karsten Weide — original UAP framing

Bedrock + Index Cloud (containerised DSP / supply-chain compression)

• AdWeek, Index Exchange Welcomes DSPs into New Cloud Infra, Bringing Bidders Closer to Ad Inventory, 21 April 2026 — link

• ExchangeWire, Bedrock Debuts Containerised DSP Deployment on Index Cloud, 24 April 2026 — link

• Index Exchange press release, Introducing Index Cloud, 21 April 2026 — link

• Bedrock Platform, Bedrock Platform Launches the First Containerized DSP in Index Cloud — link

• PPC Land, Bedrock becomes first DSP to run its bidder inside an exchange — link

Meta opens to third-party AI tools (MCP)

• Digiday, Meta opens its ad ecosystem to third-party AI tools, 5 May 2026 — link

• Mobile Marketing Reads, Meta opens ad platform to third-party AI tools in shift toward integrated workflows, May 2026 — link

• Performance Marketing World, Meta opens ad accounts to third-party AI tools, May 2026 — link

• Marketing Brew, How Meta’s AI push is changing ad creation (April 2026 context) — link

Protocol layer context (AdCP, MCP, AAMP)

• Kochava + IAB Tech Lab, Open-Source StationOne AI Workspace using IAB Tech Lab’s AAMP Buyer Agent, March 2026 — link

• IAB Tech Lab, Agentic Roadmap initiative (AAMP / ARTF documentation)

Ask ten B2B marketers what a deal ID is, and you’ll get nine variations on the same answer: it’s how you buy premium publisher inventory programmatically. A way to get into the Wall Street Journal or The Economist without picking up the phone.

That answer was correct in 2017. In 2026 it’s so incomplete it’s actively misleading.

The deal ID has quietly become the single most important construct in modern B2B programmatic. Not because publishers have got more important, but because deal IDs are now the activation token for almost everything that matters: curated B2B audiences, first-party data overlays, supply-chain accountability, fee transparency, frequency capping at the account level, and the entire sell-side targeting movement that has reshaped how the open web works.

If you’re running B2B programmatic in 2026 and you can’t articulate what’s underneath every deal ID in your campaign, you don’t really know what you’re buying. That’s a hard sentence, but it’s the truth, and the rest of this piece is about why.

This is the first in a sixteen-week series unpacking the B2B programmatic stack, the way it’s actually operated by people who run it. Not the version vendors pitch.

What you’ll learn in this newsletter

• Why the deal ID has quietly become the most important construct in modern B2B programmatic, and why most B2B marketers are still operating with a 2017 mental model of what it does - and missing out on what it can really do for them

• A plain-English primer on how programmatic actually works, and why every glitzy new channel (CTV, audio, retail media, the imminent ChatGPT ad surface) runs through the same plumbing

• The four canonical types of deal in programmatic (PG, Preferred, PMP, curated open auction), why the names confuse buyers, and how to tell which one you’re actually transacting on

• Why curated deal IDs have structurally changed B2B targeting, and how Audigent, D&B, and the new wave of B2B-specific curators are rewriting the economics of the open web

• The four operating patterns most B2B programmatic stacks fall into, and why pattern four is the only one that performs in 2026

• Five questions to ask about any B2B deal ID that will surface most of what’s broken in a typical stack within an hour

First, the frame: what programmatic actually does (skip if you live in this stuff) and why the biggest vendors use it

If you run B2B marketing but don’t operate programmatic day to day, here’s the orientation you need before the rest of this piece makes sense.

Programmatic advertising is automated media buying. Instead of an agency calling The Economist’s ad sales team to negotiate a banner placement, software does the buying in real time. When someone loads a webpage, opens a connected TV app, streams a podcast, or scrolls a news feed, an auction runs in the background. It takes about a hundred milliseconds. Your demand-side platform (DSP, the buying software) decides whether to bid for that impression based on who the user appears to be, what the content is, and what your campaign rules say. The supply-side platform (SSP, the publisher’s selling tool) collects the bids and awards the impression to the highest bidder. The ad renders. The user is none the wiser. Repeat around one trillion times a day.

This used to mean banner ads. It doesn’t anymore. Programmatic is now how almost every digital ad format gets bought: display, online video, native (in-feed sponsored content that looks editorial), connected TV (Netflix, Disney+, Paramount, the lot), digital audio (Spotify, podcast networks), and increasingly digital out-of-home (the screens at Canary Wharf and JFK). The flashy new channels everyone is excited about (CTV, audio, retail media networks, the imminent ChatGPT ad surface) all run on the same underlying plumbing. Different inventory, same auction mechanics, same DSP and SSP architecture, same supply-chain dynamics.

For B2B marketers, this matters more than for most. Your audiences are small. Your sales cycles are long. Your spend is scrutinised by a CFO who wants to know what every dollar did. Open auction (the default, hands-off way of buying programmatically) treats your budget the same way it treats a consumer brand running a back-to-school campaign: it tries to find scale at the lowest CPM. That’s not what B2B needs. B2B needs precision against a defined account list, supply-path discipline so fees don’t swallow the budget, and a way to apply your CRM data to a CTV campaign without giving away your customer list. In other words, the B2B use case starts stretching he technology capability behind programmatic, finding the edge cases and nuances and maxes them out. DealIDs are not that edge case, but in B2B we need them even more than our consumer focussed cousins

All of this is where deal IDs come in. They are the configurable control mechanism that turns programmatic from an auction running on autopilot into an activation framework you can actually steer. Without them, you’re shouting into the auction and hoping. With them, you’re operating the stack on purpose. Every premium B2B channel you might want to run (CTV against a target account list, podcast inventory against a buying committee, native placements in trade publications, contextual buys against intent-aligned content) is delivered through deal IDs. Knowing how they work is the difference between getting value from these channels and getting a slick post-campaign report that doesn’t translate into pipeline.

That’s the frame. Now the substance.

What a deal ID actually is, mechanically

Strip away the marketing layer and a deal ID is a string. Usually nineteen characters, generated by a supply-side platform (SSP) or ad server when a deal is set up, passed in the bid request from the SSP to demand-side platforms (DSPs), and recognised by the DSP as a signal to bid under pre-negotiated terms.

That’s it. A unique key that says: this impression has rules attached to it, here’s the rules, and here’s who’s allowed to bid.

Three things flow from that:

The deal ID is the carrier, not the cargo. It tells the DSP that special terms apply. The terms themselves (price floor, format, frequency, audience overlay, inventory list, payment treatment) sit in the deal record at the SSP. The DSP just needs to recognise the ID and apply the matching campaign logic. Often case Deals transfer a chunk of the targeting work off of the DSP, meaning your DSP fees typically come down (most contracts offer cheaper DSP take rates for DealID trading)

The deal ID is the unit of accountability. Because every impression purchased under a deal ID is auditable back to a specific set of terms, deal IDs are the only sane way to enforce supply-chain transparency at scale. Open auction buying, by comparison, is a black box where you trust the DSP’s reporting to tell you where the impression came from, what it cost the SSP, and what fees got stacked on top.

The deal ID is the activation primitive for sell-side targeting. This is the part most B2B marketers haven’t internalised yet. More on this below.

The four flavours, and why the names are misleading

There are four canonical types of deal in programmatic. The names are technical, the differences are commercial, and the way the industry talks about them is genuinely confusing because most explainer content was written before curation arrived and rewrote half the rules.

Programmatic Guaranteed (PG). Fixed inventory, fixed price, guaranteed delivery, one buyer, one seller. The closest thing to a traditional direct buy, executed through pipes. Used when you absolutely need a specific spot at a specific price (a homepage takeover, a high-impact format on a brand-safe property). Limited optimisation flexibility because the deal commits both sides to specific volume.

Preferred Deal. First look at inventory at a fixed CPM, no commitment to buy. The buyer gets the option to bid; the seller doesn’t have to fill the impression that way if the buyer passes. Useful for premium audience targeting where you want priority but not obligation. This is a personal favourite, it lets you (often-case) get a near equal look at the inventory as the publishers ad server. It thus comes at a price, but in B2B that price is always worth it

Private Marketplace (PMP). Invitation-only auction. The seller invites a small group of buyers to compete for inventory at a floor price. More buyers means competitive bidding, which can lift CPMs but provides the seller more revenue stability than open auction. Most “premium publisher” deal IDs B2B marketers think they’re buying are actually PMPs.

Open auction with deal ID overlay (the curated deal). The newest and most important category, and the one that has structurally changed the role of the deal ID. The inventory itself is sold through the open auction, but a curator (an SSP, a data company, or a specialist) has packaged audience signals, contextual signals, supply-chain validation, and pricing logic into a single deal ID. The buyer activates one deal ID; everything underneath is pre-negotiated. We’ll spend the next section on why this matters specifically for B2B. This is an area of innovation that I am focussing on in my day job - super signal aggregation for B2B

The reason the names confuse people: a “PMP” in vendor sales decks often refers to any deal ID, including curated open-auction deals. Industry shorthand has collapsed four distinct constructs into one phrase, and B2B marketers buying inventory through agencies or platforms frequently don’t know which type they’re actually transacting on.

If you want one mental model, hold this: PG and Preferred Deals are direct relationships executed programmatically. PMPs are auction restrictions. Curated deal IDs are audience and supply-chain packages activated through a string. They serve different jobs.

Why curated deal IDs changed the game for B2B

Open-exchange programmatic has always struggled with B2B. The reason is structural and worth being plain about.

B2B audiences are small and they are very difficult to locate with standard programmatic signals. A typical enterprise sales motion is targeting somewhere between two thousand and twenty thousand decision-makers across a target account list, assuming they have their ICP really dialled in. Some ABA programmes are much broader, but specifically on ABM, the volumes of individual users are very low. Even when you blow that up to influence the buying committee, you’re rarely above one hundred thousand reachable individuals. Open auction is built to find scale at an efficient price point, not to find precision against tiny audiences. You can spend a hundred thousand pounds in open auction and reach almost no-one in your actual ICP, because the auction has no idea who matters and no incentive to find out. These are the flaws in the underlying programmatic infrastructure - BUT (!) don’t let that put you off, we have the solutions to make this work properly

The traditional answer was to layer third-party B2B data onto a DSP buy: pay a data fee, target a Bombora topic or a 6sense intent signal or a TechTarget purchase intent segment, and hope the auction surfaces the right people. This works, partially. The problem is fee compounding (DSP fee plus data fee plus measurement fee plus SSP fee plus publisher take) and signal degradation (third-party identifiers in 2026 are a fraction of what they were in 2020). You’re still left with a single bloated segment level of granularity in your reporting.

Curated deal IDs start to solve both problems by moving the targeting upstream.

Here’s the workflow. A curator (Audigent and IRIS_TV in CTV, Experian and D&B for B2B firmographics, increasingly specialist B2B operators in display and native) takes audience data, blends it with contextual signals and publisher inventory, validates the supply chain, and packages the result as a single deal ID. The SSP transmits that deal ID to the DSP. The DSP doesn’t need to know which alternate ID matched the user, doesn’t need to look at identifiers in the bid request, doesn’t need to layer separate data fees on top. It just bids against a deal ID that has been pre-qualified upstream. The aggregation layers, or super signal aggregators, use supply logs to output better reporting - so you start to solve the marketing insights piece and the ability to demonstrate marketings impact on pipe. This is a huge step forward.

For B2B specifically, this matters in three ways:

Identity at the audience level, not the user level. Instead of trying to match a cookie to a CRM record at impression time (which fails 60-75% of the time on the open web), the curator pre-resolves the audience using deterministic identity signals (hashed emails, IP-to-business graphs, professional graphs from data partners) and surfaces only impressions where the audience match is already established. You buy reach against a known firmographic audience, not a probabilistic guess. You can do so using any DSP tech you like, without having to build your own or modify existing solutions

Supply path you can actually audit. Curated deal IDs are typically integrated through a single SSP relationship, which dramatically reduces hops in the supply chain. Open auction routinely has four to six intermediaries between advertiser and publisher, each taking a fee. A clean curated deal can compress that to advertiser, DSP, SSP, publisher, with explicit fee disclosure at each layer. This directly means more of your investment goes to working media and data - exactly where you want it working - and not inflating resellers pockets.

Margin protection that aligns incentives. When the curator owns the deal ID, the curator owns the relationship. They are incentivised to keep the audience accurate, the inventory clean, and the fee transparent because the deal ID is the product. Compare that to open auction, where the DSP’s incentive is volume, the SSP’s incentive is fill, and nobody’s incentive is the marketer’s outcome. Data vendors have played this game for years, aligning with volume with no audit level accountability. This new model is a huge flip and a massive win for B2B vendors - it is, I believe, a key reason why the worlds leading B2B tech vendors are leaning very hard on programmatic again now.

The Audigent and Dun & Bradstreet partnership announced in 2024 made over four hundred D&B B2B firmographic segments activatable as Audigent-curated deal IDs across CTV, display, native, audio, and online video. That’s not a niche capability. It’s a structural rewrite of how B2B audiences get bought.

The innovation now, which I am proud to be at the forefront of, is leaning on the extra signal from the supply side, to deploy AI contextualisation at scale in containers to really understand digital content across all mediums (text, audio, video), and align it to machine learning intent models (first party website intent, second party and third party intent from the likes of Bombora), deployed as a marketing operating system, creating live data pipelines via DealIDs, which output CRO level metrics not marketing fluff. This direction of innovation has so much opportunity for B2B marketers to really eek out value from data driven advertising

All of this is a very long way removed from the humble 2017 DealID that we referenced at the start of this newsletter

What most B2B marketers actually do (and why it’s costing them)

I’ve analysed a lot of B2B programmatic stacks over fifteen years, trying to find nuggets of gold that can be brought into how my team operates. The pattern is depressingly consistent.

Pattern one: open auction default. The agency or in-house team sets up a DSP campaign with a third-party data segment, runs it through open auction, and reports back on impressions delivered. No deal IDs in the campaign at all. Performance is mediocre, fees are stacked, supply path is a black box. The marketer doesn’t know what they bought because the report doesn’t tell them.

Pattern two: generic publisher PMPs. The team has been pitched a handful of “premium B2B publisher PMPs” by an agency or a sales rep, signs them off, and runs spend through them. The PMPs are real (Forbes, Bloomberg, the Financial Times) but the audience targeting underneath is just open auction with a publisher whitelist. Better than pattern one, but you’re still not pre-resolving audience; you’re just buying premium environments. That beats a lot of B2B activity that I have seen, especially in tier 2 self-serve DSPs, where the inventory is unbelievably bad - but it still under-sells what can be done when it is done right

Pattern three: bolt-on curated data. The team has heard about curation, has signed up for one or two Audigent or Multilocal deals, but hasn’t reorganised the campaign architecture around them. The curated deals run alongside open-auction buys, with no clean comparison data, no shift in budget allocation, no measurement framework that isolates curated performance. The result is a hedged bet that doesn’t prove anything.

Pattern four (rare): curated-first architecture. The team has restructured their B2B programmatic activation around curated deal IDs as the primary delivery mechanism, with open auction as a fallback for scale rather than a default. They know which curators they’re working with, what audience signals are underneath each deal, what the fee structure looks like, and how to optimise dynamically. This is the version that actually performs, and it’s the architecture that the next two years of B2B programmatic will reward.

If you’re in pattern one or two, you’re not getting what your budget could deliver. If you’re in pattern three, you have the components but not the architecture. The shift to pattern four isn’t expensive, but it does require knowing which questions to ask.

The five questions to ask about any B2B deal ID

These are the questions I ask when auditing a B2B programmatic stack. They will surface most of the issues that matter, fast.

1. What is the audience source, and how is it resolved? Is this a third-party intent segment loaded into an SSP, a first-party CRM match, a deterministic firmographic graph, or a probabilistic lookalike? “B2B audience” is not an answer. You should be able to name the data partner, describe the matching methodology, and see a sample of the resolved audience size. If your curator can’t tell you this, the audience is probably weaker than the deal ID suggests.

2. What is the supply path, and how many hops are in it? From bid request to ad render, how many intermediaries does this impression flow through? Each hop is a fee and a potential failure point. A clean curated deal should be advertiser, DSP, SSP, publisher. If there are resellers or aggregators in the chain, ask why.

3. What is the actual fee structure, broken down by layer? Media cost, DSP fee, data fee, curator fee, SSP fee, third-party verification fee. If the answer is a single percentage (”our take is twenty percent”) you don’t have fee transparency, you have a fee summary. The Strategus framework on the five layers of programmatic cost is the right benchmark; if your partner can’t disclose against it, that’s a finding.

4. Is it auditable at log level? Can you get raw bid logs, win logs, and the metadata that ties impressions back to specific deal IDs? Without log-level access, all your reporting is the DSP’s interpretation of the DSP’s data. The 2017 ANA programmatic study found 15% of advertiser spend was unattributable across the supply chain. That problem hasn’t fully gone away.

5. Can the deal be paused, modified, or optimised dynamically? Some deal IDs are static (PG deals committed to volume, PMPs with fixed parameters). Curated deals should let you adjust audience composition, change frequency caps, swap creative, and shift budget without renegotiating the underlying terms. If you can’t, you’ve signed up for a buy, not a programmatic activation.

If you ask these five questions and your agency, your in-house team, or your platform vendor can’t answer them in plain language, you have a stack problem. Not a budget problem.

Where this is heading

Two near-term shifts are worth watching.

Curated deal IDs are absorbing the data-marketplace model. The traditional DSP data marketplace (where buyers shop third-party segments and pay a CPM uplift) is being structurally disrupted by sell-side curation, because the same data activated through a curated deal ID is cheaper, cleaner, and more auditable than the same data activated through a DSP segment overlay. Expect the data-marketplace model to compress over the next eighteen to twenty-four months, with first-party and curated activation taking share.

B2B-specific curators are an emerging category. Most curation today is generalist (Audigent, Multilocal, Lotame). The next layer is specialist B2B curation: deal IDs built specifically for ABM motions, with firmographic depth and intent overlays designed for long sales cycles. This is where the real margin will be in B2B programmatic over the next three years, because the operational sophistication required is high enough to be defensible.

The deal ID itself will get richer. Sell-side curation is pushing more logic into the deal ID: dynamic audience refresh, bid-level optimisation, brand suitability validation, frequency capping at the account level rather than the cookie level. The deal ID of 2027 will carry more configuration than the deal ID of 2024, and the marketers who treat deal IDs as configurable activation tokens (rather than static inventory packages) will get the lift.

The takeaway

A deal ID is not a premium publisher access pass. It is the configurable activation token for everything that makes B2B programmatic work in 2026: audience precision, supply-chain accountability, fee transparency, and outcome optimisation. If you’re running B2B programmatic without understanding the deal ID architecture underneath your spend, you’re operating without the most important diagnostic tool in the stack.

The good news: this is fixable in weeks, not quarters. The five questions above will surface most of what’s broken in a typical B2B programmatic activation. Asking them is the cheapest, fastest way to upgrade your program before any other change.

Next in this series: programmatic curation explained for B2B, the deeper version of why sell-side targeting has structurally changed what your DSP buys are actually doing.

The B2B Stack is written by Mike Harty, founder of FunnelFuel, a B2B-native programmatic managed service operating across London, New York and Singapore. If you’re auditing your own deal ID architecture and want a second pair of eyes, reply to this email and we can compare notes.

That’s not a dig. It’s the structural reality of B2B marketing content. The people who write about this stack for a living are mostly not the people who operate it. The result is an infinite library of 1,500-word blog posts that describe programmatic the way an in-flight magazine describes turbulence — with more enthusiasm than understanding.

This guide is different because it has to be. I’ve spent fifteen-plus years inside this space — native advertising before it had a name, B2B programmatic before anyone thought to call it that, and now running FunnelFuel, a B2B-native programmatic managed service operating across London, New York and Singapore. What follows is an operator’s view of the 2026 stack: how it actually works, where the money flows, what’s genuinely new, and, most importantly, where it quietly breaks when you try to run B2B through infrastructure that was built for B2C.

If you’re in-housing paid media at a B2B SaaS, running accounts at a B2B agency, or trying to decide whether your current stack is fit for the pipeline numbers on your 2026 plan, this is the full-stack reference to bookmark.

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What we’ll cover:

• The anatomy of the modern programmatic stack — the six layers that actually matter.

• How SSPs and DSPs have converged, and what that means for margin and control.

• Identity in 2026: cookies aren’t dead, but they also aren’t reliable. What is the solution?

• Data at segment level: firmographic vs behavioural, and where the wiring breaks.

• First-party data as a compound-interest asset — not a one-time activation.

• Where real innovation is happening — curation, AI optimisation, new channels, and the OpenAI question.

• The seven places the whole thing breaks when you try to run B2B through it.

• The best platforms for B2B brands, honestly assessed.

• Why the best in-house teams still work with best-in-class managed service — and what “best-in-class” actually means.

Part 1: The Anatomy of the Modern Programmatic Stack

Forget the Lumascape. It’s a useful artefact of ecosystem complexity but a bad frame for understanding what’s happening when a B2B ad serves in 2026.

The stack you need to understand has six layers.

1. Demand side. The DSP (Demand-Side Platform) — The Trade Desk, DV360, StackAdapt, Amazon DSP, Yahoo, Basis. This is where campaigns are built, bids are placed, and optimisation happens. Traditionally competed around strength of inventory access, now more differentiated around unique data sets and capabilities.

2. Supply side. The SSPs (Supply-Side Platforms) — Index Exchange, Magnite, PubMatic, OpenX, Xandr — aggregate publisher inventory and sell it to DSPs via real-time auction or private deal. Evolving towards DSP-lite capabilities and shaping of inventory via curation.

3. Identity and matching. The plumbing that lets a DSP bid on an impression it can actually value. Universal IDs (UID2, ID5), hashed email matching, seller-defined audiences, IP-based identity, device graphs, CTV ACR. The technology architecture which enables data (see below) as well as frequency management and other programmatic bidding and pacing tooling

4. Data. Audience segments, intent signals, firmographic and technographic overlays. The B2B-relevant sources: Bombora, LinkedIn, Dun & Bradstreet, TechTarget, G2, 6sense. The ability to understand the user behind the ad opportunity, and shape bidding, creative and reporting capabilities. Mainly comes in the form of first party data (your own, such as your customers emails) or third party (somebody else’s data, like Bombora).

5. Measurement and attribution. What did the campaign actually do? This is the layer that most quietly hurts B2B marketers — we’ll come back to it.

6. Creative and activation. Dynamic creative optimisation (DCO), display and native formats, CTV, audio, DOOH, in-app. The message that we put in front of the user who we’re targeting

In a simplified buy, the DSP evaluates an impression offered by an SSP, checks whether it matches the campaign’s targeting (identity + data), places a bid in a fraction of a second, wins or loses the auction, and serves creative. The measurement stack then tries to tell you whether any of it worked.

The structural problem for B2B is that every one of these layers was designed to optimise for B2C outcomes, and against a single user based data hierarchy - which does not understand buying committees and accounts. We’ll spend most of this guide on what that means in practice.

Part 2: The Great Convergence — SSPs and DSPs Are Eating Each Other

The tidy separation between “sell side” and “buy side” that defined the Lumascape era is gone. What’s happening in 2026 is convergence, and it has real consequences for where margin sits and how you should think about supply.

On the demand side, The Trade Desk’s OpenPath (launched in 2022) established the direct-to-publisher model for DSPs. In early 2026, TTD expanded this with OpenAds — a next-generation auction platform with publisher commitments from AccuWeather, BuzzFeed, the Guardian and Hearst, among others. The thesis is straightforward: cut SSP take rates out of the path, win more auctions at a lower net cost. Yahoo’s Backstage initiative and Amazon’s Publisher Direct are versions of the same play. The argument extends to the commoditisation of supply - the same publisher will sell their inventory via header bidding via dozens of SSPs. Therefore, the DSP knows they gain a cleaner view of supply without the re-selling and duplication when they connect directly with the worlds best publishers.

On the supply side, SSPs are going buy-side as part of their counter-attack against DSP’s treading on their toes. PubMatic Activate lets advertisers execute direct campaigns through the SSP. Similar capabilities are being rolled out across Magnite and Index. Equativ will give you access to their DSP with a 0% fee when you buy their inventory. The boundary is dissolving.

The endpoint most people in the ecosystem now see coming is the UAP — the Unified Ad Platform — a single system that manages audience, bidding, yield, and inventory across the entire supply chain. We’re not there yet. But the direction is real, and it matters for B2B specifically because:

• Shorter supply paths mean better economics. Fewer middlemen, lower take rates, more of your budget reaching the publisher.

• But they also mean more concentration. When one DSP controls both the bidding and the publisher relationship, you have a single point of negotiation and a single point of failure. In reality you have that with the existing system anyway, because if your DSP falls over, you probably can’t reach your supply today anyway - but it is still worth flagging

• Curation is where the action is. The real locus of value creation in 2026 isn’t the pipe — it’s the audience container that sits on top of it. The deal ID is the new unit of strategy and expect it to get superseded with audience containerisation and bidders (most likely leightweight ones taking advantage of curations power) to start to live IN the containers, essentially creating a UAP. This is a very hot area of innovation

Which brings us to identity.

Part 3: Identity in 2026 — Cookies Aren’t Dead, But They Also Aren’t Reliable

The programmatic ecosystem was architected around third party cookies. They acted as a store of data, enabling DSPs and SSPs to ‘cookie-sync’, allowing the DSP to make more of the SSP’s inventory ‘addressable’ - meaning the DSP could unlock the data it had on each user in the supply ecosystem. This is critical to audience intelligence but also to fundamental DSP capabilities, like frequency management (capping impressions at, for example, a maximum of 5 per user). So the outcry around cookies falling off a clifftop was huge, and consumed the messaging around programmatic for most of the early 2020’s.

In July 2024, Google abandoned its plan to deprecate third-party cookies in Chrome. In 2026, the live state is a “Privacy Choice” prompt that lets users decide — rather than Google deciding for them.

This surprised people who were expecting a cliff. What actually happened was a slow erosion. Safari (Intelligent Tracking Protection) and Firefox (Enhanced Tracking Protection) have blocked third-party cookies by default for years. Combined with ad blockers, cookie-consent opt-outs, and user privacy behaviour, a substantial share of the open web is either cookieless or flagged as low-signal before an auction even starts.

The working identity stack in 2026 looks like this:

• Universal IDs. UID2 (The Trade Desk’s open standard, widely adopted), ID5, LiveRamp’s RampID. These are mostly hashed-email-based deterministic identifiers that work in places where cookies don’t. ID5 is more probabilistic and behaves a little bit differently

• Seller-defined audiences. IAB’s standard that lets publishers expose audience signals directly, bypassing the identity layer entirely for some use cases.

• Contextual and semantic targeting. Not identity at all, but increasingly sophisticated thanks to AI: topic taxonomy, content-level matching, LLM-driven semantic relevance.

• Device graphs and probabilistic matching. For CTV, mobile app, and cross-device stitching where deterministic IDs aren’t available.

For B2B specifically, identity is where the stack starts to wobble. All the universal ID infrastructure is built around consumer email matching. Consumer email is simply a much more persistent signal - you may have your old Gmail from 15 years ago, you probably do not have your work email from 15 years ago. The moment you try to ask “is this impression being served to someone at a target account?” you’re asking a question the universal ID framework doesn’t natively answer.

We’ll come back to this in Part 7. It’s the single most important thing B2B marketers misunderstand about their own stack.

Part 4: Data at Segment Level — Where the Real Margin Leaks

Every B2B media plan in 2026 leans on third-party data segments. Here’s what actually happens when you activate one.

The segment is created by ant one of the plethora of data providers — which could be Bombora for intent, D&B for firmographics, TechTarget for technographics, 6sense and Demandbase for buying-stage signals. That segment is matched (typically via cookie ID, UID2, or IP) to users or devices. The matched segment is packaged into a taxonomy and distributed to DSPs. You target it.

Each step loses signal.

• Match rates degrade. A segment claiming two million accounts in its source data might match 15–30% of that when activated against open-web impression supply. Fewer in cookieless environments.

• Data decay. Intent signal is freshest on day one. By the time it’s flowed through the taxonomy, matched, and hit your DSP, you’re often operating on one- to two-week-old signal. For fast-moving B2B cycles, that’s material.

• Segment aggregation. Most commercial B2B segments are aggregated at person or household level, then inferred back up to account level. That inference is where a lot of precision dies.

• Hierarchy mismatches. If you target “IT Decision Makers at Fortune 500 companies,” the segment is built by joining signals that were never deterministically linked. You’re buying an inference, not a matched audience.

The other huge issue with segments is that you can only target and report at a segment level - and a segment may = 10,000 accounts. So all you get back is impressions, clicks etc at a 10,000 account segment level. No account level data. No ability to shape your bids to match surging engagement or other buying signals coming out of your CRM. This is the problem that has only really been solved by IABM from TradeDesk, Bombora and ChaliceAI, and FunnelFuel (built into SSPs and DSPs as adtech upgrade package for B2B)

The operator move is to treat third-party segments as a directional tool, not a ground truth, and to prioritise paths where the signal is closer to source. That means:

• Direct relationships with data providers where possible. Raw signal access beats pre-packaged segments.

• Curated deal IDs that align inventory, context, and audience in one package. The Audigent, D&B, and Experian curation stacks are instructive. Over 400 Dun & Bradstreet B2B audience segments are now activatable via Audigent-curated deal IDs across CTV, display, native, audio, and online video.

• First-party data layered on top of third-party segments, not replaced by them.

Which brings us to the compound-interest play most B2B marketers still under-index on.

Part 5: First-Party Data — The Asset You Actually Own

If you take one thing from this guide: the B2B marketers winning in 2026 are the ones who have built first-party data into the core of their programmatic activation, not bolted it on as an afterthought.

Third-party data is rented. It’s degrading. It’s increasingly expensive, matched to shrinking pools of addressable users. The quality cannot easily be audited and the segment owners are incentivised by their cost per thousand revenue model, to inflate the segment size. First-party data is the opposite on every dimension. It compounds, it’s owned, and match rates to your own CRM and MAP are structurally higher than anything you’ll ever buy.

The B2B first-party data playbook in 2026:

1. Build the pipes first. Your CRM (Salesforce, HubSpot, Dynamics) and MAP (Marketo, HubSpot, Pardot) are the source of truth. Get the identity graph clean before you try to activate any of it. Hashed emails, IP data, form-fill metadata, LinkedIn unique IDs, D-U-N-S numbers — these are your activation tokens.

2. Segment by intent stage, not just ICP. Early-stage research visitors behave differently from BDR-engaged accounts, which behave differently from late-stage opportunities. Build segments that match how your pipeline actually moves, then activate each with a different media strategy.

3. Activate through multiple surfaces. Customer match in programmatic and LinkedIn is the obvious layer. Less obvious but more powerful: activating hashed CRM records directly into the DSP, matching them via UID2 or IP-based identity, and using them as targeting seeds or suppression lists across your full media mix.

4. Measure back to source. Every first-party activation should close the loop into your CRM. Exposed accounts, engagement scores, pipeline progression — all of it should live in the system the sales team already looks at.

This is where B2B-native measurement earns its keep. We’ll get to that in Part 7.

Part 6: Where the Innovation Is Actually Happening

A lot of what gets sold as innovation in B2B adtech is features dressed up as revolutions. Here’s what’s actually moving in 2026.

Curation and audience containers

The single biggest shift in how media is bought and sold is curation. Experian’s 2026 State of Advertising report makes the case plainly: programmatic is shifting from broad open-exchange access to curated, performance-ready supply paths.

For B2B, this matters because curation is finally solving a problem that open-exchange programmatic has always had: matching the right firmographic audience to the right contextual environment at the right price, in a way that a self-serve DSP seat simply cannot.

Curators like Audigent, Experian, and specialist B2B operators are building audience containers — curated deal IDs — that combine vetted inventory, joined data, and margin protection in a single activation token. The advertiser buys the deal ID; everything underneath is pre-negotiated.

The strategic implication: in 2026, if you’re running B2B programmatic at any scale and you’re not operating through curated deal IDs, you’re leaving both performance and margin on the table.

AI optimisation — the real version

Every DSP has been calling everything AI for five years. What’s actually new in 2026:

• Bid-level optimisation models that learn auction-by-auction rather than campaign-by-campaign, materially reducing wasted impressions on B2B activations.

• Creative optimisation — LLM-assisted DCO that adapts copy to buying stage, firmographic segment, or account intent level.

• Semantic context matching — moving beyond keyword-and-category contextual targeting to LLM-evaluated page content. This is dramatically better for B2B, where relevant context is often nuanced (”a CFO article about working capital cycles,” not “finance vertical”).

• Audience expansion and lookalike modelling on first-party seeds — genuinely useful now that the models can operate on structured firmographic dimensions rather than just behavioural ones.

The test for any AI feature: does it demonstrably reduce waste or improve outcomes against a first-party-measured baseline? If it doesn’t, it’s a pitch deck.

Formats and channels

CTV is now fully programmatic and fully relevant to B2B — the “B2B decision-makers watch CTV too” argument has won and paid search money is moving over on mass. DOOH has become more targetable via data-linked screens, especially in business districts, and ABM versions of it can now be run globally. Audio — podcast advertising, Spotify DSP inventory — continues to grow, and is probably the single most under-represented audience pool considering the latest stats show that ITDM’s spend upwards of 54 minutes a day on podcasts. Retail media, which has dominated the B2C conversation for three years, is still mostly irrelevant for B2B unless you’re selling into commerce.

The interesting wildcard is CTV plus account targeting. This is hard — CTV identity is usually household- or device-based rather than person-based — but curation stacks combining IP-based account identity with CTV supply are making genuine account-level CTV targeting viable for the first time. Campaigns are showing incredible promise for B2B lead generation when optimised against attention metrics

The OpenAI question — and the new conversational surfaces

The event that every B2B marketer should be paying attention to: OpenAI launched paid advertising in ChatGPT on January 16, 2026, initially on Free and Go tier users in the US. The entry threshold is enterprise-grade — reported at roughly $200,000 committed beta spend — and Criteo became the first adtech integration partner in March 2026. Google has committed to bringing ads to Gemini in 2026. Perplexity has been building sponsored content for some time.

What this means for B2B: a new set of conversational AI surfaces is becoming an advertising channel, and the first movers will learn more about how to use them than anyone can extract from case studies later. For B2B specifically, these surfaces are particularly interesting because they intercept intent at the research stage — exactly when the buying committee is forming its options. And we know upwards of 90% of B2B buying journeys are now shaped or at least influenced by LLM ‘co-pilots’

None of this is ready for primary spend in 2026. But it’s ready for structured experimentation, and the marketers who will have an advantage in 2027 are building their testing programs now.

Part 7: Where the Whole Thing Breaks for B2B

This is the section most “programmatic for B2B” guides never write, because writing it requires admitting that the infrastructure is mostly not built for the job you’re doing.

This is why I theorised in late 2020 that the worlds best adtech needed an ‘upgrade pack’ - a tech layer that would upskill its capabilities for B2B WITHOUT building a homebrew DSP. The wheels and engine didn’t need re-building but the data models sure did. Anyone trying to run programmatic B2B will be familiar with the below challenges

1. No native account understanding. Every mainstream DSP operates natively on person, device, or household as the unit of targeting. Account is a derived layer, not a native one. When you target “IBM” in your DSP, what’s really happening is pattern-matching of IPs, known personas, and firmographic-tagged identifiers — with all the noise that implies.

2. Buying committees are invisible to the stack. B2B purchases are made by committees of six to ten people. A programmatic stack that optimises for individual-level engagement will systematically under-value the multi-touch, multi-person pattern that actually indicates pipeline movement. Most platforms cannot tell you that five different people at a target account engaged with your brand in the last 30 days — because they were never built to care about that.

3. Data hierarchy mismatches. Third-party B2B segments are built in a B2C taxonomy and retrofitted. The decision-tree logic (”IT Decision Maker at 1000+ employee company in Finance”) sits on top of consumer-identity infrastructure that wasn’t built to carry firmographic weight. Activation works; precision is largely theatrical.

4. Segment-level scale gaps. Commercial B2B segments lose 50–70% of their inferred scale between source and DSP activation once you account for match rates, freshness decay, and unique-user deduplication. The numbers on the rate card are almost never the numbers that reach impression delivery.

5. Reporting on delivery, not account progression. The DSP will happily report impressions, clicks, CTR, viewability, and CPM. It will not tell you which target accounts are more engaged this month than last, which have moved from cold to research stage, or which are showing buying-committee patterns. That’s the report B2B marketers actually need. Almost no platform produces it natively.

6. Attribution designed for last-click B2C. Most DSP attribution is a variant of last-click or probabilistic multi-touch. For a B2B buyer with a six-month sales cycle, that’s close to useless. Without offline conversion import, CRM integration, and account-level modelling, you’ll spend 2026 optimising against signals that don’t correlate with pipeline.

7. The margin stack is hidden. Between DSP seat fees, SSP take rates, data costs, curator margins, verification fees, and any agency or managed-service layer, the real working-media rate on a typical B2B campaign is often 40–60% of gross spend. Most in-house teams don’t know their own number. The ones that do win the budget arguments.

This list isn’t a pitch for any particular solution. It’s the structural reality of running B2B through infrastructure that wasn’t built for it. Once you see it clearly, you stop asking “how do we do programmatic better?” and start asking “how do we build around these gaps?”

Part 8: The Best Platforms for B2B Brands in 2026

There is no single best platform. There are platforms that solve specific parts of the B2B problem, and the art is assembling the right combination. An honest tour:

DSPs worth knowing

• The Trade Desk. The best independent DSP. Deep supply relationships, OpenPath and OpenAds direct-to-publisher access, strong identity infrastructure (UID2), genuinely programmatic CTV, solid measurement API. Weak on native account-level reporting without an overlay.

• DV360. Scale, YouTube integration, Google’s identity graph. Data operations are increasingly walled. Fine for B2B if you’re willing to accept Google’s measurement worldview as ground truth.

• StackAdapt. Strong self-serve, good display and native, increasingly competitive CTV. Popular with agencies and mid-market B2B. Less deep on enterprise data integrations.

• Amazon DSP. Unique data asset via Amazon’s retail graph. Relevant for B2B companies selling into commerce or with distributed supply chains. Niche otherwise.

B2B-specialised ABM platforms

• 6sense and Demandbase. The incumbents. Both offer integrated intent data, account identification, and media activation. Powerful, expensive, and often positioned as replacements for a full stack when they’re really one layer within one. Media delivery is rarely best-in-class even when the signal layer is strong.

• RollWorks. Solid mid-market ABM, good pipeline integration.

• Terminus. Continues to evolve; historically strong on ABM orchestration.

• Metadata. AI-driven campaign automation. Useful for smaller teams without in-house media operators.

• Influ2. Person-based advertising; narrow but differentiated niche.

The honest reality: no single one of these platforms solves the full B2B programmatic problem end-to-end. The ABM platforms give you account signal but constrain your media surface. The DSPs give you media surface but can’t give you account-level reporting natively. The gap between them is where most B2B marketers quietly burn budget.

Part 9: Why the Best In-House Teams Still Work With Managed Service

Here’s a pattern I’ve watched for 15 years — and especially sharply for the last five: the smartest, best-resourced in-house B2B marketing teams don’t move to full in-housing for programmatic. They in-house the strategy, the brand, the planning, and the measurement — and they work with a best-in-class managed service for the execution layer.

This is counter-intuitive if you’ve read the consultancy pitches that frame in-housing as primarily a cost-saving exercise. It makes sense the moment you look at what “managed service done properly” actually delivers.

1. Access, not outsourcing. The right managed service partner isn’t renting you a trader. They’re giving you access to supply relationships, curated deal IDs, raw data partnerships, and tech integrations that an in-house team — no matter how well-funded — cannot economically replicate. Raw signal-level data access (the kind FunnelFuel built with Bombora as only the second company to hold that level of integration) is the clearest example, but it’s not the only one. Supply-side relationships, curation stacks, and first-look deal pipelines are all asymmetric assets that a managed service can offer structurally.

2. Expertise that compounds across clients. A managed service working across dozens of B2B campaigns sees patterns an in-house team running only its own activity never can. What works for a cybersecurity vendor informs what works for a fintech, which informs what works for a SaaS marketing platform. That cross-sectional pattern recognition is impossible to recreate internally.

3. Margin protection through scale. Managed services that operate on the supply side — curating their own deal IDs, negotiating directly with SSPs, holding raw data partnerships — can deliver working-media rates that an in-house team cannot match at typical scale. The case for in-housing almost always assumes the in-house team can operate at specialist margins. It almost never can.

4. Account-level measurement as default. A B2B-native managed service builds the HVA scoring, the account progression reporting, the buying-committee detection — because that’s the job. A DSP seat gives you impressions; a managed service gives you a pipeline story.

5. Focus. An in-house team’s “programmatic specialist” is usually also their email specialist, their paid social strategist, and their webinar operator. A dedicated B2B programmatic partner has one job and does it across their waking hours.

The litmus test: does your managed service get you results, measurement, and access that you provably cannot replicate in-house at the same cost? If yes, it’s not a cost — it’s a capability. If no, it’s a cost, and you’re right to in-house.

The 2026 B2B Programmatic Scorecard

Seven questions to ask against your own stack:

• Supply path. Are we buying through curated deal IDs, or mainly through open exchange?

• Identity. Do we have a working first-party identity graph, and are we activating it programmatically?

• Data. Do we have direct or raw-signal access to the data providers that matter for us, or are we buying pre-packaged segments through three layers of resale?

• Measurement. Can we report on account progression, not just delivery?

• Channel mix. Are we running multi-channel (display, CTV, native, audio) from a single orchestrated plan, or stitching together disconnected activations?

• Innovation surface. Are we running structured experiments on emergent surfaces — curation innovations, AI optimisation features, ChatGPT/Gemini advertising — or waiting for them to become proven?

• Margin. Do we know our true working-media rate after all fees, and is anyone held accountable for improving it?

Clean answers to all seven puts you ahead of 95% of B2B brands. If you don’t have them, that’s the honest gap analysis — and it’s where 2026 budget should go first. Hit reply to this email if you need help going through any of this

Where This Leaves You

Programmatic in 2026 is more powerful than it’s ever been and more dangerous to run on autopilot than it’s ever been. The stack has converged, matured, and specialised simultaneously. The gap between B2B marketers who operate it intentionally and B2B marketers who let their DSP seat run on defaults has never been wider — and it will widen further as AI optimisation, curation, and emergent channels like ChatGPT ads pull the front of the pack further ahead.

The point isn’t that programmatic is hard. It’s that programmatic for B2B is specifically hard, because the infrastructure isn’t natively built for the job. The marketers who win in 2026 aren’t the ones with the best DSP seat. They’re the ones who understand the gaps clearly enough to build around them — and who partner with the people who’ve already built the scaffolding.

If you’re running B2B programmatic in-house and want to pressure-test your stack against what we’ve covered here — supply path, identity, data, measurement, margin — reply to this email. It comes straight to me and we can get into it. I read every response, and these conversations are usually the most interesting part of my week.

And if you want more of this — full-stack B2B adtech analysis, written by someone who’s actually operating the stack — you’re in the right place. Hit subscribe if you haven’t already, and forward this to the one person on your team who’d genuinely benefit from reading it.

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Mike Harty is the founder of FunnelFuel, a B2B-native programmatic managed service operating across London, New York, and Singapore. The B2B Stack is his practitioner-led newsletter for B2B marketers, agencies, and operators working in programmatic.

No big M&A. No earnings shockers. No fresh Trade Desk audit drama (mercifully, since that has been the only story for a month). But pull the threads from this week together, and what you actually saw was the operating layer of programmatic advertising being rewritten in front of your face.

OpenAI flipped ChatGPT ads from CPM to CPC and dropped the floor from a quarter of a million to fifty grand. The Trade Desk shipped its first live agentic product with a named agency. PubMatic wedged custom creative into its agent OS. Magnite lost its CFO and its CPO inside a fortnight. Digiday’s quote of the week: the days of bloat are over.

This is what an industry restructure looks like when it is happening to the plumbing. Quietly. On a Tuesday.

If you sit on the demand side of B2B media, here are the four stories that actually move the road in front of you.

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1. OpenAI: from experiment to ad platform, in eleven weeks

The story. On 21 April, OpenAI flipped its ChatGPT ad pilot from CPM (cost per thousand impressions) to CPC (cost per click), with bids set in a $3 to $5 range. The minimum spend dropped from the original $200K to $250K floor down to $50K. A self-serve ads manager went live. The pilot is now reported at $100M of annualised revenue after roughly six weeks of live inventory, with internal targets of $2.5B in 2026 and $100B by 2030. The CPM model collapsed because launch pricing of $60 eroded to as low as $25 inside ten weeks. International pilots in Canada, Australia and New Zealand are next. Sources: The Information (15 April), Digiday (21 April), Reuters.

The ads manager is covered by PPC land here

My take. Forget the corporate framing of “structured pilot phase.” This is the moment ChatGPT became an actual ad channel. CPC pricing tells you everything: OpenAI could not sustain the CPM, so they are pricing on intent. $3 to $5 CPC is materially cheaper than LinkedIn (which routinely runs $8 to $15+ for B2B targeting) and broadly competitive with Google Search at the lower tail of B2B SaaS keywords.

For B2B marketers, the implication is uncomfortable. Your buyers spend a meaningful share of their vendor research time inside ChatGPT. That has been your dark funnel. As of this week, that funnel has paid placements in it that sit beside, but separate from, the AI-generated answer. Two truths follow. One: the dark funnel just got brighter on the buyer side and more contestable on the seller side. Two: if your competitors get into the pilot before you, they are training the relevance algorithm against your category. Early advertisers will accumulate algorithmic memory you cannot retroactively buy.

The catch: ChatGPT still has no proper attribution layer, no real targeting beyond contextual signal, and CTRs are reportedly weaker than search (not that we should waste energy worrying about that). Nobody has cracked B2B measurement on it yet. But “weaker than search” is not “doesn’t work” — it is the same noise you heard about LinkedIn lead gen in 2014. Get test budgets in now. Treat it as a research and brand touchpoint, not a pipeline driver, and you will not be embarrassed when your CMO asks in Q3 why you have not done anything about it.

On a personal level, I was really hoping against all hope that ChatGPT would embrace programmatic - marrying the best transparent data layering in the ad game with one of the most exciting emergent surfaces that I have seen in my career. I understand agencies have been pushing for CPC and are keen to re-stock the lost search clicks. This one may still have some miles to cover before the final shake out emerges so lets keep a watching eye

2. Agentic moves from slide deck to live deployment

The story. Three things landed in the same week. The Trade Desk launched Koa Agents in alpha with Stagwell as the named agency partner, plus an “Open Agentic Kit” integration framework (Ad Age, AdTechRadar, 21 April). PubMatic integrated its Creative Innovation Suite across AgenticOS, bringing custom creative formats into the agent workflow across CTV and mobile (PubMatic, 22 April). Yahoo, Basis and Magnite have all shipped agentic capability inside the past quarter. The IAB Tech Lab is now maintaining an Agent Registry of vetted tools.

My take. The real story is the pace of standardisation. Six months ago, “agentic” was a slide. This week it is a live feature in the major DSPs (demand-side platforms) and SSPs (supply-side platforms), with cross-industry protocols emerging. What is quietly happening is that buyer agents and seller agents are starting to negotiate inside the same frameworks, and the human in the loop is moving from execution to oversight.

For B2B this matters more than the consumer use cases the trade press leads with. B2B has long sales cycles, complex buying committees, and account-level orchestration that benefits enormously from a system that can plan, activate, optimise and report in one loop. The bit no vendor will tell you: agentic only works if the upstream signal is clean. If you feed an agent the same noisy account intent scores you have been feeding human planners, it will burn through budget faster, not smarter. The premium on raw signal quality, rather than processed platform scores, is about to get higher. This is something I have been arguing for two years. We may finally have the market context for it to land.

3. The independent ad tech squeeze is real

The story. Magnite, the largest independent SSP, announced the planned retirement of CFO David Day on 20 April, three weeks after CPO Adam Soroca departed on 8 April. Digiday’s Tuesday Ad Tech Briefing led with the headline that the days of bloat are over, and reported that independent ad tech players are facing a binary choice of bulking up or flaming out under combined LLM and walled-garden pressure (Digiday, 21 April). Hearst News also selected Magnite as preferred deal partner for high-impact formats the same week, hinting at where the surviving indies will need to specialise.

My take. Read this alongside the OpenAI story and a clear pattern emerges. The middle of the ad tech stack — independent DSPs, SSPs, DMPs (data management platforms), identity resolution layers — is being squeezed from above by LLM-mediated buying surfaces (ChatGPT, Gemini, eventually Claude) and from below by Amazon, Google and Meta consolidating end-to-end.

For B2B buyers this is significant because most of the genuinely B2B-specific tooling sits in that squeezed middle. The 6senses, Demandbases, Bomboras and StackAdapts of the world are not Magnite, but they live in the same gravitational field. Expect more M&A, more “strategic refocusing” press releases, and quiet feature deprecations through 2026. If you are renewing a multi-year contract with a B2B point solution this quarter, ask hard questions about cash runway, agentic readiness and acquisition exposure. The roadmap they show you in the QBR is worth less than the cash position on their last filed accounts.

4. LinkedIn quietly raises the content bar (and ends the AI thought-leadership slop)

The story. LinkedIn confirmed that from 22 June it will end spontaneous live streaming and require scheduled events. In the same announcement cycle this month, the platform said it will downrank repetitive click-driven posts and recycled thought-leadership content that does not add substance, and is improving its semantic relevance detection (LinkedIn Marketing Blog, summarised by ALM Corp, mid-April).

My take. This one went under the radar because everyone was watching OpenAI, but for B2B marketers it may matter more in the medium term. The AI-generated thought-leadership flood of the last eighteen months has clogged the LinkedIn feed with what is best described as adjective soup. LinkedIn now has the semantic models to detect it, and a commercial reason to suppress it (engagement on hollow content depresses session length). The platform is signalling that specificity, expertise and substance get distribution, and that everything else gets buried. I was talking about this on LinkedIn earlier this week - click to see full post below

The implication for B2B teams running organic and Thought Leader Ads: the ROI on volume is collapsing, and the ROI on actual practitioner authority is rising. If your content programme is currently a prompt-and-publish pipeline, you are about to find out. If you have genuine operators on staff whose voices are being underused, this is the moment to put them out front.

Looking ahead

POSSIBLE in Miami next week (27 to 29 April) will be the first major US gathering since the OpenAI CPC switch. Expect every DSP, agency holdco and intent data vendor to have a slide on what it means for your media strategy. Most will be bad. The interesting people to find at the bar are the ones planning their first ChatGPT test budget, not the ones explaining why nothing has changed.

Cannes Lions agenda also dropped this week. Agentic, AI commerce and “the open web” are the three structural themes. The continued absence of B2B as a named track tells you most of what you need to know about how the global ad industry still sees us.

One question I am sitting with this week

If ChatGPT can quietly become a $2.5B ad business in twelve months on bidder primitives borrowed from Google circa 2010, what does that tell you about how much of your stack’s complexity has been protecting margin rather than creating value?

If you want my answer, hit reply.

Have a good weekend.

— Mike

Sources

• The Information, OpenAI shifts ChatGPT ads to CPC, 15 April 2026

• Digiday, OpenAI’s ads manager: a closer look, 10 April 2026

• Digiday Ad Tech Briefing, the days of bloat are over, 21 April 2026

• Ad Age, Trade Desk launches AI agents with Stagwell as client, 21 April 2026

• AdTechRadar, The Trade Desk leans into agentic AI with Koa Agents, 21 April 2026

• PubMatic / Advanced Television, Creative Innovation Suite within AgenticOS, 22 April 2026

• Magnite 8-K via StockTitan, CFO David Day plans retirement, 20 April 2026

• ALM Corp, Digital Marketing News April 1 to 10 2026 (LinkedIn updates summary)

• OpenAI, Testing ads in ChatGPT, updated 26 March 2026

What you’ll learn in this article

• Why The Trade Desk’s new Performance Mode, now in closed beta, is a well-engineered product aimed at the wrong problem for B2B marketers

• The three-layer data hierarchy (global account → buying committee → individual stakeholder) that B2B actually runs on, and why every DSP in the market operates on only one layer

• How auto-optimisation against 10–18 month B2B sales cycles systematically pushes spend toward the wrong impressions, no matter how strong the underlying AI

• What a B2B “adaptation layer” sitting on top of a DSP actually has to do: identity resolution, committee clustering, HVA scoring, curated supply, and pipeline tie-back

• Why the Publicis and Omnicom fee audits are the surface debate, and where the deeper architectural argument for B2B actually sits

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In early April 2026, Digiday confirmed what four ad executives had already been whispering: The Trade Desk’s new Koa Adaptive Trading Modes had moved into closed beta. The product was announced to investors in September 2025, and its a fascinating aside that an all-in-one priced solution has launched into a headwind around transparency. Anyhow, the outcome (pun intended) is that advertisers can now choose between Performance Mode - where TTD’s Koa AI automatically manages bidding, pacing, inventory prioritisation and data application, with media, data and tech fees rolled into a single CPM - and Control Mode, where traders keep manual control over every lever.

The product was first announced in September 2025 alongside Audience Unlimited, TTD’s third-party data marketplace overhaul. Performance Mode bundles Audience Unlimited at no extra cost; Control Mode charges tiered rates of 3.3% or 4.4% of impression costs. As of February 2026, Control Mode is the platform default but Performance Mode is the one TTD is actively steering advertisers towards.

The timing is awkward. Trading Modes have gone live in beta in the same quarter the industry has spent auditing TTD for fee opacity — Publicis first, Omnicom second, with Publicis publicly advising clients to avoid TTD’s stack while the review ran. That backdrop is what most of the trade press has focused on. It is not the interesting story but frankly, simply reading the paragraph above this one - these complex named products with 3.3% extra fees here and 4.4% there gets to the heart of the debate triggered in these audits - the fee accruals are complex and the traders with their hands on their keyboards getting battered internally to drive better outcomes, can easily end up toggling on far more fee generative products then they maybe realise. Long gone are the DSP days of a bid reduction and some data costs.

The actually interesting story, if you work in B2B, is that Performance Mode will optimise brilliantly against a universe that was never designed to carry the structure B2B actually runs on. The fee debate is a surface argument. The data architecture underneath is the real one.

TradeDesk remains the best DSP for B2B marketers, but as I have constantly argued here and on LinkedIn, there is no genuinely turnkey DSP for B2B - and that boils down to data hierarchy at its core. Before that though, what is Performance Mode?

What Performance Mode actually is

Setting the commercial debate to one side, the product itself is well engineered.

Koa — TTD’s optimisation AI — has been evolving for years. What Performance Mode does is give it full agency (control): dynamically optimising bids and allocation within buyer-set guardrails, pulling in Audience Unlimited, Predictive Clearing, Identity Alliance, Prism and measurement in concert, all continuously optimised toward the outcomes the advertiser has defined. TTD’s own framing is that Performance Mode “activates the best performance features for every impression” while keeping strategy “firmly in human hands.”

For a consumer brand buying open-internet reach against tens of millions of addressable users, this is a genuine step forward. The state of the art in DSP-led optimisation is, on balance, better than what most trading teams can achieve manually across fragmented inventory. Public commentary from TTD’s partners supports that reading. This levels the playing field between what say an in-house brands team can achieve with the TradeDesk and what specialist B2C trading arms like MiQ and Goodway can achieve. Its codified the best traders and weaponising vast machine learning and AI to pour over vastly more data then any human can use. Its genuinely very exciting for what programmatic can achieve in driving performance alongside other consumer channels.

So this is not a piece arguing that Performance Mode is a bad product. It isn’t.

It is a piece arguing that it’s the wrong shape of product for B2B — not because of the optimisation layer, but because of what sits beneath it.

The foundational data model that programmatic has never built

Every programmatic platform, TTD included, is built around a single unit of targeting: the individual ID. This construct was the centre of attention around the cookie-apocolapse, movement towards unique IDs and other identity solutions and the eventual reversal of Chrome’s deprecation. This was existential because programmatic is built around individual a-placement auctions to individual users.

So whether you are identifying that individual user using cookies, mobile device IDs (MAIDS), hashed emails, CTV identifiers, Unified ID 2.0 — the format changes, but the unit doesn’t. The DSP sees an individual, applies audience logic to that individual, bids on that individual’s impression, and measures an outcome attached to that individual.

That model works because consumer advertising is, fundamentally, an individual-level problem. One person researches an air purifier. One person buys a new pair of jeans. One person installs an app.

B2B is not an individual-level problem. B2B is a hierarchical, multi-tier account problem, and the hierarchy is non-trivial. It is in-fact very complex and thre i sno evidence with any changes int eh way of the world and work with the like sof AI, that this is getting any simpler.

A typical complex B2B purchase, by the available evidence, looks like this:

• The global account. The company you’re trying to win. Often multi-entity, multi-region, with subsidiary relationships and parent-company rollups that matter for deal attribution.

• The buying committees inside that account. Gartner’s widely cited research puts the typical B2B buying group at six to ten decision-makers for complex solutions, and Forrester’s 2024 State of Business Buying puts the average complex B2B purchase at 13 stakeholders, with 89% of buying decisions crossing multiple departments. In enterprise technology, committees regularly exceed 20 people. Crucially, these are not one committee, they are several: procurement, IT, finance, security, the end-user business unit, legal. Each runs its own parallel evaluation. Potentially multiple committees live for multiple procurement exercises all running at once

• The individual stakeholders inside each committee. The people the DSP can actually see. Each with their own role, seniority, influence weight, content preferences, and position in the buying cycle.

Three layers minimum. In regulated, multi-entity enterprise deals, sometimes four or five.

Programmatic’s data model has one layer. The individual.

There is no native concept of account in the DSP. No native concept of buying committee. No way to express, in the platform’s optimisation logic, that three engaged stakeholders inside one target account are worth more than thirty random individuals scattered across the open web. No way to tell the bidder that a finance VP engaging after IT and the end-user BU have both engaged is a materially different signal than a finance VP engaging in isolation.

This is not a flaw in The Trade Desk specifically. It’s the shape of the entire programmatic stack. DV360, Xandr, Amazon DSP, Yahoo DSP — same single-layer model. It is the inherited architecture of a consumer-advertising industry that never had to think in terms of accounts because it was never pricing against accounts.

Why Performance Mode magnifies the problem rather than solving it

Here is where the argument sharpens - or to put it better, the problem becomes real if you are trying to use this for B2B

When you hand optimisation to an AI co-pilot, you are making a bet that the data the AI is optimising against is the right data. In consumer, it is. Koa is optimising toward an outcome (a purchase, an app install, a lead form completion) that is genuinely attached to an individual, inside a short feedback loop, with a clear signal. Their tracking captures it.

In B2B, the optimisation target is fundamentally different:

• The outcome is account-level, not individual-level. The “conversion” that matters is a deal closing inside the target account — potentially 10 months later on average for B2B, 12+ months for enterprise deals with large committees, and 3–6 months for mid-market software rising to 9–18 months for enterprise. No real-time bidder can feedback-optimise against that timescale.

• The short-term proxy signals are misleading. Clicks, lead forms, content downloads, demo requests. Koa will optimise against these because they’re all it has. But in B2B, the individuals clicking are frequently not the individuals in the buying committee — they’re adjacent researchers, competitive analysts, consultants, or junior staff running background work. Optimising toward “more clicks from this kind of person” pulls spend away from the harder-to-reach decision-makers who actually matter. This is before you realise how the DSP uses a load of segments from data vendors like Eyeota - AND before you realise the tooling like Predictive clearing and just how much that pulls against trying to find the right person in the right account at the right moment. Its really very flawed for this use case

• The addressable universe is structurally narrow. Not 50 million consumers. Five hundred to five thousand target accounts, containing perhaps 20,000 to 50,000 relevant individuals across committees. Inside that narrow universe, auto-optimisation against cheap CPMs will systematically prefer the impressions that are easiest to win — which are almost always the wrong ones.

The result is predictable: Performance Mode, applied to a B2B brief, will deliver strong-looking dashboards against consumer-grade metrics while the account-level pipeline underneath stays flat. The AI is not failing. It is doing exactly what it was built to do. It just has nothing useful to chase, because the data model beneath it doesn’t hold the structure that B2B outcomes actually live in.

This yet again is why sophisticated teams need to reject vanity metrics in B2B and with this type of auto-optimisation running behind the scenes, you’ll be burning budget and zeroing in on terrible outcomes

The adaptation layer: what has to exist before any optimiser adds value

This is the work.

Before any DSP-side AI can meaningfully optimise for B2B, someone has to build an adaptation layer that re-hierarchises the data. That layer has to do several things the DSP cannot:

1. Resolve individuals into accounts. Every impression, every engagement signal, every content interaction needs to be tagged not just to a person but to a company, and ideally to a business unit within that company. This is non-trivial identity work — it draws on firmographic resolution, IP-to-company mapping, B2B identity graphs, CRM integration, and increasingly AI-driven entity resolution against unstructured intent data.

2. Assemble individuals into committees. Once accounts are resolved, individuals have to be clustered into the functional committees doing the buying. IT, finance, procurement, business unit owners, champions. The committee is the unit that predicts pipeline progression — not the individual.

3. Score engagement at the account level, not the impression level. An engagement from three stakeholders in two committees is a qualitatively different signal from an engagement from three stakeholders in the same committee, which is different again from twenty engagements from one junior researcher. The scoring logic has to reflect committee shape, seniority, and cross-committee coverage.

4. Tie the whole thing to pipeline. Marketing-influenced pipeline, not clicks. That requires CRM integration deep enough to close the loop on 6–18 month cycles — which means building the kind of longitudinal view no DSP has, because DSPs are built around short-loop optimisation.

5. Curate the supply side. Not every impression is worth the same bid inside a 2,000-account universe. Curated deal IDs, premium B2B supply, private marketplaces built around professional context — all matter far more in B2B than in consumer, because the cost of wasting budget on irrelevant impressions is an order of magnitude higher.

Once that adaptation layer is in place, and only then, the things B2B marketers actually need become possible:

• Account-level progression, not individual clicks. Measuring whether a target account is advancing through stages of awareness, consideration and preference, across the buying committee, not just whether one person filled in a form.

• High-value action (HVA) scoring. Identifying which specific engagement patterns correlate with pipeline creation months later, and weighting the bidding and targeting accordingly.

• Marketing-influenced pipeline, measured properly. Closing the loop from impression to committee engagement to opportunity to closed-won revenue, at the account level.

• Automated bidding at the account level. Not at the impression level. Bidding logic that says “this account has shown early-stage committee engagement but no finance-side touch yet, push spend toward finance-titled inventory this week” — the kind of logic no DSP-native AI can generate because no DSP-native AI knows what a committee is.

None of these capabilities can be built inside the DSP, because the DSP’s data model doesn’t carry the hierarchy. They have to be built on top.

This is not a criticism of The Trade Desk. It’s a product boundary.

It’s worth being precise here. The Trade Desk is the best independent DSP in the market. Koa is a genuinely strong optimiser. Audience Unlimited is a real simplification of a previously painful data-activation workflow. Performance Mode will, by every indication, deliver meaningful efficiency gains for the consumer advertisers it was designed for.

What it cannot do and what no DSP can do, is carry the foundational data structure B2B depends on. That structure has to be built externally and applied to the DSP from outside.

This is the category FunnelFuel has been operating in for the 4 years. A managed-service layer sitting on top of TTD (and other best-of-breed infrastructure: Index Exchange on the supply side, Bombora on the intent data side, CRM systems on the pipeline side), with the account/committee/individual hierarchy modelled explicitly, HVA scoring running against it, account-level reporting sitting on top, and curated B2B supply underneath. This is not building another DSP. It is adapting the worlds best DSP’s to a problem they weren’t designed for.

The reason this matters right now, in the week Performance Mode has entered beta, is that a lot of B2B marketers are about to be sold a story. The story will say: “Turn on Performance Mode, let the AI drive, the results will improve.” For consumer, it will. For B2B, without the adaptation layer underneath, the AI will confidently optimise toward the wrong thing — and the flatness in pipeline will arrive six months later, uncorrelated in the dashboards with the decision that caused it.

Performance Mode is a great product for the problem it was built to solve.

In B2B, the problem is one layer deeper.

That layer has to be built before any optimiser, however good, has anything useful to chase.

Mike Harty is the founder of FunnelFuel and publishes The B2B Stack. FunnelFuel runs managed-service B2B programmatic on best-of-breed adtech infrastructure, including The Trade Desk, with account-level analytics, HVA scoring, curated deal IDs and the adaptation layer described above.

Further reading / sources

• The Trade Desk is changing how advertisers buy — and what they can see — Digiday, 6 April 2026

• The Trade Desk Announces Major Overhaul of Digital Advertising Data Marketplace — TTD Investor Relations, 29 September 2025

• Performance Mode: AI-powered trading — The Trade Desk

• The Trade Desk and Publicis split, ad tech community divided — Ad Age, 23 March 2026

• Omnicom launches audit of The Trade Desk’s fees — Ad Age, 24 March 2026

• Publicis vs. The Trade Desk isn’t really about transparency — it’s about who gets the margin — Digiday

• Gartner research on B2B buying committees (6–10 decision-makers, complex solutions)

• Forrester, The State of Business Buying, 2024 (13 stakeholders average)

• 6sense Buyer Experience Report 2025 (10-month average B2B cycle; 12+ months enterprise)

• Demandbase 2025 research on buying group complexity

• Why LinkedIn structurally can’t reach half your buying committee

• The attention data on CTV and audio and why it shifts the maths - these really are no-brainer B2B channels when you see the data below

• An honest persona map: where LinkedIn is strong and where it leaks

• The account graph: without it, CTV is just expensive reach

• Six reasons B2B CTV breaks self-serve execution models + how to over-come them

• How to run a credible test without setting £50k on fire

• Why the share-of-voice window closes sooner than you think

Here’s a sentence you don’t hear enough at B2B marketing conferences: LinkedIn is structurally incapable of reaching large parts of your buying committee.

That’s not a swipe at LinkedIn. I engage regularly on it every week, and for certain personas, most notably CMOs, VPs of Sales, Product leaders in tech, anyone whose career depends on being professionally visible, it’s the closest thing we have to a deterministic reach channel. In this regard it is brilliant, there are no bugs to bare here.

LinkedIn’s own research backs this up: the platform identifies five departments that most heavily influence B2B purchase decisions — IT, Finance, Business Development, Accounting, and Operations. LinkedIn is very good at reaching one or two of them.

The problem is the other three.

If you’ve ever tried to serve an ad to a plant manager, a facilities director, a controller at a mid-market manufacturer, a mid-career IT operations lead, a procurement head, a compliance officer, or a GP practice manager — and tried to do it at the scale and frequency required to register a brand — you already know what I’m about to say.

LinkedIn’s panel is self-selected, skewed toward the ambitious, the career-mobile, and the marketing-adjacent. Vast swathes of the actual decision-making economy — operations, finance below the C-suite, field-level tech buyers, public sector, non-English-speaking Europe, and most of the mid-market below the Fortune 5000 — are either absent, dormant, or invisible behind limited activity. This is a big swarth of the B2B marketing universe.

Ice Blue Sky’s recent work on UK tech and finance buyers made the uncomfortable point plainly: public-sector IT Directors, Heads of Technology, and Finance Directors are meaningfully less active than their private-sector counterparts, with posting constrained by communications policy and profiles that are “dormant between moments” of recruitment or programme launches. They are still on LinkedIn. They are not reachable through it. In plain english? they are not logging in and spending time on there until they are looking for a new job.

So when the B2B marketing industry has a collective panic about a cookieless world, signal loss, and LinkedIn CPM inflation, the answer most of us reach for is “spend more on LinkedIn.” It’s the wrong answer. The right answer is: find the committee where they actually pay attention and use a central account graph to do it.

That’s what this piece is about.

The attention argument, with numbers attached

Let’s get past the part where we all agree that “attention matters” and look at what the measurement firms are actually finding.

Adelaide’s 2026 Outcomes Guide, published in January, pulled together 60 case studies across 16 industries. Their headline: campaigns optimised to attention metrics saw an average 33% lift in upper-funnel KPIs and 53% increase in lower-funnel impact in 2025. That’s not a rounding error. That’s a capital-allocation argument.

And CTV, specifically, sits at the top of the attention league table. Per Adelaide’s industry KPI data reported by eMarketer, CTV attention units declined slightly between 2024 and 2025 but “remain strong overall” — Adelaide’s finding is that CTV is “relatively unmatched in capturing audience interest.” An earlier Adelaide index showed CTV jumping from 57.5 to 69.5 (out of 100) between 2023 and 2024, far exceeding online video or display.

There is a reason why TV was always the king of brand advertising.

Digital audio earns the same kind of premium, and interestingly, in my opinion is radically under-valued in agency and advertising land. Industry data consistently puts podcast completion rates at 80%+ versus roughly 12% for video content. 83% of senior executives report listening to a podcast in the past week, and business leaders spend 54+ minutes a day on audio content. The UK podcast ad market is forecast at only £56m in 2025 — tiny relative to engagement — which means share-of-voice is still cheap for anyone who moves now. I’d argue podcast advertising may be the single biggest bang for buck in B2B advertising today based on this data.

Host-read podcast ads outperform programmatic audio insertions by roughly 1.7x on brand outcomes. CPMs in specialised B2B shows regularly run $50–$100+ which sounds expensive until you remember your LinkedIn Message Ad is probably costing more on a CPMQL basis and being ignored.

These aren’t advocacy numbers from a sponsored deck. They’re the aggregate of years of third-party measurement across CTV, audio, online video and display, and they point in one direction: the attention you buy on high-attention channels is not the same unit as the attention you buy on a scrolling feed.

Further reading

The LinkedIn persona map, honestly drawn

Before anyone takes this as a “LinkedIn is dead” piece, let me be clear about where it’s brilliant.

Where LinkedIn is strong:

• Senior marketing (CMOs, VPs, marketing ops)

• Sales leadership (CROs, VPs of Sales, revenue ops)

• Tech executives in private-sector software and SaaS (CTOs, CIOs, Heads of Engineering) as well as founders and owners

• Product leaders in venture-backed companies

• HR and People leaders

• Anyone actively hiring, fundraising, or personal-branding

This is the LinkedIn sweet spot. Deterministic targeting, high match rates, usable frequency.

Where LinkedIn is weak;

• Operations, plant, and facilities leadership

• Mid-market finance (Financial Controllers, FDs below the AIM 100)

• IT ops and infrastructure managers at non-tech companies

• Procurement and supply chain

• Compliance, risk, and legal in regulated industries

• Public sector at all levels

• Clinical and practice-management roles in healthcare

• Most non-English-speaking European markets below the C-suite

• The “invisible committee” — the internal champions, budget gatekeepers, and end-user influencers who don’t post, don’t engage, and whose profiles are five years out of date

These are the people who sign off - or kill - your deal. And they are, on average, not scrollable on LinkedIn.

What they do do is watch Netflix. Listen to podcasts on their commute. Stream sport on CTV. Put on a podcast whilst doing admin. Leave an Amazon Prime Video ad-supported tier running in the background. The attention is real; the channel is just different.

Why CTV actually works for B2B (when it does)

The CTV-for-B2B case is no longer theoretical. The data has shown up.

Salesforce, in a recent case study, found LinkedIn’s CTV ads reached four times as many on-target impressions compared to linear TV. ServiceNow’s Jonathan Vu, VP of marketing, credited LinkedIn CTV with a 19% lift in awareness at large companies and a 45% improvement in lead submission rates. His comment on AdExchanger cuts to the commercial logic: “The competitive edge here is that we’re able to use that same audience that we’re running other campaigns with on LinkedIn for that CTV buy.”

Magna Media Trials found 98% of LinkedIn users watch CTV in a given week, compared with 83% for linear TV. 94% watch CTV with ads. These are the exact same decision-makers — just on a bigger, more attentive screen, in the evening, with their partner. LinkedIn have now on-ramped their data onto TradeDesk to activate CTV buys against in the US market, and my understanding is that will not expand beyond the US, at least in the near term.

Scott Stedman at the B2B agency Imaginarium, speaking to Bombora last year, put it well: “CTV is the most underutilised area of the marketing ecosystem. There’s still such an opportunity, surprise, and delight in B2B on CTV — because it’s not where most marketers are putting their budgets.” He adds the critical caveat: “With Bombora, the ability to take CTV and target it at an account level unlocks that channel in such a powerful way.”

That last line is the one that matters. This is where the activation layer, or DSP, needs to be strong enough to enable the B2B data layers that we need - as I was arguing on LinkedIn this week;

https://www.linkedin.com/posts/miketharty_a-major-holding-group-audited-a-leading-dsp-activity

Without a graph, none of this works

Here’s the part the CTV vendor marketing doesn’t lead with.

Consumer CTV is easy. Buy a demo, optimise to a conversion, walk away. B2B CTV is structurally different. You are trying to reach:

• A seven-person committee

• Across a six-month cycle

• In a thousand named accounts

• With co-ordinated frequency

• Against a pipeline value not a CPM

That requires a central account graph — a single source of truth that tells you which companies are in your ICP, which are in-market, which are in active opportunities, and which individuals sit on which buying committee inside each. Without it, CTV is just expensive reach.

With it, every channel in your mix becomes a conditional layer that lights up when the graph says so:

• Account enters awareness trigger (intent spike, site visit, webinar attendance) → light up CTV and audio for the account’s known DMA / IP range

• Account moves to consideration → layer in LinkedIn targeting the identified committee members + contextual display on vertical pubs

• Account enters active pipeline → co-ordinated air cover: CTV to the committee, host-read audio on a category podcast, sales-enablement email

• Account stalls or goes dark → retargeting via display + podcast network, paused LinkedIn to preserve frequency budget

• Account closes → cut spend, move to post-sale / expansion sequences

This is not a 2028 vision. It’s what leading operators do right now. Demandbase’s CTV playbook describes exactly this: uploading first-party CRM data to a CTV platform to target active opportunities as “air cover for your sales team, ensuring your brand stays visible to the entire buying committee while a deal is being negotiated.” Unisys’s Kate Coppola, speaking in their case study, landed on the same language — “visibility into and control over the campaigns we are running” as the unlock.

The graph is the conductor. CTV and audio are instruments. Without a conductor, you get noise.

CTV is HARD

Let me break character for a moment and name the reality that most of the LinkedIn “you too can do CTV” content glosses over.

CTV is hard to execute properly. And B2B CTV is harder.

The AdExchanger piece from three days ago on “agentic advertising” in CTV is instructive. Their framing: “CTV never fit cleanly into campaign execution models built for RTB-based trading.” Each CTV campaign combines custom packaging, negotiated pricing (often upfront commitments), creative requirements, delivery guarantees, and advertiser-specific measurement expectations — across systems that were never designed to operate as a single workflow. That’s before you layer on B2B-specific requirements like account-level delivery reporting, committee-level frequency management, and IP-to-account resolution.

Stedman’s take in Bombora’s piece was blunter: “You want to buy CTV? It’s a much more sophisticated process. You really need either to have a programmatic expert on your team or to have an agency partner.”

The specific executional pain points, in the order they tend to bite:

1. Inventory access. Premium B2B-suitable CTV is bought upfront, guaranteed, direct-IO, or through a small number of curated deal IDs. RTB will not get you on Disney+ in primetime.

2. Identity resolution. as we covered with the need for an account graph

3. Frequency management across publishers. Netflix, Disney, Paramount, Samsung Ads, Roku and the FASTs don’t share a frequency cap. Managing committee-level frequency across them requires a unified pixel, clean room, or media operator coordinating across deal IDs.

4. Creative. B2B has historically spent 90% of its budget on display banners. You now need 15s and 30s video for CTV, 15s and 30s audio for podcasts. Most B2B brands have none of this in the library.

5. Measurement. Net-new to most CROs. Upper-funnel lift studies, brand tracker alignment, MMM integration, and the stubborn fact that CTV will not directly convert in GA4. CFOs don’t love this conversation.

6. Reporting. Turning all of the above into something a board can read — account-level reach, committee penetration, assisted pipeline — is the bit that separates “we ran CTV” from “we prove CTV worked.”

This is why you are seeing a consistent pattern across the leading B2B marketing operators: the execution runs as a managed service, not a self-serve licence. Not because managed service is a fashionable model, but because the span of capability required — programmatic trading, identity, data engineering, creative ops, measurement science, account-graph management — is not a seat-based SaaS problem. It’s a specialist operational problem.

The brands doing this well are either (a) running the whole thing internally with a >10-person team — plausible for Salesforce, not for the 99% of B2B companies — or (b) running it via a managed partner whose entire operation exists to solve this exact class of problem. There isn’t really a credible third option.

This isn’t a self-congratulatory aside. It’s a structural observation about a channel whose complexity continues to grow: Nielsen’s measurement is contested, IP match rates are inconsistent, inventory is fragmented across walled gardens and FASTs, and the operating model for “B2B-suitable CTV” has yet to be solved by a single DSP UI.

Calculated experimentation, not a YOLO pivot

None of this is an argument to take 30% of your LinkedIn budget and put it on Roku next week.

It is an argument to do five things, in this order:

1. Build (or buy access to) a central account graph. If your “account list” is a spreadsheet in HubSpot, you are not ready for CTV. Start with identity: firmographic, intent, committee, status.

2. Honestly audit where LinkedIn is weak for your ICP. Pull match rates for your target-account person-list. If you’re missing 40%+ of your committee — and for most operations, finance, and mid-market sellers you will be — that gap is what high-attention channels are for.

3. Run a paired test. Take one segment. Layer CTV + podcast against LinkedIn-reachable committee members on the same accounts. Measure brand lift, reach, and pipeline velocity — not clicks.

4. Commit a credible budget to the test. Demandbase’s guidance is $25,000–$50,000 for a 90-day CTV test. Underfunding a test produces inconclusive data that makes it harder to justify future investment. This is correct. A £5k “experiment” is not an experiment.

5. Run the test through a partner who operates the whole stack. Or build the team. But don’t half-build it. That is where most of the failure stories come from.

The window is open for a bit longer

The reason this matters now — rather than, say, in 2028 — is that the high-attention channels are still under-priced for B2B share-of-voice. UK podcast ad spend at £56m, CTV pricing still consumer-optimised rather than B2B-optimised, and most B2B competitors still in the “we’re testing LinkedIn video” phase.

That window will close. LinkedIn’s own CTV product, now generally available across US and Canada with VAST integrations and expanded Paramount reach, is the clearest signal that the B2B audience on CTV is being actively priced in. When LinkedIn themselves are selling CTV to B2B buyers, it is no longer an information asymmetry worth waiting on.

My view — for what it’s worth — is that over the next 18 months the B2B marketing winners will be the ones who have built the account graph, connected it to high-attention inventory, and gotten serious about reaching the whole committee, not the one in four who happen to post on LinkedIn.

The committee doesn’t live on LinkedIn. It never really did. It just became convenient to pretend it did, because LinkedIn was the only channel we could bill against a persona.

That’s no longer true. And the operators who notice first will have the advantage.

— Mike

The B2B Stack is a practitioner-led newsletter on B2B marketing and programmatic advertising. If this was useful, forward it to the colleague who’s still telling you LinkedIn is enough.

Need help leveraging a B2B account graph, or making best in class channels like CTV and audio work for your brand or agency? FunnelFuel was built to address these challenges, upgrading the best of adtech for B2B. If you’re reading this in email, simply reply and you will land in my inbox or email me at mike [at] funnelfuel.io and we can help you get started. Or connect with me on LinkedIn here

• US programmatic spend crossed the $200B milestone in 2026 but the story isn’t scale, it’s where the value actually lives in the supply chain. More on that below. This could expand rapidly if the rumoured ChatGPT ads go programmatic - and their 6 week test on a limited cohort hit a $100m a year advertising run-rate - watch this space

• Microsoft Invest DSP shut down on 28 February 2026. Microsoft Monetize (the SSP) lives on and has joined Amazon’s Certified Supply Exchange. One less buy-side option; one cleaner supply path. The AppNexus DSP was a long time favourite of B2B marketers and this was the end of an era

• Bombora + Proximic by Comscore launched 300 new predictive B2B contextual audiences — activatable on TTD’s Contextual Marketplace and via portable Deal IDs across DV360, Yahoo DSP and Adobe. Worth knowing about. Worth keeping in perspective too.

• B2BMX 2026 sessions are calling out the “Intent Trap” - the argument that signal-based marketing is mostly “15-year-old intent data with a new AI wrapper.” The best conferences are now doing our jobs for us.

• Bombora data now reaches Reddit’s 116M daily active users — company-level audience targeting on the platform where real B2B peer research actually happens. A genuinely useful signal environment for B2B.

LEAD STORY

The Trade Desk is fighting to stay king but the fight has changed shape

The question is no longer DSP vs DSP. Curation, supply-side containerisation, and a new generation of infrastructure players are rewriting the rules underneath everyone’s feet.

This week, Marketing Brew ran a piece examining The Trade Desk’s position in a shifting market. CMO Ian Colley and two other senior executives departed as part of what the company called a “changing of the guard.” Omnicom and Publicis are both running agency audits of TTD’s fee structures. Jeff Green responded by buying approximately $150M of company stock. The numbers underneath it all remain very solid; roughly 19% year-on-year revenue growth for three consecutive quarters, JBPs now accounting for well over half of revenue, pipeline more than doubled year-on-year.

So the business is performing. The narrative is what’s under pressure.

But the deeper story isn’t about executive changes or agency relationships. It’s structural, and it’s being driven from the supply side.

The programmatic market is in the middle of a genuine architectural shift around curation and audience containerisation. The traditional model — an advertiser loads targeting parameters into a DSP, the DSP bids across open exchange inventory, audiences get assembled on the demand side — is being challenged by a different thesis: that the audience is better shaped in the supply layer, closer to the publisher, before the bid ever happens. Curated deals and private marketplaces aren’t new. What’s new is the sophistication and the commercial models being built around them.

A good example of this direction is Bedrock platform, a newer entrant approaching the DSP market with a fundamentally different architecture. The proposition: bake curation into the core of the platform rather than treating it as an add-on, and load the bidder directly into the SSP’s infrastructure. The logic being that by living on the supply side, you stop losing relevant traffic to the filtering and path optimisation that happens before a bid request even reaches a traditional DSP. Audiences get assembled with better signal fidelity. Waste is reduced before it’s even created. The commercial model is differentiated accordingly — not the standard DSP fee layer on top of media, but something built around the curation value itself. This is the cutting edge of programmatic advertisings direction of travel, and it stands to solve a bunch of structural problems in one big fowl swoop

Bedrock is just one example. But the direction of travel it represents is a market-wide signal. The comparison is no longer DSP vs DSP. It’s about where in the supply chain you want to do your audience work, and what infrastructure model best serves that. For B2B specifically — where audiences are finite, targeting precision is everything, and identity resolution matters more than reach — this has significant implications.

TTD’s response has been Kokai and its expanding direct-brand relationships. Smart moves. But as the supply-side curation model matures, the question of where the intelligence layer should sit — demand side, supply side, or both — is genuinely open. The independent DSP’s strongest argument has always been neutrality and breadth. Both of those things are harder to defend when your competitors are operating inside the supply chain rather than across the top of it.

My take

The agency audits are ultimately about leverage, not fees. When TTD deepens direct brand relationships and JBPs, it compresses the agency’s role in the media supply chain. Omnicom and Publicis understand exactly what that means over a five-year horizon. The audit is a message sent in commercial language.

For B2B programmatic, TTD remains the best open-web infrastructure available — 1 trillion QPS, unmatched supply breadth, the Bombora iABM integration, the UID2 ecosystem, the choice of platform for businesses like FunnelFuel who are building the B2B layers into the wider adtech layer. None of that changes with a CMO departure. What it does lose is the person best positioned to articulate the measurement story at a moment when B2B CMOs are demanding exactly that. The risk for TTD isn’t losing the open web. It’s losing the narrative just as the supply-side architecture conversation gets louder.

STORY TWO

ABM in 2026: 67% claim it. 26% can prove it. And 6sense may have a bigger problem than the data suggests.

New figures from the State of Account Based Marketing report land a stat that should embarrass a lot of CMOs: while 67% of organisations define ABM as a strategic GTM motion, only 26% describe their programme as truly successful. Fifty-five percent say ABM influences 0–20% of company revenue. Another 26% aren’t sure of its financial impact at all.

The structural diagnosis is familiar: most teams are running campaign-based ABM — time-bound, light on personalisation, reporting on engagement and meetings at best and media vanity metrics like CTR at worst — rather than an always-on system that adapts to signals, coordinates sales action, and compounds. Campaign ABM creates movement. It doesn’t create momentum.

But there’s a sharper conversation happening beneath this data, and it’s landing loudest around 6sense specifically. Spend any time on LinkedIn this week and you’ll encounter a growing volume of practitioners expressing genuine frustration: the signals that 6sense produces don’t appear to be correlating reliably with pipeline outcomes. Sales teams are increasingly sceptical of the intent scores they’re handed. Marketing and sales misalignment, the very problem ABM was supposed to solve, is reportedly getting worse in some organisations running 6sense, not better. For a platform that built its entire identity around predicting buyer readiness, that’s a serious reputational problem. Increasingly I’m reading about more tech centric marketers in big vendors building their own solutions with Claude

This is hard to quantify from the outside, and to be clear, anecdote is not data. But the sentiment is consistent enough and the voices prominent enough that it warrants naming. 6sense also feels notably quieter in the practitioner conversation than it did even twelve months ago. The category energy has shifted. Whether that’s a product problem, a go-to-market problem, or a consequence of the full C-suite reset the company is currently mid-way through — new CEO Chris Ball arrived in September, and since then there’s been a new CSO, COO, CPO, and CMO, the last of whom was poached directly from Demandbase — is genuinely unclear. But a company rebuilding its entire leadership team while managing growing signal-quality questions in the market is navigating a difficult combination.

Salesforce’s 2025 State of Marketing report adds useful context: B2B deals now involve an average of 11 stakeholders, each consuming 5–7 assets before touching Sales. An intent score against a single account, delivered to a sceptical sales rep, doesn’t capture any of that. If your ABM programme is built around a single buyer signal, it was never really ABM.

My take

The 26% success rate is not a platform problem. It’s a systems problem. Most organisations bought the software before they sorted the operating model; shared KPI contract, ICP tiers, MQA thresholds, SLA timelines, a single definition of what “an in-market account” actually means between Marketing, Sales, and RevOps. No platform resolves that misalignment for you. There is no silver bullet in the software as a service biz, and solving for that needs more of a white glove service then a tech stack

But the 6sense signal quality conversation points at something harder to fix with process. If the correlation between platform signals and actual revenue outcomes is genuinely weak, that’s a data and model problem. And the market’s patience for expensive platforms that don’t move pipeline is running out faster than the renewal cycles.

STORY THREE

6sense just poached Demandbase’s CMO. The ABM platform wars are getting personal.

On 19 March, 6sense announced the appointment of Kelly Hopping as Chief Marketing Officer. Her previous role: CMO at Demandbase. That is not a polite industry transition. That is a direct competitive strike and it signals where 6sense’s new leadership thinks the battle is being won and lost.

On the Demandbase side, the company isn’t standing still. This week, Demandbase and NetLine announced a joint integration syncing Demandbase’s ABM account lists directly into NetLine campaigns — bridging account-level intelligence with buyer-level content engagement in target accounts. Demandbase has also shipped its “Agentbase” GTM AI agents on AWS Marketplace and a deep real-time integration with Salesforce Data Cloud. Both platforms are in an arms race to own the label “agent-powered” before the other gets there first.

My take

Watch the Hopping hire for what it tells you about product direction. She came from the advertising and orchestration side of Demandbase, not predictive analytics. That tells you 6sense under Ball is prioritising execution and activation over dark funnel intelligence — which is a meaningful strategic pivot for a company built on predicting buyer readiness. I have said for a while that media execution, especially in channels like programmatic where the data exhaust fumes are so rich, is the best bet to cast for usable signal and cracking the dark funnel is a fools errand.

There’s also a structural point worth making about where both of these platforms sit relative to actual intent data. 6sense and Demandbase both bundle Bombora into their platforms. What reaches you is a processed Company Surge score — Bombora’s aggregated view of account-level topic activity. What you don’t get is the raw signal layer: the individual content consumption events, the publisher-level data, the granular research threads that Bombora actually collects before summarising. Platforms that surface a score are giving you a summary. Operating at the raw signal level means reading the original material. That gap is structural, and it doesn’t close with a CMO hire or an agentic product launch. Watch out for a big announcement in the coming weeks which will change this game completely

ICYMI = publisher rev splits from programmatic are back in the news again

The “36 cents to the publisher” narrative needs some serious pushback. This argument is as old as programmatic itself, and it persistently misses the point of where value actually lives in the supply chain. Yes, the open exchange has historically had dirty, multi-reseller paths and intermediaries who add nothing. That’s a real problem and curation is the right response to it. I strongly believe that the best users of programmatic do not have this issue any more - its a solved problem, move on.

But the implication that publishers should be capturing close to 100% of what enters a DSP fundamentally misunderstands what transforms an impression from worthless to valuable. In B2B especially, the difference between a raw impression — a device ID on a publisher page — and a verified impression reaching a CFO at a named account on your target list is entirely downstream of the data layer. Without identity resolution, without intent signals, without firmographic targeting, that impression is worth nothing to a B2B advertiser. Zero budget would be allocated. The publisher would get nothing.

Add in the humans managing campaigns, the brand safety technology, the viewability and attention measurement, the attribution infrastructure, the ad serving keeping the system honest — all of these represent real value that has to get paid for somewhere in the chain, and the accepted industry model is that it comes from media margin. When you view programmatic “waste” purely through the lens of publisher revenue yield, you miss the entire value exchange that makes B2B programmatic worth running at all.

Contextual: let’s be honest about the scale problem. The 74% of marketers planning to maintain or increase contextual targeting is a real signal. But before we declare contextual the quiet winner of programmatic, it’s worth naming the constraint that tends to get left out of these reports: there is no meaningful scale in contextual advertising for B2B, and there never has been. Bluntly, it borders on a waste of time

B2B audiences are finite and tiny. The personas we care about — C-suite and senior buyers at named accounts in specific verticals — spend a decreasing share of their time in environments where we can contextually target them today. LLMs are eating traditional editorial consumption patterns faster than new contextual inventory is being created. In fifteen-plus years of building and operating B2B adtech, contextual has always been a thin sliver of overall delivery. It works as a signal layer and a brand safety control. It does not work as a volume channel. I do not think about it as a delivery mechanism - instead I think of it as a signal amplifier. That is why we use it

The real value in B2B programmatic is audience — verified, identity-resolved, intent-qualified buyers — placed in premium, brand-safe environments where you have enough scale to optimise to high-attention units, which correlate to engagement, which drives account-level engagement scores and measurable shifts in vendor-level intent. Contextual tells you about the page. Audience tells you about the person. In B2B, the person is everything.

On intent data providers: Intentsify scored highest on Current Offering in Forrester’s B2B Intent Data Wave, ahead of better-known peers. Worth knowing about, though the important context is that Intentsify’s core heritage is content syndication — the signal depth they’re known for comes from tracking content consumption within their own publisher network. That’s genuinely useful for understanding what specific content buyers are engaging with. What it doesn’t address as well is the advertising execution layer — reaching those buyers programmatically, at scale, in premium environments, with account-level frequency control and buying committee analytics. Data depth and advertising chops are different capabilities, and the market conflates them more often than it should.

Genius Monkey + CTM integration. Programmatic meta-DSP Genius Monkey integrated impression-level campaign data with CallTrackingMetrics’ multi-touch attribution this week. The goal: connecting programmatic exposures to inbound call conversions — a measurement gap that matters especially in B2B, where enterprise deals still close over the phone.

References:

Here’s the full source list for the edition:

<br>

Trade Desk / Programmatic Infrastructure

• Marketing Brew — “Can The Trade Desk Remain King of the Open Web?” — April 9, 2026 — marketingbrew.com

• eMarketer — Programmatic Advertising Forecast & Trends H1 2026 — January 2026 — emarketer.com

• Basis Technologies — Expert Roundtable: Trends That Will Shape Advertising in 2026 (36 cents stat, supply chain waste) — December 2025 — basis.com

• Basis Technologies — 7 Programmatic Advertising Trends Shaping 2026 ($26.8B waste estimate, curated deals) — January 2026 — basis.com

ABM & Intent Data

• N.Rich — Why “ABM Campaigns” Don’t Work in 2026 (67%/26% State of ABM data) — February 2026 — nrich.io

• Salesforce — State of Marketing 2025 (11 stakeholders, 5–7 assets per deal) — 2025 — salesforce.com

• Demand Gen Report — 2026: The Year Marketing Enters the Age of Accountability (VTA, 266 touchpoints, MQL decline) — March 2026 — demandgenreport.com

• Forrester — Wave: B2B Intent Data Providers, Q1 2025 (Intentsify highest Current Offering score) — Q1 2025 — forrester.com

• Forrester — Wave: Revenue Marketing Platforms for B2B, Q1 2026 (6sense named Leader) — January 2026 — forrester.com

6sense

• Demand Gen Report — 6sense Launches AI Email Agents, Names Chris Ball as New CEO — September 2025 — demandgenreport.com

• Business Wire — 6sense Appoints Kimberly Bloomston as CPO and Kelly Hopping as CMO — March 19, 2026 — businesswire.com

• 6sense Newsroom — 6sense Welcomes Chris Ball as CEO — September 9, 2025 — 6sense.com

Demandbase

• Demand Gen Report — NetLine and Demandbase Join Forces to Elevate ABM Programs — April 6, 2026 — demandgenreport.com

• Demand Gen Report — Demandbase Experts Share Strategies for Privacy-First Marketing: 2026 COSeries — March 2026 — demandgenreport.com

Bombora / Contextual / Data

• PPC.land — Bombora and Comscore Unite to Create 300 B2B Contextual Audiences (Proximic partnership, Reddit integration) — January 2026 — ppc.land

• Comscore — 2026 State of Programmatic Report (74% contextual usage stat, curated marketplace data) — January 2026 — comscore.com

Other

• Newsfile Corp — Genius Monkey Announces Programmatic Integration with CTM — April 7, 2026 — newsfilecorp.com

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Before I walk in, I’m doing what I always do: writing down exactly what I think. Not what sounds smart. Not what’s safe to say in a room full of peers. What I actually believe right now, in April 2026, based on what I’m seeing in the market every day.

Consider this my pre-match notes. I might walk out tomorrow with all of it intact. I might walk out having had my mind changed on half of it. That’s the point.

Here’s where I stand going in.

Roundtable 1: What’s actually changing in 2026 and what’s just noise?

My position going in: the B2B playbook isn’t being rewritten. It’s being stress-tested like never before

The fundamentals of demand creation haven’t shifted. What’s shifted is buyer tolerance for irrelevance. They’re forming opinions about your brand before you’ve had a single touchpoint, and they’re doing it on terms you don’t control. That’s not a content problem. That’s a signal quality problem.

It’s the living embodiment of the dark tunnel

Multi-channel orchestration is real now too. CTV is not just noise. Search dollars are moving to the ad games best storytelling surface, showing the need to raise awareness higher up the funnel. Brand advertising is back.

On AI-generated content: the backlash isn’t coming, it’s already here, it’s just polite.

Buyers aren’t complaining about it. They’re just quietly not converting on it. There’s a difference between content that fills a pipeline and content that moves a buyer. Most AI-generated B2B content does neither.

All content ever written by mankind has now been processed by LLMs. To save a doom spiral, they NEED fresh perspective and thoughts. For better or worse that’s partly why LinkedIn is now a major source for LLM’s. The chance is there to shape narrative but it needs fresh and real thoughts of your own - and your brands - to work. In LLM world this is the chase for net new knowledge, otherwise the systems learn on their own output, in a spiral of no new knowledge. There’s a chance to win here. It’s partly why I write this substack

The trend I’m actively ignoring right now? Anything that tells me to produce more, faster. Volume is not a strategy. It’s a symptom of not knowing what actually works.

What does the 2026 buyer expect that they didn’t twelve months ago? They expect you to have done your homework. Generic outreach, generic retargeting, generic nurture — all of it now reads as proof that you don’t know them. The relevance bar has moved. Most programmes haven’t. Thin AI personalisation can still be seen through a mile away

I’m open to being wrong on the AI content piece. I know there are people making it work. I want to hear how.

A clever play which I like is one my old friend Dax Hamman shared with me via a loom. His business fomo.ai now has an AI interview tool, called Hello Gordon. It uses AI voice to track the interview and behaves more like a journalist - reacting to answers and asking smart follow-ups, with the aim of extracting knowledge. I’ve immediately shared that around my business, FunnelFuel, because we’re trying to encourage our specialist team to share knowledge outwardly. It seems like a great combination of the best of LLMs ability to “write well” but with proper knowledge based input. I’m going to be testing it too to help me interview sector experts for this newsletter.

I’m keen to find more such solutions that bridge human + LLM vs “write me an article on buying committees”

Roundtable 2: What are CEOs actually scared of and what did Q1 teach us?

My position going in: most senior leaders are scared of being wrong about AI. Not AI in the abstract but the specific version of AI transformation they’ve committed to internally, while quietly suspecting their competitors have found a better one.

The gap between talking about AI and doing something with it is still enormous in most organisations I encounter. The companies that moved fast in 2024 are iterating on real outputs. The ones that waited for the right strategy are still waiting for permission. That’s not a market problem. That’s a leadership culture problem.

AI is fast becoming business infrastructure. Whilst others chase between LLMs based on the latest update, the winners are building AI into the heart of their business operations and mobilising it at scale

On Q1:

I think it’s widely accepted that many companies in our space had a soft Q1. The macro is full of bad news, from the economy, government anti-business decisions here in the U.K. and the war in Iran. None of this is the bedrock to encourage significant marketing investment around. There are signs that is picking up in to Q2, with a slightly more bullish outlook than I was expecting

More specifically on B2B marketing…

I suspect the honest reviews in most rooms looked something like this — ABM programmes that looked sharp in the planning deck and underdelivered because the underlying data wasn’t clean enough. Paid that hit impressions and missed pipeline. Content that ranked and didn’t convert a single account worth having. The macro combines with this, all of which could feel discouraging.

Metrics, metrics, and more metrics - this pain is not subsiding fast

The question I want the room to sit with: are we measuring the right things, or the things we know how to measure? Most Q1 reporting I’ve seen is built around comfort metrics. Vanity-to-signal ratio is still far too high across the industry. I’m hearing for example that agencies are testing ChatGPT ads and looking at CTR (around 0.9%) and judging it for being lower than search. Its $60 CPM is being backed into a CPC, or is it?

This is a live view of metrics in the battlefield. Search has high CTRs because of a combination of intent - the user is searching for answers AND a user experience where the search engine presents solutions, which you need to click to see. ChatGPT and LLMs are a different game - a conversational, generative flow. They are in many ways designed to REPLACE clicks with answers delivered straight to you. We need to get better at understanding what a click in a particular surface means - and how high quality is it relative to intent. Agencies OR brands if they are in-housing and doing it all themselves need to create a valuation model for clicks which takes into account context, surface, intent and not just raw numbers.

I want to hear from people who had a genuinely good Q1. What did you do differently?

Roundtable 3: AEO, discovery, and the question nobody has cleanly answered yet

My position going in: AEO is mostly SEO with a rebrand, except for the small minority of companies that have genuinely rethought how buyers discover them in an AI-first world. Most haven’t.

The point that I keep coming back to: if Google isn’t the front door anymore, and increasingly it isn’t, most B2B brands have no idea what their new front door looks like. And that is not a good look

They haven’t checked how they appear in AI-generated answers. They don’t know whether the summary a prospect gets from ChatGPT or Perplexity about their category is helping or hurting them. And really, these tools are hard to properly check because they are so personalised, shaped by the chats you have had previously, and what it knows about you. This is not checking Google 1.0 for a term like “CRM software” and seeing if you show up. This is positioning based on far more inputs form the user, collected over far longer, and reflecting in an ever evolving chat. What you can check is, if in general, you get cited.

All of this threatens to be a strategic blind spot for B2B brands. This will need to change, and the upcoming ChatGPT ads, which look likely to be programmatic ads powered by the TradeDesk, will put more emphasis on LLMs, and darned quickly

The question that I think cuts through everything else in this session: is your website still your best asset, or is it quietly becoming a fallback? For a lot of brands, the real answer is the latter. The website exists for the already-converted. Discovery is happening elsewhere, on terms you don’t control.

Visibility in 2026 is about being cited, referenced, and trusted by the systems your buyers use to form their initial view of a category. Its partially why I am bullish on influencer advertising done right. That’s as much a thought leadership problem as a technical one. How can you take thought leadership and seed it in ways that drive discoverability, and at scale? Most SEO teams aren’t set up to solve it. Most content teams don’t know it’s their job either.

This is the session I expect to have my mind changed the most. I don’t think anyone has fully cracked this yet — most definitely including me.

Why I’m publishing this before the event

Partly as prep - I like to write down my thoughts and think through them in this sort of form, so why not share? Partly because I think the most valuable thing I can offer this room tomorrow is a genuine point of view, not a facilitated fence-sit.

If you’re a subscriber and you’re going to be there, come find me! If you’re not going to be there but you’ve got a strong take on any of the above — hit reply. I read everything.

And if you’re in London, free tomorrow, and want a seat — reply to this email now. There may be drop-outs. First come, first served.

More from me after the sessions.

The B2B Stack is a practitioner-led newsletter for senior B2B marketers. If someone forwarded this to you, subscribe below. We cover the latest news, changes and evolution of paid B2B media and the underlying B2B tech stack

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Display advertising has spent fifteen years being the unloved middle child of the media mix. Measurable enough to be scrutinised, unfashionable enough to be defunded, and technically sophisticated enough that nobody outside adtech really understood why it kept surviving. B2B marketers in particular treated it like a necessary evil, something you did when search inventory ran dry and you needed to justify the impressions line on a media plan.

That era is ending. And the trigger is the last thing anyone expected.

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Display Was Never The Problem

Let’s be honest about something the industry spent years dancing around: display was never a weak channel. It was a channel with a format problem.

The data exhaust from display has always been extraordinary. Browse behaviour. Dwell time. Scroll depth. Return visits. Content affinity signals that tell you more about genuine purchase intent than any self-reported form fill. When programmatic scaled in the early 2010s, the infrastructure built to activate that signal was genuinely world-class — second only, arguably, to search.

But the creative format never kept pace. The banner ad arrived with Web 1.0 and, structurally, never really left. The 728x90. The 300x250. Formats conceived when broadband was a novelty and screen real estate was rationed. As the web scaled into immersive, social, video-first experiences, the banner sat in its corner — tolerated, banner-blocked, ignored.

The signal was premium. The execution was not.

The Native Promise That Wasn’t

I was at the sharp end of native advertising when it emerged as the supposed solution to this. The logic was compelling: match the form and function of the environment, earn attention rather than interrupt it, and suddenly display becomes a format that people might actually choose to engage with.

And it almost worked. For about eighteen months in the mid-2010s, native felt like the recalibration the industry needed.

Then reality arrived. Scale required aggregation. Aggregation required standardisation. Standardisation killed the native premise. You can’t be contextually matched to an environment when the same unit is running across three thousand publishers on a single insertion order. The UX became inconsistent. The quality signals became unreliable. Brands lost the editorial control they’d been promised, and found their assets sitting next to content they’d never knowingly have associated with.

Native chased scale by hijacking the 300x250, and morphed itself back into good ol’ display all over again.

More fundamentally: native tried to solve a format problem without solving the underlying tension between reach and relevance. It papered over the structural issue rather than resolving it.

The format problem remained unsolved. And then something larger happened.

The Zero-Click Inversion

The rise of LLMs did something to the open web that nobody in the ad industry quite reckoned with in time. It didn’t just change how people searched. It changed where intent forms.

For decades, the implicit contract of the internet was linear: user has a question, user goes to a search engine, user visits a website, publisher gets traffic, advertiser buys access to that attention. The entire programmatic ecosystem — the SSPs, the DSPs, the data platforms, the attribution models — was built around that flow.

LLMs broke the middle of that funnel. Not instantly, and not completely, but structurally and irreversibly. A meaningful cohort of users — skewed younger, skewed professional, skewed exactly toward the decision-makers B2B brands most need to reach — began resolving their queries inside the interface. Zero-click behaviour throttled traffic into the premium open web. Publishers felt it in their analytics before they knew how to talk about it publicly. They have been fighting it ever since, and the direction of travel is accelerating it. LLM’s were adopted about 4x faster than the internet.

The downstream effect for display was supply compression. Less premium inventory reaching the audiences that matter most. Higher CPMs for what remained. And a growing suspicion in CMO offices that open web display was a channel in structural decline.

It wasn’t. The intent was just moving upstream. The often forgotten truth is that even with this going on, the underlying open internet IS still growing.

The Irony At The Heart Of This Moment

Here is where it gets interesting. And where, if you’ve been watching programmatic for long enough, it starts to feel like a kind of poetic justice.

The same technology that drove zero-click behaviour and starved the open web of high-quality traffic is now, potentially, about to become the most significant demand driver display advertising has seen in a generation.

ChatGPT’s move into advertising and the signal that The Trade Desk could power the buy side of a display CPM model within that environment, reframes the entire equation. Suddenly, the question isn’t “where did the intent go?” It’s: “what happens when you can buy against it?”

If GPT is where modern purchase intent is being formed — and the data increasingly suggests it is, particularly among the millennial and Gen Z professionals who now hold real B2B buying authority — then ads served within that environment carry a different kind of weight. This isn’t a user who’s been retargeted twelve times and glazed over. This is a user at the precise moment of active research, forming a view, building a shortlist.

That’s not a display impression. That’s a search impression in a new coat. It is the best of search married with the best of programmatic. Real intent married with an open, data rich operating system.

Why The Trade Desk Angle Matters

I’ll admit my prior assumption was that OpenAI would build this in-house. They have the capital, the data, and the incentive to own the monetisation layer. A homebrew ad server, a proprietary auction, a walled garden that controls signal and inventory simultaneously — it would have been the logical move. I thought we’d be looking at Google 2.0 here

If they’re instead opening the buy side to TTD’s infrastructure, that changes everything.

It means the intent signals originating in GPT become actionable through the same programmatic pipes that B2B brands are already using to run CTV, audio, DOOH, and open web display. It means a DMP or CDP integration that already talks to TTD can, in principle, extend its reach into the highest-intent environment on the internet. It means the category doesn’t fragment further — it consolidates around infrastructure that serious B2B marketers already understand and can buy efficiently.

The best data ecosystem suddenly meets one of the best ad canvases - a huge moment of evolution. I have always said a lot of the programmatic infrastructure has been wasted on low rent sites with often unviewable placements. In a heartbeat the tide could turn here

And for programmatic as a category? This is the legitimacy moment. CMOs who have been steadily moving budget from display into performance channels, from awareness into last-touch, will be compelled to reconsider the model. Not because display suddenly got better at claiming credit — but because the audience and the moment are now undeniable.

A Rising Tide For The Whole Ecosystem

The fuller implication, though, goes beyond ChatGPT inventory itself.

Think about what happens to the intent trail. A user researches a solution inside GPT. They form a view. Maybe they click through to a vendor — maybe they don’t. But that interaction is a data event. That intent signal, if it flows back into the programmatic ecosystem — through TTD’s ID infrastructure, through first-party data matching, through the kind of clean room arrangement that makes sophisticated B2B marketers salivate — suddenly enriches every subsequent touchpoint. The data story is either born in ChatGPT or seriously fortified by it.

Find that same user on CTV. On LinkedIn. On the open web. On audio. The signal you’re activating against isn’t a third-party cookie approximation or a lookalike model built on last-quarter’s data. It’s live, declared, contextual intent from the moment they were actively researching.

Solutions like FunnelFuel, already adept at recognising buying committee members within accounts, can suddenly take a committee members research and amplify the message, knowing the moment is perfect, to the wider buying committee or even the wider org.

This is the thesis: GPT doesn’t just become an ad channel. It becomes the top of an intent graph that makes every channel below it more effective. And because the data all lives in programmatic pipes like TradeDesk, why would a CMO not work to maximise this intent layer FIRST, rather then last as they currently do?

For B2B specifically, where the buying cycle is long, the committee is complex, and the difference between an in-market account and an out-of-market one is everything, that signal enrichment is worth more than the display impression itself. It’s the data that makes your CTV spend smarter. It’s the trigger that makes your LinkedIn retargeting relevant rather than repetitive. It’s the signal that lets your programmatic partner — and yes, this is where FunnelFuel’s position becomes acutely relevant — run account-level progression scoring against real, current intent rather than modelled proxies.

Display was never just a reach channel. It was always a signal channel with a format problem. If ChatGPT’s ad model resolves the format question by placing the unit at the moment of intent formation, and TTD resolves the activation question by connecting that signal to the rest of the addressable ecosystem, then what we’re watching isn’t a minor product update from OpenAI.

It’s the structural rehabilitation of an entire media category.

What This Means For How You Allocate Budget

The practical implication for B2B CMOs is uncomfortable but important: the channel hierarchy you built your media mix around was constructed during a period of structural distortion. Display was undervalued because its format underdelivered. Search captured disproportionate budget because it sat closest to the moment of declared intent. Social inherited the brand dollars that used to go to TV.

If the moment of declared intent is now inside ChatGPT — and it is, increasingly, for the professionals you most need to reach — then the channel hierarchy needs rebuilding from scratch.

Display, powered by GPT intent signals and activated programmatically across a converging addressable ecosystem, doesn’t look like the unloved middle child anymore.

It looks like the critical pillar.

The brands that recognise this first, build the infrastructure to activate against it, and resist the temptation to wait for the case study before moving — those are the brands that will own the consideration set when budgets tighten and every other CMO is fighting for the same search inventory at inflated CPMs.

The tide is coming in. The question is whether you’ve built anything worth floating.

Mike Harty is a co-founder of FunnelFuel.io, a B2B-native programmatic advertising business, and the author of The B2B Stack — operator-led intelligence for modern B2B marketers. If you want to talk to me directly, simply reply to this email or email mike at FunnelFuel.io

Down 33% for the year. Three holdcos out the door in thirty days. The CEO spending $148 million of his own money defending the narrative. And then, right at the bottom of the selloff, a leak drops that TTD is in early talks with OpenAI to power advertising inside ChatGPT.

The stock bounced 18% in a single session. The founders $148m stock buy back suddenly looks a heck of a trade.

That’s the TTD story in microcosm right now: structurally under pressure, narratively fascinating, and impossible to write off. The OpenAI talks don’t fix the holdco problem. They don’t resolve the Publicis audit. They don’t rebuild the agency relationships. But they do point at something important — the next major ad inventory surface is being built right now, and whoever gets a seat at that table early shapes the decade.

Meanwhile LinkedIn showed up to NewFronts this week with genuine swagger, a campaign called “Cut the Bullspend,” and a pitch that’s as much pressure on every other B2B media platform as it is a product announcement. The Google AdX remedy ruling — overdue since Q4 2025 — could now land any day. And the IAB Tech Lab quietly unified all its agentic work under a single initiative, signalling that the standards war for autonomous programmatic is entering a new phase.

This week was the TTD-OpenAI story. But around it, a set of signals that, when read together, tell you exactly where the B2B advertising stack is heading.

Here’s what they mean.

📩 STACK SIGNALS lands in your inbox every Friday. The week’s B2B intelligence briefing, distilled.

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THE PATTERN

There’s a single thread running through every major story this week.

Trust.

The Publicis/TTD fight is fundamentally about who trusts whom in the programmatic supply chain — and who has the leverage to demand accountability. The LinkedIn “bullspend” campaign is a direct attack on the industry’s long-running habit of substituting metrics for outcomes, and the trust gap between marketing teams and their CFOs that creates. The OpenAI/TTD talks are about whether a completely new ad surface can be built on top of existing infrastructure — which only works if advertisers trust it enough to actually spend there. There is also the consumer trust element - does the average ChatGPT user trust the platform when the answer is surrounded by ads? The Google ruling is about whether structural trust can be legislated into a market that’s been structurally compromised for a decade.

When the whole industry is arguing about trust simultaneously, it’s not a coincidence. It’s a reckoning.

STORY BREAKDOWN

STORY 1: The Trade Desk, Publicis, and the War Nobody Is Being Honest About - which has rumbled on from last week as expected

What happened: In an email sent to select clients and obtained by Adweek, Publicis explained that an independent audit by FirmDecisions found TTD had improperly applied its DSP fee to other charges, billed clients for tools they were automatically opted into without evidence of authorisation, and failed to provide the auditor with information necessary to validate that media and data costs were invoiced at cost and without mark-up. Ouch. Adweek

This followed Dentsu and WPP, who had stepped back from TTD’s OpenPath supply initiative weeks earlier. Trade Desk stock has retreated 33% in 2026. Campaign Live

Three of the Big Five holdcos. One month. All citing transparency. Publicis accounts for more than 10% of TTD’s gross billings, with two ad holding companies each accounting for over 10% of 2025 gross billings — combined, around 30%. Investing.com

Why it matters - my read:

Take the audit findings at face value. Fee complexity, auto-enrolment, audit access refused. Real problems. TTD’s fee structure is transparent in principle but confusing in practice. It has been slow, stubborn even, in response to legitimate concerns about the Kokai interface. Adweek Those are genuine operational failures worth fixing.

But they don’t explain the pattern.

Three holdcos, same month, same language. That’s not three independent audits arriving at the same conclusion. That’s a coordinated moment. And the commercial logic underneath it is visible if you look for it.

Industry observers pointed to potential overlap between OpenPath and services traditionally handled by agency trading desks, including supply-path optimisation and inventory curation. This is getting to the heart of the matter. e4m OpenPath wasn’t just a transparency initiative — it was TTD moving into the supply chain and threatening the margin line that agency trading desks charge for SPO and curation. When WPP cited “overlap with agency services,” they weren’t confused about what OpenPath does. They were telling TTD to get out of their lane.

The audit gave them a legitimising narrative for what is fundamentally a power move. You can’t tell clients “we’re stepping back because it competes with our trading desk margin.” But you can absolutely tell them “an independent auditor found multiple violations.”

The irony — which nobody is saying loudly enough — is that the loudest voices on transparency are the same organisations facing their own transparency crisis. WPP is currently subject to a $100 million whistleblower lawsuit alleging nearly $1 billion annually in undisclosed principal trading income. Publicis’s own holdco model runs on disclosed margins and undisclosed recovery. To survive, agencies have to generate profit from hidden fees and undisclosed arrangements. Marketers, in turn, reward this by continuing to hire the agencies that play the game most aggressively. Adweek

That is not a small irony. It’s the defining contradiction of the current holdco moment.

My view:

Stop reading this as a billing dispute. Read it as a territory war over who controls the supply chain in a post-OpenRTB world. The holdcos want the SPO and curation advantage back. TTD tried to disintermediate them. Neither side is being fully honest about why.

The B2B buyers in the middle should be asking a single question: if my DSP and my agency are publicly fighting over who controls the path to inventory — what does that mean for the quality, transparency, and true cost of the media I’m buying? The answer is more than most teams currently account for.

None of this is about the foundations of TradeDesk as a world class DSP. It boils down to lanes and margin.

STORY 2: TTD and OpenAI in Talks — The Most Important Sentence in Adtech This Month. Display Advertising Has Been B2B's Most Undervalued Channel for a Decade. ChatGPT Just Changed That.

What happened: OpenAI has been running early advertising trials since February, exploring partnerships with ad technology providers to support its effort to diversify revenue beyond subscriptions and enterprise services. ChatGPT reaches an estimated 910 million users globally. Early indications suggest OpenAI may use CPM pricing — a model that aligns with TTD’s strengths in inventory valuation and automated bidding. TheDesk.net

OpenAI is in early talks with The Trade Desk about a partnership that would help the AI firm sell ads within ChatGPT — TTD’s infrastructure could be used to quickly scale up ChatGPT’s ad business, rather than OpenAI building walled gardens that rival Meta and Google. eMarketer

The stock jumped 18.4% on the news. The market read it as a TTD recovery story. That’s the wrong read.

The real story — and it’s a MUCH bigger one:

Let’s talk about display.

For the better part of a decade, display advertising has been the unloved stepchild of the B2B marketing budget. CMOs direct spend toward events, LinkedIn, paid search, and content syndication. Display gets the scraps — typically a small retargeting budget, managed by someone junior, optimised to CTR metrics that everyone quietly knows don’t mean anything, and measured by last-click attribution that gives it no credit for the pipeline it actually influenced.

The reasons given are always the same. Display doesn’t perform in B2B. The targeting isn’t precise enough. The audiences are too broad. The CPMs don’t justify the reach when you’re selling to a committee of seven people at 500 accounts.

There’s truth in parts of that. Generic banner display on the open web, bought without signal and measured badly, is a waste of money in B2B. Agreed.

But here’s what that argument has always missed: the problem was never the channel. It was the signal quality, the supply quality, and the measurement framework sitting underneath it. The channel itself — reaching a relevant professional at the right moment in their decision-making process, with a CPM-based buy that doesn’t require a click to work — is one of the most powerful tools in B2B marketing. The world’s most sophisticated B2B vendors figured this out years ago. Their in-house programmatic teams run display at serious scale, with serious signal, and generate serious pipeline from it. The channel works. It just requires expertise most B2B marketing teams don’t have in-house — and most agencies haven’t been incentivised to build.

So they defaulted to LinkedIn. And events. And search. And called it a strategy.

Now here’s where it gets genuinely interesting.

If ChatGPT becomes a display channel, bought on CPM, powered by TTD’s infrastructure, served into an environment where 910 million users are actively researching and asking explicit questions, the entire calculus flips.

Think about what that surface actually is from a B2B perspective. A buyer researching CRM vendors, cloud infrastructure, logistics software, or any other enterprise category isn’t scrolling passively. They’re asking specific, high-intent questions and reading the answers with full attention. That’s not a banner impression on a news site. That’s something closer to the intent moment of paid search — but on a CPM model, at scale, in an environment that the open web cannot replicate.

The brand safety argument that has always haunted display disappears — you know exactly where the ad appears and what content surrounds it. The targeting precision argument weakens — query-level context tells you more about buyer intent than any third-party segment. The measurement argument shifts — you’re buying presence at the highest-attention moment in the research process, which is exactly where B2B influence actually happens.

If this plays out, display advertising doesn’t just get rehabilitated in the B2B CMO’s budget. It potentially becomes the most strategically important channel in the stack — the place where you reach buyers at the exact moment they’re forming category opinions, with verified context, at a price point that scales.

The B2B-specific angle nobody is saying:

Millennials and Gen Z now make up roughly 70% of B2B decision-makers. These are the buyers who run their research through AI assistants rather than search engines. They don’t fill in content syndication forms. They don’t attend three-day trade shows. They don’t respond to SDR cold sequences. They ask ChatGPT which vendors to shortlist and let the answer do the work.

If that’s where the research happens — and the evidence increasingly says it is — then the brand that isn’t visible in that environment is not on the shortlist. Full stop. No amount of LinkedIn spend or event presence compensates for not existing in the research moment.

Display, done right, in the right environment, with the right signal — has always been the answer to that problem. Most B2B CMOs just never built the capability to run it properly.

ChatGPT as a display surface might be the forcing function that finally changes that.

The honest caveat:

A partnership with OpenAI might not last long. OpenAI is reportedly also developing its own adtech capabilities, and in a worst-case scenario could become a future competitor to TTD rather than a partner. AOL

So TTD’s window is real but potentially short. More importantly, the pricing model, the brand safety standards, the targeting parameters, and the measurement framework for ChatGPT advertising are all undefined. This is genuinely early. No B2B CMO should be reallocating Q2 budget toward it yet.

But as a directional signal about where high-intent B2B display inventory is going? This is one of the most important stories of the year. The channel that B2B CMOs wrote off is the one that may define their 2027 strategy — and the teams building display capability now, with proper signal and proper measurement, will have a structural head start when that inventory opens up.

The question to ask your team this week isn’t “should we try ChatGPT ads?”

It’s “do we actually have a display capability worth scaling if the inventory becomes available?”

Most B2B marketing teams, honestly, don’t.

STORY 3: LinkedIn Tells the Industry to Cut the Bullspend — and It’s Not Wrong

What happened: At their second-ever NewFront, LinkedIn showed up with genuine ambition — emphasising creator partnerships, live-event integrations, CTV distribution, and measurable outcomes. Their own ad campaign, “Cut the Bullspend,” launched across New York the same week, running on billboards, branded cars, and digital surfaces outside competitor NewFront venues. Marketing Brew

The campaign challenges the industry habit of optimising B2B video for metrics like clicks and impressions that look impressive on a dashboard but fail to deliver business results. BrandLink campaigns see an average 130% higher video completion rate compared to standard in-feed ads, and members exposed to BrandLink are up to 18% more likely to become a lead. Adweek

Why it matters:

“Cut the Bullspend” is genuinely sharp positioning. It names something every B2B marketer thinks privately and nobody says publicly — that a significant portion of their budget is producing measurable activity and unmeasurable impact, and that the metrics being reported to the CFO are increasingly implausible as proxies for pipeline.

Millennials and Gen Z now make up roughly 70% of B2B decision-makers, and what moves them isn’t noise or hype but credible expertise and confidence that a choice will drive real outcomes. Adweek LinkedIn is building a pitch that speaks directly to that generation — they don’t download whitepapers, they watch video from voices they trust, in environments where professional context does real work.

My read;

LinkedIn is not your friend. It’s a platform with a commercial interest in you spending more money inside its walls. “Cut the Bullspend” is good positioning, but read the logic carefully — the proposed remedy to bullspend is more LinkedIn spend, not less spend overall. Its already an over-saturated channel which has great data but low intent, and functionally under-reaches many personas.

That said, the underlying argument is correct. B2B marketing has had a measurement problem for years, and AI is making it worse by creating more surfaces, more touchpoints, and more attribution complexity simultaneously. The CFO pressure is real. The move from optics to outcomes isn’t optional in 2026.

The B2B teams that handle this well won’t just shift budget into LinkedIn. They’ll build measurement frameworks that can attribute pipeline influence across the full stack — including the dark funnel activity that never shows up in Campaign Manager. LinkedIn is one piece of that. A well-run piece. But it’s not the whole answer, and the “Cut the Bullspend” campaign knows that.

STORY 4: The Google AdX Ruling Is Coming — Probably This Week or Next

What happened: Judge Brinkema is expected to issue her ruling on Google’s ad tech monopoly remedies before the end of Q1 2026 — meaning it could land any day. The DOJ wants Google to sell its ad exchange AdX and potentially its publisher ad server DFP. The remedies trial concluded in November 2025. Linos NEWS

The market has been waiting five months. The delay itself has been a signal — the longer it takes, the more likely a complex, nuanced ruling that neither side fully wins.

Why it matters:

Whatever Brinkema decides, the structural question it answers — or fails to answer — is the same one the holdco/TTD fight is about: who controls the supply chain?

If AdX is divested, the entire open-web publisher supply stack is in play. SSPs, curation platforms, and deal marketplaces get a once-in-a-generation opportunity to capture inventory that has been locked inside the Google pipe for fifteen years. That’s not theoretical. Publishers who have never interacted with programmatic through anything other than AdSense would suddenly be accessible via competitive auction.

If the ruling is behavioural only — which is increasingly the likely scenario based on the judge’s scepticism during closing arguments — the structural monopoly remains intact, but with mandated interoperability. Prebid integration, UPR reform, data sharing. Meaningful on paper. Google will test the wording of every clause. Progress will be slower than the industry hopes.

My read:

Build your supply strategy like the ruling changes nothing. Because even in the best-case divestiture scenario, the legal process runs for years — Google appeals immediately, the market fragments gradually, publisher migration takes longer than anyone models. The curation layer is the right answer regardless. Deal-based buying with curated, B2B-quality supply paths — where you own the signal, control brand safety, and see the full supply chain — is not a hedge against the Google ruling. It’s the correct strategy in every scenario. The ruling just makes it more urgent.

The real question for B2B buyers isn’t “what does Brinkema decide?” It’s “am I building supply relationships that don’t depend on the answer?”

STORY 5: IAB Tech Lab Unifies Its Agentic Work — The Standards Battle Has a New Name

What happened: In March 2026, IAB Tech Lab unified all of its agentic advertising work under a single name: AAMP — Agentic Advertising Management Protocols. This brings together the Agentic RTB Framework (ARTF), Agentic Audiences, Agentic Mobile, and the Agent Registry, launched March 1st, under one umbrella. ARTF promises to reduce bid request-response latency by up to 80%, cutting round-trip times from 600-800ms to approximately 100ms. IAB UK

Rather than blowing up the stack, IAB Tech Lab is betting that agentic systems can run on top of familiar foundations like OpenRTB, AdCOM, and others, while layering in newer protocols such as MCP and Agent2Agent. AdTechRadar

Why it matters:

The IAB unifying under AAMP is a power move as much as a standards exercise. AdCP — the rival framework backed by Scope3, PubMatic, and others — has been gaining momentum as an agent-native alternative built from scratch. The IAB’s message with AAMP is direct: you don’t need a new stack. The existing stack can be agentified. Build on what’s already there.

Both arguments have merit. AdCP is cleaner and purpose-built for agent-native workflows. AAMP carries the weight of existing adoption and the trust of a standards body that’s been governing programmatic since the RTB era.

My read:

This standards fight matters less for what it decides technically and more for what it signals commercially. Whichever framework wins, the outcome is the same for B2B advertisers: AI agents are going to be making media buying decisions, negotiating deal terms, and optimising campaigns without human sign-off. That’s not 2029. Over half of brands are using or planning to use agents in 2026. AdTechRadar

The question is not whether your agency or DSP is building agent capability. They all are. The question is whether your first-party data and signal infrastructure is clean enough for an AI agent to act on it correctly — because a badly structured audience or a polluted intent signal doesn’t just underperform when a human is watching it. It compounds at machine speed and at scale. Garbage in, garbage out, at 100ms per cycle.

🧠 ZOOM OUT: What this week actually tells us

Shift 1: The supply chain is the new battleground — and everyone knows it. TTD vs the holdcos. Google’s remedy. IAB AAMP vs AdCP. LinkedIn building its own walled inventory. Every major story this week is fundamentally about who controls the path from audience to impression. The buyers — brands and B2B marketing teams — are largely spectators in a fight that directly determines the cost and quality of their media.

Shift 2: Intent is moving off the open web — and nobody has fully priced that in. The OpenAI/TTD story isn’t a DSP recovery narrative. It’s a signal that the dominant research interface for B2B buyers is shifting. If intent forms in AI assistants rather than on web pages, the entire signal layer that B2B programmatic runs on is pointing at the wrong place. The industry is building faster, more agentic infrastructure on top of a signal model that may be structurally eroding.

Shift 3: Measurement credibility is now a competitive advantage. LinkedIn’s “Cut the Bullspend” resonated this week because it named something true. CFO scrutiny on marketing spend is at a generational high. The teams that can demonstrate causal pipeline impact — not dashboard metrics — will get budget. The ones that can’t are already having difficult conversations. Incrementality measurement isn’t a nice-to-have. It’s table stakes by Q3.

⚙️ WHAT SMART B2B TEAMS DO MONDAY

Audit your agency agreement. If you’re running programmatic through a holdco, ask explicit questions. What is the gross cost vs net cost of every line? Is principal trading or proprietary inventory involved? The Publicis/TTD fight just gave you the justification to ask questions you should have been asking for years. Use it.

Map your intent signal sources. If your ABM motion depends on third-party intent from web-based behavioural signals, model what happens to that data if 20% of your ICP’s research moves into ChatGPT or Perplexity within 18 months. Because it’s already happening for some buyer segments.

Build measurement infrastructure now. Not a dashboard. Not a report. A methodology — ideally always-on incrementality — that you can take to your CFO and defend against scrutiny. The “bullspend” problem is real, but the solution isn’t switching platforms. It’s being able to prove, causally, what’s working.

Run LinkedIn with intent, not dependency. The platform is exceptional right now — better targeting, better formats, better measurement than it’s been. But the walls are getting higher. Run it hard while ensuring you’re building signal outside it simultaneously.

Don’t wait for the Google ruling. Start building curation-first supply relationships this quarter. Whatever Brinkema decides, it will take years to implement and Google will fight every word of it. Your supply strategy can’t wait for a court case that’s been running since 2023.

💡 Next week: The Dark Funnel Playbook — a full activation guide for building pipeline from the 80% of your ICP that will never fill in a form, click an ad, or identify themselves anywhere in your current stack. The accounts that are already deciding before you can see them.

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No prior form fill. No campaign touch. No attributed source. The SDR who logged it has written “inbound / unknown” in the notes field because that’s the only honest thing to write. Leadership asks where it came from. Marketing checks the attribution model and finds nothing. The contact, when asked how they heard of you, says something vague like “I’d seen you around” or “a colleague mentioned you.”

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In most revenue teams, this gets filed under “lucky.” A random inbound. Unexplained pipeline. The kind of deal you’re grateful for but can’t learn from, can’t replicate, and definitely can’t put in a board slide.

But here’s what I’ve come to believe after a long time watching these deals accumulate: they are not random.

They are the visible output of an invisible system — a months-long process of brand impression, peer validation, and autonomous research that happened entirely outside your tracked channels. The buyer didn’t appear from nowhere. They arrived pre-educated, pre-validated, and in many cases, pre-decided. You just didn’t have visibility into any of the journey that led them there.

This is the dark funnel. And the instinct to treat it as a measurement problem — to chase attribution, to install tracking tools, to add “how did you hear about us?” to every form — fundamentally misunderstands what it is and what to do about it.

The dark funnel isn’t a gap in your analytics. It’s where modern B2B buying decisions are actually made. And the organisations that understand this aren’t trying to illuminate it. They’re building strategic presence inside it.

Where Buying Decisions Are Actually Being Formed in 2026

To understand why the dark funnel has become so consequential, you need to start with a clear-eyed account of how B2B buyers actually conduct research today — which looks almost nothing like the linear content journey that most marketing funnels are designed around. The systems are grossly too simple for how complex the B2B journey is in reality

The modern B2B buyer is, first and foremost, deeply peer-networked. They have professional communities — some formal, most informal — where they ask questions, share opinions, and get vendor recommendations from people they trust far more than they trust any vendor. These conversations happen in private LinkedIn groups, in industry Slack communities (many of which have thousands of members and active daily discussion), in WhatsApp threads between former colleagues or groups like the Slack ones mentioned above, in Discord servers built around specific disciplines, and in the DMs of people who’ve become trusted advisors through years of LinkedIn connection. Revenue Collective, Pavilion, demand generation communities like Exit Five, category-specific forums — these are not niche edge cases. They are the primary research environment for a significant proportion of B2B buyers in technology and marketing.

The defining characteristic of all of these channels is that they are invisible to your analytics stack. Invisible today, invisible tomorrow and they will remain invisible forever. A buyer posting in a private community asking “has anyone used FunnelFuel?” will generate opinions, recommendations, warnings, and first-hand accounts from ten different people — none of which will appear anywhere in your attribution data. The entire conversation, which may be the single most influential input into that buyer’s shortlisting decision, will register as zero.

Review platforms represent another dark channel that is simultaneously more measurable and less understood than it should be. G2, Gartner Peer Insights, Capterra, and TrustRadius are heavily consulted during active evaluation phases — but buyers also browse them passively, long before an active buying process begins, forming impressions about vendor categories and the competitive landscape. The buyer who reads twelve reviews on G2 and forms a strong negative impression of your product has had a meaningful interaction with your brand that will shape their entire subsequent evaluation. You will almost certainly never know it happened. Not to mention the impact these platforms have on the sentiment presented back to the user on their favourite chatbot. These are a foundational part of the funnel, and are under estimated in their ability to forge narrative

Podcasts occupy a similar position. The B2B marketing and revenue ecosystem has a remarkably active podcast landscape — The Marketing Millennials, The Revenue Formula, Exit Five, The B2B Playbook, Demand Gen U, and dozens of category-specific shows with engaged professional audiences. A decision-maker who listens to a forty-five minute conversation featuring one of your leaders — or, equally, featuring your competitor — will be influenced by that content in ways that far exceed what you’d expect from a comparable amount of time on your website. The intimacy of the medium, the apparent authenticity of the conversation, and the passive consumption context all make podcast impressions stickier than most owned content. Attribution: zero.

LinkedIn itself is partly visible and partly dark. Company page impressions, paid campaign performance, and post engagement are measurable. But the dark social dimension of LinkedIn — content shared into DMs, screenshots circulated in WhatsApp groups, posts saved and returned to weeks later, organic algorithm reach into second and third-degree networks — is largely untracked. Research from Wynter and others into B2B buying behaviour consistently shows that LinkedIn content influences purchasing decisions that never surface in any marketing attribution model.

And then there’s the new dimension that changes everything.

The AI Research Layer: The Dark Funnel Just Got Pitch Black

Something has shifted in the past eighteen months that most B2B marketing teams have not yet fully reckoned with, even if they are faintly understood conceptually.

Buyers are now using AI-powered research tools — ChatGPT, Perplexity, Claude, Gemini — as active research assistants at every stage of the purchasing process. This is not a marginal behaviour. A 2024 survey by Forrester found that over 60% of B2B technology buyers reported using generative AI tools to assist with vendor research and evaluation, a number that has almost certainly continued to climb. Gartner has projected that by 2026, traditional search engine volume will fall by 25% as AI assistants absorb a significant proportion of informational queries.

The implications for B2B marketing are profound and largely unaddressed.

When a VP of Marketing at a mid-market SaaS company asks Perplexity “what are the best B2B programmatic advertising platforms for a company targeting enterprise accounts in financial services,” they will receive a synthesised answer drawn from whatever sources the AI has indexed, weighted, and deemed authoritative. That answer will influence whether you appear in their consideration set at all — and the buyer will have no awareness of, and no particular interest in, understanding what determined the AI’s response. They will treat it as a relatively neutral research output, in the same way a previous generation treated Google’s first page results.

The critical question this raises — and one that the B2B marketing community is only beginning to grapple with seriously — is: what determines whether your brand, your positioning, your distinct point of view appears in AI-generated research outputs?

The answer, based on the current evidence and the way large language models work, appears to be some combination of: the breadth and authority of your published content across the open web, the frequency with which your brand and specific claims are referenced in third-party sources (analyst coverage, review platforms, trade press, guest articles, podcast transcripts, community discussions), the clarity and consistency of your positioning language across those sources, and the degree to which your perspective is genuinely differentiated rather than category-generic.

This is, in a deep sense, the 2026 version of SEO. The underlying mechanisms are different — you’re not optimising for keyword rankings in a search index, you’re building the kind of distributed content presence that gives an LLM enough signal to include you as a credible answer to a relevant question. But the strategic logic is familiar: sustained investment in high-quality, genuinely useful content, consistently published and distributed across authoritative channels, creates compounding presence that eventually influences how your category gets described by automated systems.

What’s new — and what makes this more urgent than traditional SEO — is that LLM-generated research outputs are presented with an authority and completeness that search results are not. A search results page is obviously incomplete; the buyer knows they’re seeing ten blue links out of millions of possible results. An AI-generated research summary presents itself as a synthesis. The buyer’s instinct is to treat it as more definitive than it actually is. This means the stakes of appearing or not appearing in AI research outputs are higher than the equivalent SEO stakes — and the consequences of absence are more severe. This last point is largely unrecognised, in my opinion.

The industry is, understandably, concerned about this. Revenue teams watching unexplained inbounds arrive referencing “I did some research online” without being able to identify the touchpoint are experiencing a genuine measurement anxiety. The instinct is to demand a solution — a way to track AI-referred traffic, to measure LLM citation share, to get the dark funnel back under the attribution model. Several vendors are beginning to offer tools in this space: Share of Model tools that test how frequently your brand appears in AI responses to relevant category questions, AI SEO platforms that attempt to optimise content for LLM consumption, and attribution approaches that attempt to infer AI-influenced journeys from first-party data patterns. Bluntly I am yet to see an enterprise grade version of this concept, most are clunky and full of gaps, and seem to miss just how personalised results are alongside the depth of prompt versus old school keyword research - the former uses paragraph long unique inputs, the latter uses 1-2-3 keyword inputs. Clearly the latter is more modellable

That said, these tools have real value as directional intelligence. But the underlying anxiety they’re designed to address is worth examining critically, because I think it reflects the wrong frame.

Why Tracking the Dark Funnel Is the Wrong Instinct

The response of most marketing organisations to dark funnel activity — whether it’s community discussions, AI research, peer recommendations, or any other untracked channel — is an attribution reflex. If we could just measure it, we could optimise it. If we could just track it, we could report on it. If we could just connect it to pipeline, we could justify the investment.

This instinct is understandable in our data led world, and naturally, like many others, my views started here - how can we hack a track[ing solution]. Marketing has spent a decade building increasingly sophisticated measurement infrastructure, and the dark funnel represents a significant and growing portion of buying activity that sits entirely outside that infrastructure. The temptation to find a way to include it in the model is strong. FunnelFuel’s ABM analytics encourages this thinking.

But the attempt to force dark funnel activity into an attribution framework almost always fails, and the effort itself often crowds out more strategically valuable work. Here’s why.

Dark funnel channels are dark by nature, not by accident. The conversations that happen in private Slack communities are private because the participants want them to be and they would not be the same if they were public. The peer recommendations exchanged in DMs are personal because their value depends on that intimacy. The AI research process is opaque because that’s how these tools work. You cannot instrument these channels without fundamentally altering them — and in most cases, you cannot instrument them at all. The UTM parameter approach to dark social (using unique URLs for every distribution channel and inferring source from direct traffic patterns) produces data that is, at best, directionally suggestive and at worst, actively misleading.

More importantly, the questions that an attribution model is designed to answer — which channel drove this conversion, what was the ROI of this specific activity — are the wrong questions to ask about dark funnel presence. Dark funnel influence doesn’t work like a campaign. It doesn’t have a start date and an end date, a target audience, and a measurable conversion event. It works like a reputation — slowly, cumulatively, through repeated small impressions across many contexts, building a mental model in a buyer’s mind that may take months or years to translate into an active evaluation. Asking “what was the ROI of our G2 review strategy this quarter” is roughly equivalent to asking “what was the ROI of our CEO being well-regarded at industry events.” The question isn’t wrong, exactly, but the frame is too narrow and the timeline too short to produce a useful answer.

What effective dark funnel strategy requires is not better measurement. It’s a fundamentally different relationship with investment — one built around sustained presence and compounding brand equity rather than attributable campaign performance. This is, for most marketing leaders operating under quarterly pipeline targets, a genuinely difficult organisational and political challenge. But it’s the right challenge to take on, because the alternative is optimising exclusively for the 5% of buyer activity that your tools can see and ceding the other 95% to whoever is building the most effective dark funnel presence.

There is, however, one layer where measurement is both possible and important — and it’s one that tends to be significantly under-utilised. Your own first-party data, properly instrumented and combined with third-party intent signals and programmatic engagement data, can provide meaningful inferential signal about which accounts are in an active research phase, even when the research itself is happening in channels you can’t see. An account that goes dark on your CRM but simultaneously shows a surge in Bombora intent topics, new stakeholder visits to your website from previously unseen contacts, and increased engagement with your programmatic advertising is almost certainly doing research — somewhere. You don’t need to know it’s happening in a Slack community or a Perplexity session to act on the pattern. The convergence of signals is enough to adjust the account’s progression score and trigger the appropriate response.

This is a critical point: the dark funnel doesn’t mean absence of signal. It means the signals you have are inferential rather than direct. The job of a well-built account intelligence system is to read those inferences accurately and act on them at the right moment.

The Dark Funnel Playbook: What You Actually Do

If the goal isn’t measurement, it’s presence. And presence in the dark funnel is built through a specific set of activities, each of which requires sustained investment to produce compounding returns.

Build thought leadership that travels without you.

The most powerful dark funnel asset is a clearly articulated, genuinely differentiated point of view that other people want to share, quote, and reference in their own conversations. This is worth stating plainly, because most B2B thought leadership is not this. Most B2B thought leadership is category-confirming — it says the same things the category already believes, in slightly different language, with a vendor logo attached. This content does not travel. Nobody screenshots a blog post that confirms what they already think and shares it in their community Slack.

What travels is content that challenges a received wisdom, introduces a genuinely useful framework, makes a specific and defensible claim about where the category is heading, or articulates a frustration that the audience shares but hasn’t seen expressed clearly before. The article you just published on signal interpretation is a good example of this — “signal collection is commoditised, interpretation is the advantage” is a specific, contestable claim that buyers in the category will want to discuss. That’s the kind of IP that generates dark funnel presence: it gets quoted in community threads, referenced in peer conversations, and eventually indexed by the AI research tools your buyers will use.

The implication for content strategy is to produce less content and make each piece more genuinely useful and more distinctively voiced. Ten forgettable blog posts generate less dark funnel presence than one article that genuinely challenges how a senior practitioner thinks about their problem.

Equip your champions to carry your narrative.

In most B2B buying processes, your best advocates are not your marketing team — they’re the customers, partners, and peers who are already convinced and have the social capital to be persuasive in the communities where your buyers operate. These people exist in most organisations but are systematically under-activated. They have connections you don’t have, credibility you can’t manufacture, and access to conversations you’ll never be invited into.

Equipping champions means giving them content that is genuinely worth sharing — not case studies written to serve your sales process, but perspectives and frameworks that make them look smart when they share them with their networks. It means making it easy for them to tell your story in their own words by giving them clear, memorable positioning language. And it means systematically identifying who in your customer and partner base has the network and the inclination to carry the narrative, rather than assuming that all customers are equally valuable dark funnel amplifiers.

Customer advisory boards, practitioner communities built around your category rather than your product, and co-created thought leadership with key customers are all mechanisms for building this kind of distributed advocacy. They’re also exceptionally difficult to run well, which is precisely why most competitors don’t do it properly.

Build a review platform strategy, not just a review count.

Most B2B companies treat review platforms as a passive reputational layer — something to monitor and occasionally encourage customers to contribute to. This dramatically underestimates their strategic importance as a dark funnel channel.

G2, Gartner Peer Insights, and TrustRadius are active research destinations, not passive directories. Buyers in evaluation mode spend significant time reading reviews, looking for evidence of specific use cases, reading between the lines of critical reviews, and forming category impressions that will shape their entire evaluation framework. The quality, specificity, and recency of your reviews matters far more than the aggregate score. Reviews that describe specific problems solved, name specific features, and speak to specific company types are significantly more useful to a researching buyer — and significantly more likely to appear in AI-generated research outputs — than generic “great product, great team” five-star reviews.

A serious review platform strategy involves active management: identifying the customers most likely to write detailed, specific reviews and making it easy for them to do so, responding thoughtfully to critical reviews in ways that demonstrate genuine responsiveness rather than defensive deflection, and ensuring your category positioning on these platforms reflects how you want to be found by relevant buyer searches. It’s not glamorous work, but it compounds over time and increasingly feeds directly into the AI research layer.

Establish presence where your buyers are learning.

The community strategy question isn’t “should we be in communities?” — it’s “how do we build genuine presence in communities without being the vendor who ruins them?” The distinction matters because communities are acutely sensitive to brand intrusion, and getting it wrong produces the opposite of the intended effect: a vendor reputation for spam and self-promotion that travels through exactly the peer networks you were trying to reach.

Genuine community presence is built through contribution, not promotion. This means showing up with useful perspectives in discussions where you have something real to add, not posting vendor content into threads that don’t invite it. It means your most credible practitioners — people who are recognised as genuine experts in the category, which in your case means you — participating as practitioners rather than as vendor representatives. The line between “sharing a useful perspective” and “marketing myself” is porous and contextual, but experienced community members can always tell when someone is there to extract value versus contribute it.

Podcast appearances, guest contributions to respected trade publications, speaker slots at category events, and AMAs in relevant communities all build the same thing: a distributed body of evidence that your organisation has genuine expertise worth listening to, which is exactly the kind of signal that both peer networks and AI research tools use to determine whose perspective is worth amplifying.

Run always-on programmatic as the floor of your dark funnel strategy.

There is one channel that bridges the dark funnel and the measurable world: programmatic advertising targeted at your ICP accounts, running continuously across quality B2B publisher environments. This is not glamorous, and it is frequently the first thing cut when marketing budgets tighten. It is also, in the context of a dark funnel strategy, the most important always-on investment you can make.

Here’s why. Your dark funnel strategy will ensure that your brand, your ideas, and your positioning are circulating in the peer networks and research channels where your buyers operate. But buyers move between those dark channels and the visible web constantly. A buyer who encountered your perspective in a community discussion, or heard your name mentioned by a peer, will frequently conduct follow-up research that does touch your website, your paid media, and your owned channels. The question is whether you’re visible when that follow-up happens — whether your brand appears consistently across the premium environments they visit, whether your programmatic creative reflects the same positioning and ideas they encountered in the darker channel, and whether the experience of encountering your brand in paid media reinforces and extends the impression they already have.

Programmatic advertising in this context is not primarily about generating clicks or driving direct conversions. It’s about maintaining consistent brand presence across an entire ICP account list, so that when a buyer who has been educated by dark funnel activity finally surfaces into visible channels — a website visit, a form fill, an SDR response — the brand is already familiar. The research on this effect is consistent: buyers are significantly more likely to engage with and ultimately purchase from vendors whose brands they encountered repeatedly before the active buying process began. Binet and Field’s long-run research on advertising effectiveness, while originally focused on B2C, has been extensively replicated in B2B contexts by the LinkedIn B2B Institute, and the finding holds: brand salience built through sustained presence is one of the strongest predictors of competitive win rates.

The practical implication is that programmatic for B2B should be evaluated on a longer time horizon and a different success metric than most teams currently apply. Reach, frequency, and brand recall across the ICP are the right leading indicators — with pipeline contribution tracked on a six to twelve month lag, not a thirty-day attribution window. This is the hardest argument to make to a CFO, and the easiest to win with evidence once you’ve run the experiment long enough to see the data.

The Compounding Logic of Dark Funnel Presence

Here is the strategic insight that, in my experience, most B2B marketing leaders understand intellectually but struggle to act on operationally: dark funnel presence is not a campaign. It does not have a beginning, a middle, and an end. It does not produce a spike of attributable pipeline that you can point to in a board presentation. It works like compound interest — slow, invisible, and eventually transformative.

The organisation that has been consistently producing genuinely useful thought leadership for three years, has a distributed network of advocates carrying their narrative in peer communities, has 400 detailed G2 reviews that describe their product’s specific advantages in language that buyers actually use, and has been running always-on programmatic across their ICP for eighteen months will, other things being equal, win deals that start with an anonymous AI research query or a private Slack recommendation in ways that a competitor without that presence simply cannot. The advantage is not visible on any attribution dashboard. It shows up in win rates, in the frequency of “we’d already heard of you” at the top of discovery calls, in the pipeline that consistently appears with no attributable source.

This is the competitive moat that the dark funnel, properly understood, enables you to build. And because most competitors are still optimising for what their attribution model can see — still chasing the MQL, still cutting always-on brand spend when the quarter gets tight, still treating community contribution as a nice-to-have rather than a strategic priority — the bar for differentiated dark funnel presence is, right now, surprisingly low.

The buyers are there. The conversations are happening. The AI research queries are being typed. The peer recommendations are being sought. The only question is whether your brand is present in those moments — or whether you’ve ceded them to someone who understood the game earlier.

The Practical Starting Point

For most B2B marketing teams, moving from “dark funnel is a measurement problem” to “dark funnel is a strategic presence problem” requires a shift in how investment is justified as much as a shift in what activities are prioritised. That’s a leadership challenge as much as a marketing one.

But the practical starting point is simpler than it might appear. Audit where your last twenty deals actually came from — not what the attribution model says, but what buyers told you when you asked them directly. Look for the patterns in the “I’d seen you around” and “a colleague mentioned you” answers. Identify which community or peer channels your best customers inhabit and where they get their professional information. Understand what your presence currently looks like on G2, on relevant podcast circuits, in the communities where your buyers are most active. Ask your AI research tools — directly — how they describe your category and whether your brand appears in the answer.

That audit will tell you more about where your dark funnel presence currently is — and where the gaps are — than any analytics platform you own.

The rest is sustained investment in building something that compounds. Which is, in the end, exactly the kind of strategic work that separates the organisations who win in the long run from those still optimising for next quarter’s attribution report.

The B2B Stack is written by Mike Harty, a fifteen-year veteran of B2B programmatic advertising and founder of FunnelFuel.io. If you’re building a programmatic layer into your dark funnel strategy, FunnelFuel is where to start. Reply to this email or email mike [at] funnelfuel.io and I’ll point you in the direction of the right regional teams to get you started

FreeWheel shipped agent infrastructure for CTV and declared itself a neutral rail for AI buyers and sellers. Smartly signed a letter of intent to acquire INCRMNTAL and moved measurement inside the execution layer. LinkedIn expanded its creator and CTV moat and quietly became a harder wall to climb over. Viant posted record earnings by leaning fully into autonomous AI campaign execution. The Trade Desk, meanwhile, is facing pressure from every direction — fee compression, agentic disintermediation, and a CEO who’s simultaneously right about AI and wrong about how fast the floor drops. This week Publicis publicaly told their clients to avoid TTD.

Five moves. One direction.

The platforms that control signal, execution, and measurement in a single loop - rather than as three separate vendor relationships - are separating from the rest of the market.

This is what that looks like in real time.

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THE PATTERN - PROGRAMMATIC IS CHANGING RAPIDLY

For a decade, B2B programmatic ran on a three-layer stack. You bought data from one vendor. You activated through a DSP. You measured with a separate tool, usually quarterly, usually after the damage was done.

That model is being compressed into a single continuous loop - and AI agents are the mechanism doing the compressing. All of the major traditional layers of programmatic, most notably the Demand Side Platform, are under immense pressure. In my 15+ years in the space, this is unprecedented. The darling of independent adtech, once on a one way upward journey, is getting systematically attacked across the agency landscape

What’s accelerating this: the emergence of MCP (Model Context Protocol) as a shared language between AI systems and advertising infrastructure. Every major platform move this week — FreeWheel, Viant, even LinkedIn’s CAPI integration — is a play for position inside that loop.

The question for every B2B marketing team isn’t whether this happens.

It’s whether you’re building toward owning signal inside the loop, or renting access to someone else’s.

🔴 THIS WEEKS MAJOR STORY BREAKDOWN

TradeDesk: Three Holdcos in One Month — The Trade Desk Isn’t Losing an Audit. It’s Losing a War.

What happened:

In an email sent to select clients and obtained by Adweek, Publicis explained that The Trade Desk had failed a third-party audit conducted by FirmDecisions — a specialist media contract compliance auditor — that evaluated the platform’s fee structures and media and data spend. Adweek The audit found that TTD improperly applied its DSP fee to other fees it charged Publicis and some of its clients, billed clients for tools they were automatically opted into without evidence of authorisation, and did not provide the auditor with information necessary to validate that media and data costs were invoiced at cost and without mark-up. Adweek This follows Dentsu and WPP, who quietly stepped back from TTD’s OpenPath supply initiative weeks earlier over what they described as hidden fees, transparency concerns, and lack of clarity on where ads ran. Campaign Live Three of the Big Five holdcos. One month. All citing transparency.

The Trade Desk stock has retreated 33% in 2026. Campaign Live The CEO responded by personally buying $148 million of shares. The board lost a member. Rosenblatt and Stifel both downgraded the stock.

The official version — and why it’s only half the story:

Take the audit findings at face value for a moment. Fee complexity, auto-enrollment in tools, audit access refused. That’s a real problem and TTD’s own fee structure, transparent in principle, confusing in practice, has made it harder on itself. It has been slow — and I’d argue stubborn even — in response to legitimate concerns about the Kokai interface. Personally I never liked the withdrawal away from a programmatic traders interface and the force manoeuvre of putting new features into Kokai only. Worst then that, clients face more bureaucracy than they should when trying to access their own data. Adweek Those are genuine operational failures. Worth fixing. Worth holding TTD accountable for.

But they don’t explain the pattern.

Three holdcos. Same month. Same language. Same framing.

That’s not three separate audits arriving at the same conclusion independently.

That’s a coordinated moment.

My read: this is a territory war, not a billing dispute

Here’s the structural reality that nobody is saying loudly enough.

The Trade Desk spent years building the buy-side’s most powerful independent platform — and then made a strategic decision to move into the supply chain. This is becoming table stakes, and the programmatic ecosystem of Supply Side Platforms representing publishers (SSP’s), and Demand Side Platforms (DSP’s) representing media buyers is dead. This was a significant line of discussion that I had with adtech titans in New York during the first week of March. Both sides have converged into a singular access layer, and their competition is now less themselves, as truley alterntive ways of buying media, driven by AI protocols.

Thus, OpenPath wasn’t just a transparency initiative. It was designed to make ads perform better and help buyers directly purchase publishers’ inventory — a viable alternative to Google’s infrastructure. Adweek That sounds great for advertisers. And maybe it is.

But here’s what OpenPath actually does to the holdco trading desk model: it removes the agency’s supply-path optimisation layer from the equation. SPO is one of the services agency trading desks charge for — and earn margin on. OpenPath, positioned as “at cost,” directly threatens that revenue line. Agencies have spent years accepting lower takes and making their margin opaquely out of media deals, and curation has opened up a route to making margin from the supply layers. This very much links to our discussions about Principal Media, and the billion dollar agency revenue game that comes with it

Industry observers pointed to potential overlap between OpenPath and services traditionally handled by agency trading desks, including supply-path optimisation and inventory curation. e4m When WPP cited “overlap with agency services” as a concern about OpenPath, they weren’t confused about what OpenPath does.

They were telling TTD to get out of their lane.

The Publicis audit, real as its findings may be, arrived at exactly the right commercial moment to give the holdcos a legitimising narrative for what is fundamentally a power move. You can’t tell your clients “we’re stepping back from TTD because it competes with our trading desk margin.” But you absolutely can tell them “we failed an independent audit and we have a responsibility to protect your media spend.”

Same outcome. Much cleaner story.

The irony that the industry isn’t saying:

The loudest voices calling out TTD on transparency are the same organisations currently facing their own transparency crisis.

The world’s largest agencies essentially pitch for free and operate on razor-thin disclosed margins. To survive, they have to generate profit from hidden fees and undisclosed arrangements. Adweek WPP is currently facing a $100 million whistleblower lawsuit alleging nearly $1 billion annually in undisclosed principal trading income — with 97.4% of GroupM’s proprietary inventory allegedly unused by its own largest clients. The same week they exited OpenPath over “transparency concerns.”

That is not a small irony.

It’s the defining contradiction of the current holdco moment.

What TTD’s actual problem is:

Strip out the agency politics and TTD still has a real structural challenge — one that exists regardless of who’s auditing them.

Unlike Google, Amazon, and Meta, which benefit from inventory ownership and closed-loop measurement systems, The Trade Desk sits entirely on the buy side. That independence is the product. But that same structure means TTD has no captive inventory to fall back on if clients walk. 24/7 Wall St. Publicis accounts for more than 10% of TTD’s gross billings. Two ad holding companies would each account for over 10% of 2025 gross billings — combined, 30%. Investing.com A DSP built entirely on the buy side, with no inventory ownership, dependent on holdco relationships for 30% of gross billings, now facing fee pressure from agentic systems that automate what human traders used to do manually — that’s a structural pressure stack, not a billing dispute.

Jeff Green’s $148 million personal share purchase says he believes the platform wins long-term. He’s probably right about the thesis. The question is whether he’s right about the timeline.

The supply-side signal worth tracking - where is all this ad spend going?

The deeper shift underneath this story — and one that barely got coverage — is where the smart money is actually moving.

As TTD gets squeezed from the buy side, the supply side is having a very different week. Curation platforms, deal marketplaces, and SSP-layer infrastructure are gaining traction precisely because they offer what OpenPath promised but without the conflict of interest: transparent supply paths, brand-safe inventory packaging, and signal-rich deal IDs that don’t require the buyer to route through a DSP’s proprietary pipe. They also offer post auction discounts, which equate to revenue lines for agencies

The agencies exiting OpenPath aren’t abandoning supply-path thinking. They’re building their own. Publicis has Epsilon. WPP has its own data infrastructure. They want the supply advantage TTD was building — they just want to own it themselves.

This is the curation layer becoming contested territory. And it confirms something we’ve believed at FunnelFuel for a while: the value in B2B programmatic isn’t at the DSP execution layer. It’s in proprietary signal, curated supply, and the intelligence layer that sits between audience data and inventory — the part that’s genuinely hard to replicate and impossible to audit away.

Your takeaway:

Stop reading this as a story about TTD’s billing practices.

Read it as a story about who controls the supply chain in a post-OpenRTB world — and why every major player is willing to pick a very public fight to answer that question.

The holdcos want the supply advantage back. TTD wants to disintermediate the holdcos. Neither side is being fully honest about why.

The B2B buyers in the middle should be asking a different question entirely: if my DSP and my agency are fighting over who controls the path to inventory, what does that mean for the quality, transparency, and cost of the media I’m actually buying?

The answer is: more than you think. Not for the first time, it is why you need expertise on your side. Simply reply to this email, it goes straight to my inbox, and we will bring you up to speed on what we’re doing at FunnelFuel to aid B2B buyers in navigating this complex world

Sources: Adweek — Publicis leaked memo and FirmDecisions audit reporting,

17 March 2026 AdAge — Publicis no longer recommends TTD,

17 March 2026 Adweek — Dentsu and WPP OpenPath exit,

February 2026 Campaign US — TTD/holdco fallout TL;DR,

March 2026 Adweek — The Trade Desk-Publicis Fight Is Really a War Against Transparency,

March 2026 24/7 Wall St. — TTD two-day selloff and CEO share purchase reporting

Investing.com — Rosenblatt and Stifel downgrades

STORY 2: FreeWheel ships an MCP server — the agent infrastructure race is live

What happened: FreeWheel launched AI agent infrastructure for CTV this week — an MCP server that lets agency AI systems plug directly into FreeWheel’s platform, with built-in intelligence tools that monitor deal performance and trigger optimisation without human input. PMG signed on for the pilot.

Why it matters: This is the first time a major sell-side platform has shipped agent infrastructure as a product, not a prototype. At CES, NBCU, FreeWheel, and Newton ran a live experiment showing AI agents planning and optimising a campaign by communicating directly over MCP. This week, that architecture became commercially available.

The infrastructure question underneath this: who becomes the connective tissue between AI agents and inventory? FreeWheel is betting the answer is “neutral SSP-layer infrastructure.” The platform that controls that endpoint controls the pricing relationship — not the agency, not the brand.

This captured my attention because it points to the post programmatic competition that I outlined above as the real competitors against the likes of TradeDesk.

My view: There’s a standards war running in parallel. AdCP ((backed by Scope3 and Brian O’Kelly - BOK is an adtech godfather and Scope3 has real credibility within adtech circles as a result), PubMatic, Yahoo, Triton, and others)) is building an agent-native protocol from the ground up — the OpenRTB of the agentic era. ARTF is a rival framework. Messy coexistence for 18–24 months is the likely outcome, then convergence. Don’t pick a side yet. Do ask your agency what their MCP integration strategy is — because within two years, their AI agent will be making deal decisions on your budget without asking.

STORY 3: Smartly acquires INCRMNTAL — measurement is being absorbed into execution

What happened: Smartly signed a letter of intent to acquire INCRMNTAL, a Tel Aviv-founded incrementality measurement startup that measures causal lift without user-level tracking and without pausing campaigns. The deal closes within weeks.

Why it matters: INCRMNTAL does something the industry has talked about for years but rarely operationalised: always-on, causal measurement that doesn’t require a controlled experiment. No holdouts. No paused campaigns. No quarterly retrospective. It analyses natural variation in campaign activity to calculate true incremental impact across every channel — CTV, DOOH, audio, social, display.

Smartly’s existing platform processes hundreds of billions of creative assets annually. Pulling real-time measurement inside that execution engine means the feedback loop between “what ran” and “what worked” shrinks from weeks to hours.

My view: This is bigger than one acquisition. It’s a signal that measurement is leaving the strategy deck and entering the activation layer permanently. This will be a huge component of agenctic buying. For B2B teams with 60–90 day sales cycles and attribution models that barely hold together, this points to where the industry is heading: incrementality as a live signal inside campaign execution, not a post-mortem you run once a quarter and file somewhere. Start asking your measurement vendor how they plan to survive when the DSP does this natively.

STORY 4: LinkedIn becomes a full-stack B2B media platform — and the walls just got higher

What happened: LinkedIn announced Premium Creator Sponsorships with Top Voices 360 this week — brands can now secure exclusive editorial shows via BrandLink, extend into co-branded posts, industry events, and content integrations, all inside Campaign Manager. CTV placements are now purchasable through Campaign Manager directly or programmatically via The Trade Desk.

Why it matters: Look at what LinkedIn has assembled in 12 months: verified professional targeting, Thought Leader Ads (company-boosted personal content which talks to the gold rush around B2B influencer marketing), CAPI/Qualified Lead Optimisation for first-party CRM signal integration, CTV distribution, and now a creator commerce layer with direct payments to Top Voices via Stripe.

That’s not a social platform making incremental improvements. That’s a walled garden being constructed specifically around the B2B buyer journey — from awareness through creator content, mid-funnel through account-level retargeting and intent signals, conversion through lead gen forms, and now living-room presence through CTV.

My View: LinkedIn is going after the entire GTM funnel and it’s winning. The platform holds something the open web cannot replicate: verified professional identity at scale. They claim ~1.3B users. 82% of B2B marketers using influencer campaigns on the platform now say they’re essential to ROI. The risk isn’t that LinkedIn doesn’t work. It’s that it works well enough that you stop building signal outside it — and then you’re entirely dependent on a platform that owns your audience data and can change pricing at will.

Let that last line sink in - apathy here could cost B2B vendors big time in the future.

The B2B teams that win long-term are the ones running LinkedIn hard and building owned signal infrastructure in parallel. Not instead. In parallel.

LinkedIn is very strong at reaching some personas and surprisingly weak in reaching others. No matter how much the chatter may suggest otherwise, LinkedIn is not B2B in its entirety.

💡 Want the tactical breakdown? This week’s deep dive: The Dark Funnel Playbook — how to build pipeline from accounts that never fill in a form, click an ad, or raise a hand.

🔴 STORY 5: Viant posts record earnings — this is what “open internet, done right” looks like

What happened: Viant Technology (Nasdaq: DSP) reported record Q4 and full-year 2025 results this week. CTV accounted for 46% of total advertiser spend in Q4 — a record. The headline from CEO Tim Vanderhook: the launch of Outcomes, described as a fully autonomous AI decisioning solution built specifically for the open internet.

Why it matters: Two DSPs. Two very different stories. The Trade Desk guided soft last month and got punished — stock down 16%, questions about structural headwinds across open-web programmatic. Viant hit record numbers and investors were pleased. The divergence isn’t about company size or market share.

It’s about positioning.

Viant leaned fully into autonomous AI execution and CTV-first, first-party addressability. TTD has been more measured — rightly articulating the long-term AI opportunity but defending ground that’s under pressure in the short term. Markets reward narratives that match the moment.

My view: The open internet isn’t dying, and despite all doom mongering to the contrary, it is still growing. It’s also bifurcating. Platforms executing banner campaigns at impression scale with legacy optimisation logic: margin-compressed, commoditised, losing budget share. Platforms running outcome-based, agent-optimised, CTV-anchored programs with proprietary addressability: growing. Viant’s result is proof the second category is real. For B2B buyers, this means the DSP conversation in 2026 isn’t “which platform has the best UI.” It’s “which platform has its own signal layer and can run autonomous optimisation against my actual business outcomes.”

🔴 STORY 6: The Google AdX ruling still hasn’t landed — and the silence is its own signal

What happened: Nothing. That’s the story. Judge Brinkema’s structural remedy ruling — the decision that will determine whether AdX gets divested or Google escapes with behavioural requirements — was expected before the end of 2025. It’s now March 2026 and the market is still waiting. European regulators are holding parallel proceedings pending her decision.

Why it matters: Divestiture of AdX looks increasingly unlikely based on the judge’s line of questioning during November’s closing arguments. What’s more probable: behavioural remedies that include Unified Pricing Rules reform, DFP interoperability with Prebid, and data-sharing with publishers. Meaningful on paper. Less disruptive in practice than the industry hoped.

But here’s what the delay confirms: Google’s structural hold on the open web’s premium publisher supply — AdSense placements, small publishers who’ve never heard of programmatic — isn’t going anywhere fast. For B2B buyers targeting niche professional content, that supply remains uniquely valuable and uniquely concentrated.

My View: Wider access to YouTube and wider access to the incredibly valuable niche B2B publishers using adsense, are the two reasons that I am following this - the third is morbid curiosity.

My general view? Stop waiting for the ruling to change your supply strategy. The curation layer is the answer regardless of what Brinkema decides. Deal-based buying through curated supply paths — where you can control brand safety, signal quality, and transparency without depending on Google’s infrastructure — is the right strategic direction for B2B programmatic in 2026 whether AdX gets broken up or not. Build your supply relationships like the ruling changes nothing. Because it probably won’t change enough.

🧠 ZOOM OUT: What this week actually tells us

Shift 1: The execution layer and measurement layer are merging — permanently. Smartly/INCRMNTAL is one data point. Viant’s Outcomes autonomous decisioning is another. FreeWheel’s agent infrastructure is a third. The three-vendor stack — data, activation, measurement — is being replaced by a single continuous loop. This has been theorised for years. This week it started shipping as product.

Shift 2: Every platform is building a proprietary moat around signal — and making it harder to leave. LinkedIn’s creator layer. FreeWheel’s MCP endpoint. Viant’s first-party addressability suite and outcomes based billings. They are all doing the same thing: capturing signal closer to the buyer and making that signal non-portable. The open ecosystem remains the scale layer. Proprietary signal ownership is the margin layer.

Shift 3: The agent rail will be owned by whoever ships the infrastructure first. AdCP vs ARTF is a surface-level standards fight. The real contest underneath it is for who becomes the connection between AI agents and inventory — permanently. That’s an infrastructure position worth decades of margin advantage. The platforms moving now have the same window the early SSPs had when header bidding launched. That window closes faster than people expect.

⚙️ WHAT SMART B2B TEAMS SHOULD DO MONDAY

Stop treating measurement as a separate workstream from activation. It’s being absorbed. Get ahead of it by demanding real-time incrementality reporting from your current vendors now — before your DSP does it natively and you lose visibility into how the numbers are being calculated.

This is exactly why, when I started FunnelFuel I began with B2B analytics. Measurement and activation need to live in one data warehouse so they can fuel each other. That was evident 3 years ago.

Start asking your agency what their MCP strategy is. Not as a technical question. As a commercial one. If their AI agents are making autonomous deal decisions on your budget, you need to know who they’re accountable to.

Audit your LinkedIn dependency and build outside the walls in parallel. The platform is exceptional. It’s also becoming more expensive and more closed by the month, with ambitions which could tread on toes. Own your audience development alongside it.

Watch Viant’s Outcomes product. If autonomous AI campaign execution delivers at scale in H1 2026, the conversation about managed services in B2B programmatic changes fast.

Pressure-test your supply chain assumptions before the Google ruling lands. Build curation-first supply relationships now. Whatever Brinkema decides, the open web premium supply question doesn’t resolve cleanly.

📬 FROM THE B2B STACK — IN CASE YOU MISSED IT

🔗 The Dirty Secret of B2B Intent Data Intent data is now table stakes in almost every B2B stack. But most teams are buying the same signals, from the same sources, pointed at the same accounts — and wondering why pipeline isn’t moving. This piece unpacks why third-party intent has become a commodity, what the signal actually tells you versus what vendors claim it tells you, and what the teams generating real pipeline are doing differently. If your ABM motion is built on Bombora alone, this one is required reading before your next stack review. →

⏭️ COMING NEXT WEEK: The Dark Funnel Playbook — A look at the channels your web analytics will never see. Why trying to measure them is the wrong approach, but equally how you can leverage dark signals to win in an increasingly pitch black world

Subscribe below to get it in your inbox the moment it drops on Tuesday.

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If STACK SIGNALS helped you see this week differently — that’s the point.

Every Friday, we pull the signals out of the noise and tell you what they actually mean for B2B pipeline, media strategy, and the platforms you’re betting on. No recaps. No “here are five trends.” Just the implication layer — written by someone who’s been operating in programmatic for 15 years and has seen most of these cycles play out before.

And if you’re rethinking how signals turn into pipeline — that’s exactly what we’re building at FunnelFuel. Reply to this email or email mike [at] funnelfuel.io and I’ll point you in the right direction to learn more

📚 SOURCES THIS WEEK

AdTechRadar — FreeWheel MCP server / AI agent infrastructure launch, March 2026 AdExchanger — MCP universal adapter explainer; Smartly/INCRMNTAL acquisition reporting Mobile Marketing Reads — Smartly/INCRMNTAL deal detail, March 2026 Global Dating Insights / LinkedIn official — Top Voices 360 / CTV expansion announcement, 19 March 2026 Viant Technology SEC filing (Nasdaq: DSP) — Q4 and FY 2025 earnings, 11 March 2026 Digiday — Ad Tech Briefing: Industry rethinking foundations, March 2026 AdExchanger — 2025: The Year Google Lost In Court And Won Anyway, January 2026 Digiday — Google AdX closing arguments and remedies reporting ADOTAT — The Agentic Advertising Survival Guide for 2026, February 2026 Marketing Dive — AdCP launch and agentic programmatic standards

Marketing has invested in an intent data platform — Bombora, G2 Buyer Intent, TechTarget Priority Engine, 6sense, Demandbase, take your pick. Dashboards are live. Signals are flowing. A weekly export goes to sales. Leadership is excited. Slides with heatmaps of “surging accounts” get presented in QBRs.

Six months later, the tool is under review. Pipeline impact is unclear. Sales reps say the data is noisy. Marketing says sales isn’t following up. And the vendor’s customer success team is desperately scheduling a “re-engagement call” to prove ROI before the renewal conversation.

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This story is so common it’s practically a genre. It pretty much is B2B in 2026

And it’s not a technology problem.

The platforms themselves are genuinely sophisticated. The data, while imperfect, contains real signal. The problem is almost always the same: organisations have invested in signal collection without building the machinery for signal interpretation. They’ve wired up sensors without building the intelligence layer that knows what to do when the sensors fire. A critical missing piece of the infrastructure is the ability to pipe these signals back into the marketing channels which have significant data exhaust - like premium (in other words ‘done right’) programmatic, where the interpreted signal can drive bidding fidelity, and ultimately account progression.

After fifteen years of working inside B2B and programmatic advertising — building Demand Side Platforms (4 and counting), a Sell side platform, a publisher adserver, an ad exchange (3 and counting), dynamic creative solutions, a patent for contextual advertising - alongside operating the tech in 2 different services businesses meaning building audience strategies, architecting intent layers, running media activations across the full demand spectrum — I’m convinced that signal interpretation is the most under-invested capability in modern B2B marketing. It’s also where the real competitive advantage now lives.

This post is my attempt to articulate why most signal strategies fail, and what the ones that actually work have in common.

The Data Abundance Problem

B2B companies have never had more behavioural data about their buyers. The modern revenue stack is typically instrumented with website analytics, marketing automation, CRM activity, intent data platforms, installed-tech intelligence (Bombora’s technographic layer, HG Insights, Slintel), G2 and Gartner Peer Insights review activity, hiring signal feeds, content engagement scoring, LinkedIn interaction data, community intelligence, and in some cases dark social monitoring tools like Sparktoro or Wynter.

That’s a genuinely impressive set of sensors. And yet, in most organisations, this data is not creating proportional insight.

The paradox is that more signal data often makes the problem worse, not better. When everything is a signal, nothing is a signal. Teams get flooded with activity data, can’t distinguish meaningful patterns from noise, and gradually lose confidence in the entire approach. The intent data platform becomes one more dashboard that people check less and less frequently, until it becomes background infrastructure that nobody really trusts and nobody really owns.

The issue isn’t the data. It’s the interpretive framework around it.

Why Signals Are Not Leads — And Why This Distinction Matters Enormously

For most of the past two decades, B2B demand generation was built on a remarkably simple and remarkably durable model: capture engagement, create a lead, pass to sales. The underlying logic was linear. A person who downloads a guide has shown intent. A person who attends a webinar is warmer still. A demo request is the warmest signal of all. MQL scores were essentially a formalised version of this intuition — a numerical proxy for purchase readiness built on engagement thresholds.

Intent data has disrupted this model conceptually, but most organisations are still operationally running the old playbook. The signal has replaced the lead as the input, but the output is exactly the same: a list of accounts, a sales sequence, an outreach motion. The machinery hasn’t changed.

This matters because signals are fundamentally different from leads. A lead is a direct expression of a person’s willingness to be contacted — they submitted a form, they said yes. A signal is an inference drawn from observed behaviour. It tells you something happened, but it doesn’t tell you what it means without additional interpretation. We got hooked on the crack of inference signals without really understanding what they meant.

A contact downloading a thought leadership guide on supply chain resilience is probably learning about the topic. A contact visiting your pricing page three times in a week after attending two product-focused webinars is signalling something categorically different. Yet most sales teams receive both as equivalent “engaged account” notifications and respond with the same outreach sequence.

The consequences of this conflation are predictable. Buyers in early discovery stages get pitched before they’re ready, disengage, and go dark. Your domain gets mentally tagged as “too salesy.” And because the buying process is long — Gartner’s research suggests enterprise B2B sales cycles now routinely extend to 12–18 months — burning that early relationship is costly in ways that rarely show up in attribution models.

LinkedIn’s B2B Institute has documented what they call “the 95-5 rule”: at any given time, roughly 95% of your total addressable market is not actively in a buying cycle. They are, however, forming preferences, building mental models of the category, and developing opinions about vendors — all of which will influence the buying decision when the moment eventually arrives. Engaging those accounts with aggressive sales outreach doesn’t just fail; it actively damages your position for the future moment when they do enter market. Engaging them with information which is genuinely useful and accretive to their journeys progression provides positive brand memory.

I’d argue that if you lack information which definitively says an account is actively IN-MARKET RIGHT NOW then the default should be to the latter types of content

This is the foundational mistake of most signal strategies. They treat every signal as evidence of near-term purchase intent, when most signals are evidence of something far earlier and more fragile: curiosity.

The Dark Funnel Reality

The urgency around signals isn’t arbitrary. It’s a direct response to how B2B buying behaviour has fundamentally shifted over the past decade.

Gartner’s research has consistently shown that buyers now complete the majority of their research independently before engaging vendors — their widely cited figure is that B2B buyers spend only 17% of their total purchasing journey in direct supplier interactions. When multiple vendors are under consideration, that number fragments further: each vendor may capture less than 5% of total buyer attention across the entire journey.

The implication is significant. The traditional lead capture model — gate your best content, require a form fill, trigger a nurture sequence — was built for a world where buyers expected to identify themselves to get information. That world has largely ceased to exist. Buyers now have access to analyst reports, peer review platforms, community forums, vendor comparison sites, LinkedIn peer networks, and increasingly, AI-powered research tools that can synthesise competitive landscapes without a buyer ever touching a vendor’s website. My chats with folks across the industry have validated my view that buyers are putting extreme - and probably unwarranted - levels of faith in these LLM chatbot tools. Gartner’s research also shows that buyers who use self-service digital channels to complete their research are more likely to experience purchase regret — a finding that suggests the research process itself has become so autonomous that vendor relationships are being established too late to add real value. All of this highlights the extreme need for always on brand activity

The buying group dimension makes this more complex still. Research from 6sense and Demandbase puts the average B2B buying committee at 6–10 stakeholders for enterprise decisions, and some analyses of complex technology purchases push that number higher. Crucially, most of those stakeholders will never fill out a form, request a demo, or identify themselves in any traditional way. They research anonymously, sometimes behind VPNs that mask even firmographic data. They consult peers in private Slack communities and WhatsApp groups. They ask questions on Reddit and LinkedIn. They exchange opinions in channels that are invisible to every analytics platform in your stack.

This is what Chris Walker and others in the demand generation community have taken to calling the “dark funnel” — the substantial portion of buyer activity that occurs in places your tools cannot see. Intent data platforms are a partial, imperfect window into that dark funnel. Their value isn’t perfect accuracy; it’s directional intelligence. A surge in Bombora intent signals for “B2B programmatic advertising” across a cluster of accounts in your ICP doesn’t mean those accounts are definitely evaluating you. It means something is happening, and that something is worth investigating.

Signals are, in this sense, an intelligence function as much as a marketing function.

The Three Failure Modes

Across the signal-driven programs I’ve worked on and observed, failure almost always traces back to one of three patterns.

Failure Mode One: Optimising for signal volume instead of signal quality.

The path of least resistance when implementing an intent data strategy is to track everything. Every page view. Every email open. Every webinar attendance. Every social interaction. Modern martech infrastructure makes comprehensive tracking trivially easy (at least on the surface, we could dig a lot deeper here and fill a whole newsletter), which means the limiting factor isn’t data collection — it’s knowing what to do with what you’ve collected.

The reality is that most signals, taken in isolation, are nearly meaningless. A single page visit tells you almost nothing. A single content download is barely signal at all. What matters is patterns — sequences of behaviour across multiple stakeholders within an account, combinations of signals from different sources that, read together, suggest something coherent about where an account is in its journey.

The organisations that get real value from intent data have usually done the hard work of mapping which signal combinations have historically correlated with pipeline creation. They’ve looked backward at their won deals and asked: what signal patterns appeared in the six months before these accounts converted? What does a real buying motion look like in the data, as opposed to a research curiosity? This is fundamentally an analytical exercise, and it requires close collaboration between marketing operations and sales — which is exactly why most teams never do it.

Failure Mode Two: Conflating engagement with buying intent.

This is the subtler version of the lead-versus-signal problem. Even sophisticated teams who intellectually understand that signals require interpretation often operationally behave as though engagement equals readiness. The pressure to activate pipeline — from leadership, from sales, from the board — creates an incentive to treat every positive signal as a green light.

The result is premature activation that poisons the well. Buyers in the learning phase are highly sensitive to being pitched. Research from the LinkedIn B2B Institute suggests that buyers who are engaged with content but not yet in an active buying cycle respond negatively to direct sales outreach — and that the negative effect on brand perception persists even after those buyers eventually enter market. You don’t just lose the immediate conversation; you lose positioning for the future one.

The most effective signal-driven organisations have built explicit stage models that separate “demand for knowledge,” “demand for solutions,” and “demand for vendors” — and they run fundamentally different engagement playbooks at each stage. Early stage signals trigger content distribution and nurture. Mid-stage signals trigger account intelligence reviews and potential light sales development activity. Late-stage signals — which are typically combination signals, not single events — trigger active sales engagement. The playbook is calibrated to buyer stage, not simply to engagement level.

Failure Mode Three: No operational ownership of the interpretation layer.

Even when organisations collect high-quality signals and have conceptually sound frameworks, signals frequently die in the gap between marketing data and sales pipeline. Marketing owns the platforms. Sales owns the relationships. The signals live awkwardly between the two.

The result is a slightly more sophisticated version of the old MQL handoff: an engaged account list goes to sales, sales reps pick the names they already recognise or the accounts closest to existing opportunities, and the remainder disappear into the CRM’s “nurture” purgatory. A tool that was supposed to generate pipeline intelligence becomes a source of more leads that nobody follows up on properly.

The missing ingredient is what I’d describe as a signal operating model — a structured, recurring process by which signal data is collectively reviewed, interpreted in context, and translated into specific account-level actions with clear ownership. This is an organisational design problem, not a technology problem. You can buy all the intent data in the world and still produce nothing if you haven’t solved the operating model.

Building a Signal-Based Revenue Engine: A Practitioner’s Framework

The organisations I’ve seen do this well share a common architecture. It’s not proprietary or particularly complex, but it requires genuine investment in process — which is precisely why most competitors skip it.

Step One: Signal curation, not signal collection.

The starting exercise is to dramatically reduce the number of signals you’re tracking and investing in — and replace breadth with precision. The right question isn’t “what can we track?” It’s “which signal patterns have historically appeared before our best deals closed?”

This requires a retrospective analysis of your won opportunities. Look at the 20 or 30 accounts that converted in the past 12–18 months and examine the signal history for each. What intent topic clusters were surging before the conversation began? Which pages on your site did stakeholders visit, and in what sequence? What was the content engagement pattern — was it heavy on educational content early, shifting to product content later? Were there hiring signals — roles that suggest the account was building capability in your solution area — before the buying process started?

This analysis almost always produces surprises. The signals that correlate most strongly with pipeline creation are rarely the ones that look most impressive on a marketing dashboard. And the signals that seem most intuitive — a visit to your pricing page, a demo request — often turn out to be lagging indicators that appear after the buying decision has already been substantially formed, not leading ones you can use to get ahead of the curve.

Build your signal taxonomy around the patterns that actually predict pipeline. For most B2B organisations, this will be a relatively small set: three to five high-confidence signal combinations that consistently appear before an account moves into active consideration. Instrument everything else, by all means, but make those patterns your operational priority.

Step Two: Map signals to buying stage with granularity.

Once you’ve identified your high-signal patterns, map each to a stage in the buying journey. A useful framework distinguishes three phases: Demand for knowledge (the account is building awareness and understanding of the problem space), Demand for solutions (the account recognises the problem and is evaluating approaches), and Demand for vendors (the account is actively comparing alternatives and building a business case).

Signal characteristics vary significantly across these stages. Early-stage demand typically shows up as broad intent topic surges around category-level keywords — Bombora topics like “B2B marketing automation” or “account-based marketing” rather than anything vendor-specific. Consumption patterns tend to be scattered across multiple content types. Stakeholder footprint is often narrow, sometimes just one or two individuals.

Mid-stage demand shows more concentrated behaviour: returning visits to content on specific use cases, attendance at solution-focused webinars, engagement with competitive comparison content (G2 and Gartner review activity is a useful signal here), and the appearance of additional stakeholders from the same account across different content properties. At this stage, buying groups are forming and starting to align on approach.

Late-stage demand looks quite different again: high-intent page visits (pricing, case studies, ROI calculators, specific product feature pages), multi-stakeholder engagement within compressed timeframes, re-engagement from contacts who had been quiet, and sometimes direct outreach from the account itself. This is the stage where sales engagement becomes appropriate — and notably, it’s also the stage where many buyers have already substantially narrowed their vendor shortlist. Getting in at this stage means competing on terms the buyer has largely set.

Step Three: Build an account intelligence layer, not an account list.

Signal data is almost always more useful in context than it is in isolation. A visiting from a specific account means very little without knowing the history of that account’s relationship with your organisation: what conversations have already happened, which stakeholders have previously engaged, what the account’s strategic context looks like, whether there’s an existing champion who’s gone quiet or a new stakeholder who’s appeared.

The best-performing signal-driven organisations build what I’d call an account intelligence layer — a living narrative document for each priority account that combines signal data with CRM history, account research, news and trigger events (funding, executive changes, acquisitions, regulatory shifts), and any conversation intelligence from Gong or Chorus calls. Tools like Clay have made this significantly more accessible in recent years, enabling enrichment pipelines that pull together firmographic data, intent signals, technographic information, and news triggers into a single account view.

The goal of this layer is to move from “this account is showing intent signals” to “this account is showing intent signals and here’s the context that tells us what those signals likely mean.” The interpretation is almost always richer when signal is combined with situational awareness.

Step Four: Design playbooks that are calibrated to signal, not just triggered by it.

Signal playbooks are the mechanism by which interpretation becomes action. The key design principle is that the playbook should be calibrated to the signal type and stage, not simply triggered by any engagement.

A first-party signal — a senior stakeholder visiting your solutions page after a period of dark — warrants a very different response than a third-party intent surge with no prior relationship history. A new stakeholder appearing within an account you’re actively pursuing warrants a different response than the same account returning to top-of-funnel content after previously engaging with product-level content.

I typically design playbooks around three scenarios: the re-engaging champion (someone you know who’s come back to you), the dark account awakening (an account you’ve had limited prior contact with that’s now showing clear intent signals), and the buying group expansion event (new stakeholders appearing within an account where a relationship already exists). Each scenario has meaningfully different implications for how to engage, through which channels, with what content, and at what pace.

The common thread is that playbooks should inform and accelerate human judgment, not replace it. Automation has a role in signal-triggered workflows, but the most important signal-to-revenue conversions almost always involve a sales or marketing professional making a contextually intelligent decision about how to engage — not an automated sequence firing because a threshold was crossed.

Step Five: Operationalise signals in revenue rituals.

All of the above is theoretical unless signals are embedded in the actual rhythms of how your revenue team operates. The best implementations I’ve seen do two things consistently.

First, they include a structured signal review in weekly pipeline meetings — not as an appendix to the main conversation, but as a standing agenda item of equivalent importance to the active opportunity review. High-signal accounts are assessed alongside active pipeline, signals are interpreted collectively by marketing and sales together, and actions are assigned with owners and timelines. This meeting structure ensures signals remain an active operational input rather than a dashboard curiosity.

Second, they create clear escalation criteria — specific signal combinations that automatically trigger a named individual to investigate an account within 24–48 hours, regardless of where that account sits in the current pipeline view. These high-confidence signal clusters (typically combining first-party, third-party, and community signals simultaneously) are treated as priority events, not routine inputs.

The Programmatic Layer: Where Signals Meet Paid Media

One dimension of signal strategy that rarely gets adequate treatment in B2B marketing writing is the role of programmatic media in signal activation. This is an area where I’d argue the real frontier is, and it’s where platforms like ours at FunnelFuel are specifically focused.

The traditional model treats signals as an input to sales activation — the signal fires, sales gets a notification, a human engages. But signals can and should also drive media activation — specifically, programmatic advertising targeted at the accounts and personas that the signal intelligence has identified as being in the right buying stage.

When a cluster of accounts in your ICP is showing mid-funnel intent signals — category-level interest, competitive research, review site activity — that’s not just a trigger for sales outreach. It’s a brief, valuable window in which programmatic impressions delivered to the right personas at those accounts will be disproportionately effective. Buyers who are actively thinking about your solution category are more receptive to brand messaging, more likely to engage with content distribution, and more likely to mentally shortlist a vendor they’ve seen consistently across the channels they’re using for research.

The challenge is that most B2B programmatic buying has been entirely disconnected from intent signal data. Campaigns are built around static audience segments — job titles, industries, company sizes — that don’t adapt as signal intelligence evolves. The smarter approach is to treat intent signal tiers as dynamic audience layers: accounts showing early-stage signals receive top-of-funnel brand and thought leadership creative, accounts showing mid-stage signals receive solution-level content and case study creative, accounts in late-stage receive more direct product messaging and offer-led creative. This creates a programmatic layer that moves with the buyer’s journey rather than broadcasting indiscriminately across the entire ICP.

The infrastructure to do this properly — marrying intent signal data to curated programmatic deal IDs, with audience activation across quality B2B publisher environments — is still being built out across the ecosystem. But the directional imperative is clear: signal strategy and programmatic strategy need to become one thing, not two adjacent things that occasionally reference each other.

The Competitive Advantage Hidden in Plain Sight

Here’s the macro observation that underpins all of this.

The B2B technology landscape has made it increasingly easy for organisations to collect signals. The major intent data platforms — Bombora, G2, TechTarget, 6sense, Demandbase, Zoominfo’s Intent layer — are genuinely capable products, and their data quality has improved substantially over the past five years. As a result, signal collection has started to commoditise. Your competitors are also running Bombora. They’re also tracking Buyer Intent in G2. They’re also using 6sense to identify in-market accounts.

Signal collection is no longer a differentiator. Signal interpretation is.

The organisations that will build durable competitive advantage in the next phase of B2B marketing are not those with the most comprehensive data stack. They’re those that have developed proprietary interpretive capabilities — frameworks for reading signals in context, operating models that convert interpretation into action at speed, and integrated programmatic strategies that activate signal intelligence across both sales and media channels simultaneously.

This is, in many ways, what separates the B2B marketing function of the next decade from the one we’ve spent the last decade building. Moving from lead-centric to signal-centric is the easy conceptual shift. Building the actual operating infrastructure to make signal intelligence your competitive edge — that’s the work.

Most companies haven’t started yet.

Which means there’s still significant ground to take.

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While most programmatic platforms optimise for impressions, FunnelFuel optimises for pipeline. That means smarter audience curation, signal-aware media delivery, and the kind of account intelligence that turns programmatic spend from a cost centre into a revenue asset. Used by B2B marketing and demand generation teams who are done guessing. Learn more at funnelfuel.io

The story starts with Richard Foster, a 17-year GroupM veteran who claims he was pushed out after blowing the whistle on WPP’s media trading practices. To defend itself, WPP filed his internal report in court. That report — now public record — contains data that would normally never see daylight: which platforms clients are spending on, how much, and how the agency profits from the structure around it.

The number that froze ad land this week: 97.4% of GroupM’s proprietary inventory was not being used by its largest clients. So what is the point of this private inventory if it is not to let their clients benefit from it, you may ask?

Google — WPP’s biggest US client at $2.3 billion in annual billings — was using less than 1% of the inventory generated from its own spend. And GroupM was allegedly generating close to $1 billion annually from what it internally called “non-product related income.”

If you run paid media for a B2B brand and you work with a holdco agency, read this carefully. If you don’t work with a holdco, read it anyway — because the logic is creeping down-market fast.

~$1B GroupM’s alleged annual “non-product related income” — rebates, markups & inventory margin

97.4% Of GroupM’s proprietary inventory its largest clients weren’t using

81% Of marketers plan to increase principal media buying in 2026 (Forrester)

Lets decode what is happening here

What Is Principal Media? (The Simple Version)

Let’s start from the beginning, because the industry has done an excellent job of making this sound complicated when it’s actually quite straightforward.

When you hire a media agency, you assume they’re buying media on your behalf — acting as your agent, with your interests at heart. That’s the traditional model. The agency charges you a fee, places your ads, and passes on whatever deals they negotiated with publishers.

Principal media is a different model entirely. In a principal arrangement, the agency stops being your agent and becomes a seller.

The Principal Media Money Machine: How It Actually Works

Read more

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The industry keeps saying the funnel is broken.

It isn’t.

It’s just moved somewhere we can’t watch. It’s like a private group chat that you weren’t invited to, or like the lights have dimmed

In 2026, B2B buyers are still researching, debating, forming opinions and shortlisting vendors.

But they’re doing it in places attribution cannot follow:

Inside AI tools.

Inside internal copilots.

Inside industry Slack threads.

Inside peer conversations.

Inside podcasts.

Inside industry communities.

Forrester now reports that the majority of B2B buyers use generative AI during their purchase process. Indeed as verifiably long ago in the world of LLMs, 89% were using them in 2024 for B2B buying journeys.

Gartner predicts traditional search will materially decline by as much as 25% as AI becomes a primary discovery interface. Adobe is already seeing exponential growth in AI-driven referral behaviour, and many claim their best leads come from LLMs. The majority of my subscribers on theb2bstack.com now come from either social or LLM

The buying journey didn’t disappear.

It went private. It went dark. It went untrackable

And that changes the role of LinkedIn, programmatic, and omnichannel media entirely. This emergent new world needs addressing materially differently to the <2022 model

The Blunt Truth

LinkedIn is not where buying decisions happen. The last stats I saw showed that buyers spend a maximum of 4% of their working time on LinkedIn

It’s where buying confidence begins. It’s where influence surfaces. It fortifies credibility and trust. It can be very influential - but it’s not everything

That distinction matters.

Because most B2B strategies still treat LinkedIn as the performance engine — the place where demand is captured and leads are generated.

But LinkedIn’s real power sits earlier in the system.

It shapes:

• perceived credibility

• category understanding

• problem framing

• narrative alignment

• trust in vendors

LinkedIn is the professional identity layer.

It’s where people decide who sounds modern, who feels safe, who understands their world.

And critically, it’s where usage spikes around career moments.

Weekly, tens of millions of professionals actively search for roles. Promotions, layoffs, new mandates and internal restructures drive waves of engagement. And those career moments often correlate with the moments when buying committees form and budgets move.

LinkedIn handles 9,000 job applications a minute - let’s not lose sight of what the majority of people use it for

LinkedIn shows you who is professionally “awake.”

But it does not show you the full journey.

Because the buying committee doesn’t live there.

The Distributed Buying Committee

Modern B2B purchases involve 6 to 10 stakeholders at minimum, often more.

But their digital behaviour is fragmented.

Sales, marketing, founders and growth leaders tend to be LinkedIn-heavy. Those in market, brand building - personal and/or their company brand - and recruiters are power users

Technical evaluators, procurement, finance and operators are not.

They research differently.

They:

• consume deep technical content

• listen to niche podcasts

• ask AI to summarise vendors

• read documentation

• validate with peers

• observe quietly

They influence decisions without broadcasting intent.

This is why the dark funnel feels so real.

It’s not just hidden.

It’s distributed.

It’s also darn hard to identify let alone influence - there’s not some easy settings to hit within LinkedIn to get to those influential committee members

The Dark Funnel Effect

As buying moves into AI-assisted and private environments, three things happen simultaneously:

Attribution weakens.

Intent signals fragment and dwindle

Measurement confidence drops.

But something else rises.

Brand.

Because when you can’t clearly identify who is in market, being known before the shortlist forms becomes disproportionately valuable.

Being known increases the odds of making the shortlist. Or the odds of being explicitly promoted in ChatGPT or searched in Google or for a brand to ask a perceived expert for their opinion about you

The system shifts from:

capture → nurture → convert

to:

influence → presence → inference (Signals era)

This is the shift most B2B organisations are still catching up with.

LinkedIn Is the Influence Layer

Omnichannel Is the Coverage Layer

The mistake is treating LinkedIn as the entire journey.

It’s not.

It’s one surface in a multi-environment decision system.

LinkedIn shapes:

• professional perception

• credibility signals

• idea adoption

• social proof

But omnichannel programmatic enables something LinkedIn alone cannot.

Coverage.

Across environments where different committee members actually live:

Contextual web captures active research.

Podcasts build technical trust.

YouTube and learning platforms reach practitioners.

CTV builds executive memory and narrative understanding.

Industry media reaches finance and procurement audiences.

AI interfaces shape shortlists.

Each channel reaches a different role, mindset and stage of thinking.

Omnichannel is not about scale.

It is about distributed influence across the buying committee.

Why Influencers Are Surging

The rise of B2B influencers is not a trend.

It’s structural. It’s a reaction to the dark distributed funnel

Buying conversations now happen in spaces brands cannot enter:

Private Slack groups.

WhatsApp threads.

Internal copilots.

Peer referrals.

Closed communities.

People trust people inside those environments. They’re much more pure then many other surfaces, less prone to bias via commercial models

So experts become bridges into rooms brands cannot access directly. A brand presented via an influencer could penetrate these private theatres of deep influence

LinkedIn becomes the credibility staging ground.

Influencers become the trust accelerators.

Programmatic becomes the amplification layer.

This is not “social + ads.”

This is influence architecture.

The Harmonic Model

How LinkedIn and Omnichannel Work Together

The most effective organisations are not choosing between LinkedIn and programmatic.

They are synchronising them.

LinkedIn builds perception.

Omnichannel builds presence.

Signal capture builds intelligence.

The sequence matters.

Stage 1 — Shape perception early via always on brand activity

Narrative.

Problem definition.

Category leadership and sharing value

Authority signals.

You influence how buying committees describe their problem internally before vendors are even discussed.

Stage 2 — Build omnichannel presence

Programmatic ensures you are visible across the environments stakeholders actually inhabit.

Technical buyers encounter you contextually.

Executives encounter you in high-attention media.

Practitioners encounter you in learning environments.

Researchers encounter you during deep discovery.

You create familiarity.

Familiarity compounds.

Stage 3 — Capture signal exhaust

Even in the dark funnel, signal still exists.

Cross-channel exposure patterns.

Attention depth.

Account-level engagement.

Behavioural sequencing.

You won’t see the entire journey.

But you can infer movement.

And inference is enough to prioritise.

Stage 4 — Optimise to account progression

Not clicks.

Not impressions.

Progression.

More stakeholders engaging.

Deeper attention.

Cross-role interaction.

Earlier brand recall.

This is how you reconstruct a journey you cannot directly observe.

The Strategic Error Most Teams Still Make

They assume the goal is to generate leads.

It isn’t. Hear me out…

The goal is to influence an invisible buying committee before they identify themselves, which form the basis of your future pipeline.

Because by the time pipeline appears in your CRM, the real decision process has already happened. And if not, near as dammit it has

In private.

In conversation.

In AI.

In memory.

What marketing sees is the outcome, not the journey. Yet the job of marketing is to influence pipe

Zoom Out

The question for modern B2B leaders is no longer:

“How do we capture demand?”

It is:

“How do we influence distributed buying committees across environments we do not control?”

LinkedIn is where credibility forms.

Omnichannel is where influence spreads.

Programmatic is how presence scales.

Signal orchestration is how adaptation happens.

The funnel didn’t disappear.

It went dark.

And the organisations that win will not be the ones with the most leads.

They’ll be the ones that understand where decisions actually form — and build systems to influence those moments long before they become visible.

A personal observation

The most advanced B2B organisations are no longer asking which channel performs best.

They’re asking how perception, presence and signal work together as one operating system.

They’re less concerned with last-touch attribution and more focused on influence before identification.

They’re building architectures that allow them to:

show up early

stay present

learn continuously

adapt quickly

Not because it sounds modern.

Because it reflects how buying actually happens now.

And once you see the system that way, you stop thinking in campaigns.

You start thinking in environments.

And that changes everything.

We’re treating every channel like it works the same, and overlooking the very real differences between omnichannel environments and normalising them so they all fit on the same excel. Often we’re trying to line them up so that;

• Clickable equals measurable.

• Impression equals awareness.

• Awareness equals pipeline.

It doesn’t because it is too simplistic in a very complex B2B environment that we operate in today.

Some formats are 1:1 nudges on personal devices.
Others hit an executive on a 65-inch screen while their family is in the room.
Some generate clicks.
Some generate memory.
Some generate neither immediately but still move revenue and have their place in the modern B2B Marketing Mix.

If you want programmatic to actually move pipeline, you have to stop asking, “What performs best?”

And start asking:

At what stage, under what conditions, powered by what data, does this format accelerate progression?

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The False Equivalency Problem: Clickable vs. Clickless

Mobile display is clickable.

Desktop display is clickable.

Native is clickable.

Audio rarely is.

CTV usually isn’t.

Out of home is certainly not.

Yet many performance dashboards treat them like comparable units.

They’re not.

A mobile display unit is often a 1:1 moment. Personal device. Solo environment. Quick decision loop. It can nudge a director who’s already aware. It is a brilliant signal capture moment, because we can anchor it to a company and often a persona, meaning its exhaust fume is pure and very high value

CTV, on the other hand, is often a shared screen. Lean-back context. Longer dwell. Higher production value. No click. No form fill. No instant attribution.

But here’s the key:

Just because something isn’t clickable doesn’t mean it isn’t causal.

Research from the LinkedIn B2B Institute and the IPA has repeatedly shown that long-term brand investment increases future revenue efficiency. Brand and performance are not competing forces — they are compounding forces. In an age of ‘doing more’ with less budget, brand can often get cut, but in the new world order of private research in chatbots, understanding who is actually in market and with what intent has never been harder - always on brand helps overcome that. It derisks. It ensures messaging is in market when an account is, even if we do not know the account is active.

If your attribution model only rewards clicks, CTV will always look inefficient.

If your model rewards pipeline velocity and conversion lift downstream, it often tells a different story.

That’s the first mindset shift. Measurement has to start anchoring on account progression and marketings influence on that

Attention Is Not a Universal Metric

A 30-second CTV completion is not the same as a 30-second scroll pause.

An audio ad read by a trusted host is not the same as a banner impression in a low-viewability placement.

Companies like Lumen Research and Adelaide have demonstrated that attention-weighted impressions are more predictive of outcomes than raw impression counts.

Why?

Because cognitive state matters.

CTV often creates high visual attention in a distraction-minimised environment.

Audio creates narrative immersion and trust transfer.

Native creates active engagement, and seeds high value content into experiences which don’t feel like advertising

Mobile display creates rapid-response micro-decisions.

Each channel produces a different kind of attention — and attention quality changes memory encoding, brand salience, and future response rates. This needs encoding into custom engagement engines wrapped around accounts, which is a huge part of our scoring at FunnelFuel

When you understand this, you stop asking which channel “performs best.”

You start asking which channel produces the right cognitive state for this stage of the buyer journey.

The Reality of the Modern B2B Buying Committee

Gartner research shows that B2B buying groups often include 6–10 stakeholders, sometimes more. Anecdotally, I understand it can be a LOT more.

That means:

You are not influencing one person.

You are influencing a distributed decision network. This is the crux of what makes B2B different to B2C marketing.

Some of those stakeholders are reachable via LinkedIn and desktop display. But lets not make the mistake of assuming all “B2B decision makers” are active on LinkedIn, and lets remember that actually it over-indexes into sales and marketing roles, and job seekers. Technical leaders and other such roles are often going weeks or months between check-ins on there. It is not the committee locating golden goose that its often presented as being.

Some are reachable via podcasts during commute, which starts to unlock a different cohort of person beyond LinkedIn.

Some are reachable via CTV in their household. Far more people watch TV then browse LinkedIn, so again, this is expanding our pools of addressability

Some respond to in-depth native thought leadership.

When we talk about omnichannel programmatic today, we are not talking about “running banners everywhere.” and hitting the same users over and over again - this may be the perfect world, but different people - and B2B decision makers are people - will personally prefer different canvases - so truly impacting the committee needs to address all the major surfaces

When I say ‘surfaces’, we are talking about:

• Connected TV (CTV)

• Streaming audio and podcast inventory

• Native and sponsored editorial

• Desktop and mobile display

• Digital out-of-home (DOOH)

• In-game advertising

• Retail media networks

• Addressable linear TV

• Account-based CTV overlays

• Cross-device sequencing

• CRM-matched media activation

• Identity-graph-powered targeting

• AI-driven dynamic creative optimization

Many of these formats were not addressable five years ago.

Now they are.

But only if the data infrastructure exists.

The Single Signal Spine: Why Data Must Power Everything

Here’s where most “omnichannel” strategies collapse.

Scoring lives in Salesforce.

Targeting lives in a DSP.

Creative logic lives in a separate system.

Reporting lives in a BI dashboard.

None of it talks to each other in real time.

That’s not orchestration. That’s channel stacking.

True omnichannel requires what we call a single signal spine.

The same data powering:

• Pipeline stage

• Engagement scoring

• Buying committee penetration

• Velocity tracking

• Suppression logic

• Budget allocation

• Creative sequencing

• Reporting

Intent signals, engagement signals, CRM stage changes, opportunity creation, deal velocity, closed-won outcomes — all feeding back into activation.

Without that spine, CTV is just reach.

With it, CTV becomes a stage-triggered accelerant.

Without that spine, display is noise.

With it, display becomes precision reinforcement.

Custom Pipeline Progression Scoring: The Real Unlock

Most B2B programs still optimise toward MQLs, CTR, or other easier to grab metrics that don’t really let data tell the story

That’s outdated.

What actually matters is stage progression probability.

Instead of asking, “Did they click?” ask:

• Did engagement density increase at the account level?

• Did buying committee penetration expand?

• Did time-in-stage compress?

• Did opportunity velocity increase?

• Did downstream conversion rates improve after exposure?

Now imagine media triggers based on those signals.

When an account hits a high engagement threshold, CTV is introduced to increase salience across stakeholders.

When a contact consumes a mid-funnel asset, mobile and native retargeting increase frequency.

When an opportunity stalls in late stage, executive-focused CTV creative and proof-driven native content activate.

When procurement begins, awareness is suppressed and proof amplification increases.

Media becomes reactive to pipeline reality.

That’s not media planning.

That’s revenue engineering.

When Each Channel Actually Moves Pipeline

CTV

CTV moves pipeline when:

• You need broad buying committee reach.

• You’re selling enterprise with long cycles.

• You need authority and category ownership - the chance to upweight trust and awareness rapidly

• You want to improve downstream conversion efficiency.

It rarely drives immediate forms.

It improves future performance of everything else.

Google and Nielsen studies on cross-channel incremental lift have shown that TV and video exposures often increase search and digital response behaviour. CTV operates in a similar dynamic — especially when identity resolution connects household exposure back to account data.

Audio

Audio moves pipeline when:

• Trust and authority matter.

• You’re targeting niche executive audiences.

• You’re embedding thought leadership in contextual content.

Podcast host reads function as trust proxies.

They rarely generate large click volumes.

But they generate familiarity, credibility, and narrative alignment — which improves mid-funnel conversion.

For complex B2B offerings, that trust transfer can be decisive, but will be very difficult to attribute

Display (Mobile and Desktop)

Display moves pipeline when:

• It is sequenced by stage.

• It is CRM-synced.

• It is frequency-managed.

• It adapts creative by pipeline status.

Mobile display can function as a 1:1 nudge in a personal environment.

Desktop display can reinforce during workday research behaviour.

Without stage logic, display is background noise.

With scoring-driven activation, display accelerates progression.

Native

Native moves pipeline when:

• Education is required.

• Complexity is high.

• You need voluntary engagement.

Time spent, scroll depth, and content consumption are mid-funnel gold signals.

Native often bridges awareness and decision — especially when paired with gated assets and CRM feedback loops.

Newly Addressable Does Not Mean Automatically Effective

The industry is excited about addressable CTV, identity graphs, household-level targeting, and AI creative.

But addressability alone doesn’t create outcomes.

Without:

• Unified identity resolution

• Clean CRM integration

• Attention-aware valuation

• Pipeline-stage triggers

• Feedback loops

You simply have more surfaces to waste budget on.

The sophistication of the channel must match the sophistication of the data.

The Research Is Clear

• The LinkedIn B2B Institute shows that brand investment improves long-term effectiveness and reduces future acquisition costs.

• The IPA’s effectiveness studies show balanced brand and activation outperform short-term-only strategies.

• Lumen Research demonstrates that high-attention formats correlate more strongly with outcomes.

• McKinsey has documented that omnichannel commercial models outperform single-channel engagement strategies.

• Gartner confirms buying committees are large and complex, requiring multi-touch engagement.

The evidence supports orchestration.

What’s missing in most B2B programs is execution discipline.

The Big Reality

Omnichannel does not mean “run everything everywhere.”

It means:

• Stage-aware sequencing.

• Attention-weighted evaluation.

• Identity-resolved activation.

• Pipeline-driven triggers.

• Continuous data feedback.

This is not self-serve DSP media buying.

This is architecture.

It requires strategy, technology, analytics, and operational control.

It requires someone who understands both media mechanics and pipeline economics.

The Question B2B Teams Should Be Asking

Not:

“Should we test CTV?”

But:

“Is our data mature enough to activate CTV at the right moment in pipeline progression?”

Not:

“Is audio measurable?”

But:

“Are we measuring influence correctly?”

Not:

“Which channel performs best?”

But:

“How do we orchestrate them so each enters the system at the right time?”

When that system exists, channels stop competing.

They compound.

And when they compound, pipeline doesn’t just grow.

It accelerates.

If you’re an agency leader or in-house B2B team building toward this level of orchestration, the real work isn’t selecting channels.

It’s designing the signal spine that powers them.

That’s where advanced programmatic stops being media buying — and starts becoming revenue infrastructure.

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I have been doing this for a long time now, and we have been building these exact solutions at Funnelfuel.io so that you don’t have to. Want to learn more? simply reply to this email or connect with me on LinkedIn below

This wasn’t a “feature launch” week but it was a pressure week.

• The Trade Desk stock cratered.

• WPP’s principal trading model exploded into public litigation.

• The Google ad tech remedy ruling looms.

• Search budgets are visibly shifting and getting disembowelled by AI, and they money is moving into social + omnichannel programmatic

• DOOH infrastructure just quietly levelled up.

Individually? Headlines. Collectively? Structural strain on the open ecosystem.

Let’s break it down. The stories that caught my attention this week

STORY 1: The Trade Desk just fell 16% — and it’s not about one quarter

The Trade Desk reported Q4 2025 revenue of $847m, beating consensus. So far so good, as their guidance was c. $842m

But guidance for Q1 2026 — “at least $678m” — implied growth slowing to roughly 10% YoY. The DSP layer is under massive pressure - supply containerisation, curation and new agentic plays are moving money in new ways. Greater AI adoption from savvy teams lets them own and manage segments and usage into TradeDesk much more themselves then in the past too, lowering barriers to doing more yourself and consuming less TradeDesk resources - and thus spending less money with the biggest indy DSP

The stock dropped more than 16%. Its reflective of a hype cycle over recent years but also a very real headwind

The Trade Desk earnings / stock reaction

FinancialContent (TTD earnings coverage)

AdExchanger (earnings / DSP analysis coverage)

On the earnings call, CEO Jeff Green faced pointed questions about:

• Competitive pressure from Amazon’s DSP — which is in market with INCREDIBLY aggressive terms, with bid reductions as low as 0% (yes really)

• Whether streaming-driven tailwinds are fading

• Structural pressure on open-internet programmatic

Why this matters for B2B

TTD is the DSP of choice for open-web B2B. Its most flexible, customisable and lacks the black box of Google’s DV360

If growth is decelerating sharply, it suggests:

• Open web budgets are tightening

• Retail media and walled gardens are gaining share

• AI-disrupted search is fragmenting performance dollars

The early streaming-era “easy growth” phase may be ending.

That’s not cyclical.

That’s maturity.

STORY 2: The WPP whistleblower case just blew open principal trading

This is the most explosive story for any B2B advertiser running budgets through a holdco.

WPP is facing a $100m whistleblower lawsuit alleging that the group generated nearly $1 billion annually from undisclosed trading income.

Sources:
Best Media Info – Whistleblower claims

Digiday – GroupM proprietary inventory usage
Ad Age – Spending disclosures

The most staggering stat:

97.4% of GroupM’s proprietary inventory was not used by its own largest clients.
Google — its biggest U.S. client at $2.3bn in billings — used less than 1%.

The allegation:

Budgets aggregated → volume thresholds triggered → rebates received → inventory classified as proprietary → resold at margin → income recorded as “non-product related.”

Meanwhile, WPP Media boss Brian Lesser stated that principal media “is a growing part of our business.”

That comment now lands differently. And awkwardly

For B2B CMOs

If your programmatic runs through a holdco:

• Do you know if principal trading is involved?

• Do you see gross vs net cost?

• Do you see rebates?

• Do you see supply-path transparency?

This case forces contract audits.

Not in theory.

In practice.

STORY 3: Google’s ad tech remedy ruling is imminent

Judge Brinkema’s ruling on the DOJ’s case against Google’s ad tech stack is overdue.

The DOJ is pushing for divestiture of:

• AdX (exchange)

• Potentially DFP (ad server)

European regulators are holding parallel cases pending this decision.

Coverage:
AdExchanger – Google ad tech remedy

If AdX were separated, it would be the most significant structural change to programmatic since RTB launched. This inventory is the only really genuinely differentiated in the SSP space because it contains publishers Adsense placements, and not just typical banner spots. Publishers who have never heard of programmatic run adsense, and this brings unique and performant placements

For B2B buyers:

Your supply infrastructure could materially change.

This isn’t a UX update.

It’s plumbing.

STORY 4: AI pressure is shifting search budgets

Chris Modzelewski (Intentsify) outlined clear shifts emerging in 2026:

• Search budgets moving to paid social, CTV, audio, DOOH - not banner display, but storytelling mediums and brand impact placements. This likely reflects the fact that brands now realise that always on brand is critical because high intent search is moving into private LLMs

• Buying-group intent replacing single-lead logic

• AI tools limited to exploration, not orchestration

Coverage:
PPC Land

The reality:

AI interfaces are intercepting research intent.

If fewer B2B buyers are clicking blue links, paid search loses signal density.

Those budgets don’t disappear.

They redistribute.

CTV and DOOH suddenly look less “brand” and more “presence.”

STORY 5: Programmatic OOH just got structurally smarter

The Out of Home Advertising Association of America introduced an updated OpenOOH taxonomy.

Instead of “transit,” screens can now be tagged as:

transit → airports → baggage claim

Coverage:
AdExchanger – OpenOOH taxonomy update

This is small on the surface.

But for B2B targeting senior decision-makers in airports, hotels, business districts?

Granularity equals precision.

Infrastructure upgrades compound slowly.

Then all at once.

The through-line

The open ecosystem is under simultaneous pressure:

• DSP growth decelerating - likely because savvy players are doing much more agentically and in the sell side
• Agency opacity exposed
• Antitrust uncertainty
• Search disruption via AI
• Channel redistribution
• Infrastructure reclassification

That’s not noise.

That’s systemic strain.

🧠 The contrarian take

The industry keeps saying:

“The open web is resilient.”

Maybe.

But resilience isn’t the same as dominance.

If:

• DSPs slow

• Agencies are litigated

• Google’s stack fractures

• Search loses intent gravity

• Traffic lives in models not web pages - even if the web grew 19% last year…

Then B2B brands with long sales cycles and high CPMs are the most exposed to waste.

The old open-web growth narrative is weakening.

Accountability is the new growth lever.

🧰 Monday Morning Checklist (For B2B Teams)

1️⃣ Audit your agency agreements
Ask explicitly about principal trading, rebates, and proprietary inventory.

2️⃣ Model budget reallocation scenarios
If search underperforms due to AI shift, where does incremental spend go?

3️⃣ Stress-test your DSP exposure
What happens if AdX is divested? If supply pipes fragment?

4️⃣ Increase first-party signal capture
You need owned analytics and HVA scoring more than ever.

5️⃣ Revisit DOOH in business environments
Taxonomy precision just improved. That matters.

This wasn’t a hype week.

It was a stress-test week.

The open ecosystem didn’t collapse.

But it blinked.

We did not.

We replaced observable identity with modelled confidence and hoped revenue would not notice. A game which was always going to need precipitous levels of skill if we were to pull it off in B2B.

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Programmatic has always been audience buying. Technologically advanced, scaled, maximised audience pools, with impression by impression bidding to deliver the opportunity to be incredibly precise. A buyers paradise when executed to its maximum capability. A chance to cherry pick exactly which audiences we want, when we want them, and to chase the promised land where there is no wastage. For all its failings, and programmatic has its share, the opportunities it gives are enormous in a world where signal exhaust fumes are weakening everywhere else

The world of identity has fragmented and changed remarkably in a short space of time; going from the pretext of certainty with cookies, to universal ID replacements and systems built on proxies and models.

This is a reality of privacy today. Its journey of travel is clear - it will only get tougher. This is the world in which you interact, so maximising the opportunities it brings is key if you want to get ahead. I believe brands should focus on owning their identity more, and playing a keener role inensuring the shaping, ownership and structure of audiences is truly aligned to what they know they need vs renting and hoping.

Yet many of the B2B teams I observe are not maximising identity and architecting their own audiences - and if they are, they are doing it half baked.

The opportunity is to own the identity spine of your audiences. To own this critical layer, and then dictate it into media buying, owning their own destiny rather then renting and hoping for the best.

So how the heck do we do that?

Most cookieless solutions today are probabilistic stitching engines, which when deployed into the buy side layers, are wrapped in polished dashboards. They report match rates north of 85 percent. They surface intent surges at the account tier. They optimise bids dynamically. Its rented infrastructure based on you putting one simple input - such as a TAL - into adtech layers. This is arguably lazy and uninvested, considering the shaping of the audiences is critical to the success of any campaign.

What most outsourced platforms cannot do, in most cases, is prove that the right members of a real buying committee were reachable at the moments that moved pipeline. They are not incentivised to do so. They want scale to earn money against small cuts of big budgets. They want to trade inputs for big scale. This helps their economics. This is why a realignment between expert B2B adtech and outcomes models

That gap is widening, behind marketing teams objectives and the realities of what lives behind the curtains, driven by commercial alignment. Aligning closer on outcomes buying would need the rest of what I am writing about today. However geting closer alignment on audience inputs and identity, is an easier win to bring brand and adtech closer

I often say we are not in a post-cookie era. We are in a post-verifiability era, where confidence in single sourced data vendors is increasingly low and increasingly questioned, and where the biggest and best players are stitching signals to improve their probabilities of success. This is how you can do that, own your audiences closer rather then renting infrastructure, and aligning media buying with real business outcomes.

Reframe: this is not a targeting problem but B2B needs much more accurate audiences then B2C

The dominant framing is technical.

Cookies expired.
We adopted new identifiers.
Problem contained. Move on.

That narrative ignores what actually changed.

Under third-party cookies, flawed as they were, you could observe persistence. A user returned. A device remained stable. Exposure paths were imperfect but inspectable.

Post-cookie, most identity is inferred, and relies on daisy chained identifiers

Graphs extrapolate from hashed emails, IP ranges, device clusters, publisher logins and cooperative data pools. Confidence scores are generated through black-box logic. Resolution is probabilistic by design. They take these tight inputs, then marry the heck out of the relationships, to build unabridged scale at all costs.

In consumer advertising, probabilistic lift can be sufficient, because the cohort that we are building is often created out of simple inputs - men, under 40, middle or higher income etc. Broad categories, with bigger data and bigger margins for error… because half the time the inferred customer isn’t the buyer anyway. It can muddle along and work.

Imagine if this trick worked in B2B - if only it was this easy

In my years of working in B2C and programmatic, there was a dirty secret. One targeting trick which could improve your odds of last touch attribution sales markedly, whilst reducing the bid price. Sounds like magic? the trick? target the late night / early hours of the morning on ‘social nights’ like Fridays and Saturdays, and catch an inebriated impulse buy. The trick was helped economically by the programmatic standard - which was to run extended office hours targeting and then shut down targeting. Therefore this trick worked particularly well, because the bid price needed went down with less competition - the common thought was that the fraud bots came out to play at night. The CPAs could be outrageously good by programmatic standards

Such tricks don’t work here in B2B. In enterprise B2B, where one deal can justify six months of spend, probabilistic ambiguity becomes a material risk. Enterprise B2B knows its customer, and missing the mark when aiming for a high earner, and hitting a mid earner can work in B2C - but being so far wide of your mark here equates to total failure. And there is no late night impulse buying to stat pad either.

Because enterprise revenue is not driven by individuals.

It is driven by coordinated committees of stakeholders who play individual roles in procurement and whom coordinate to form a consensus.

Structural shift: what actually changed beneath the surface - its much deeper then cookies alone

Three deeper forces are reshaping B2B programmatic.

1. Signal half-life has collapsed - across cookies and other signals

Browser policies shortened identifier persistence. In simpler terms - browsers made it much harder for a cookie to stick around for very long, meaning that their shelf life declined over their later years

Corporate VPN usage fragmented IP-level continuity. Mobile and desktop journeys rarely unify deterministically in enterprise environments.

Intent spikes now decay faster than most media teams model.

Very few B2B organisations quantify signal half-life empirically. They ingest third-party “in-market” flags as if they were stable states. They are not.

In enterprise SaaS, I routinely see initial research activity precede opportunity creation by 30 to 120 days. If your intent vendor refreshes weekly without modelling decay curves, you are often bidding against historical curiosity, not active deal velocity. Worse still., if they just closed a bit of business, they could be as far ‘out of the market’ as they ever could be, totally disinterested in CRM solutions because they have a fresh 12 month contract with SalesForce, sat there with wet ink still.

Cutting-edge teams model signal decay explicitly. Its critical to success in the signals era

They map behavioural events to opportunity creation timestamps. They calculate probability curves over time. They bid aggressively inside the statistically validated window and throttle outside it.

This is not optimisation.

It is actuarial discipline applied to media.

2. Buying committees widened while identity resolution narrowed

Enterprise deals now involve 6 to 12 stakeholders as standard. In regulated sectors, 15 is common.

Most identity graphs still resolve around primary contacts or dominant device clusters.

The question is not whether the account is in the graph.

The question is committee coverage ratio.

You can model this mathematically.

Start with historical closed-won data.

For each account, extract:

• Number of distinct stakeholders involved

• Functional roles represented

• Time between first engagement and close

Now compare that against media-reachable identities inside your graph during the same period.

If your reachable set consistently covers only 40 percent of historical stakeholder roles, your media strategy is structurally underpowered.

Few organisations run this analysis.

Fewer are comfortable with what it reveals.

Advanced teams treat buying group coverage as a quantifiable metric. They assign role-weighted reach targets. They design creative sequencing by functional persona. They optimise for stakeholder saturation, not impression volume.

This changes budget allocation dramatically.

3. Platforms internalised identity power - but vendors should be owning their own identity contracts with the likes of LiveRamp

Walled gardens strengthened deterministic moats.

Open web programmatic became more dependent on third-party graphs and publisher-declared signals, many of which are horribly out of date

Identity logic is now proprietary infrastructure. Companies like FunnelFuel win on their ability to match B2B audiences incredibly accurately, it becomes a core competency and differentiator

Many other vendors claim high accuracy but rarely allow independent reconciliation against CRM-level truth without restrictive NDAs and sampling constraints. In other words, they will parrot out a big match rate but they won’t go to town to prove it

The brutal reality is commercial incentives favour inflated coverage narratives - which is a flaw in the market driven from proxies for success. The vendor with the biggest match rate often wins the campaign booking, even when it is hugely unqualified and their perceived advantage is their bigger match rate number. Even when the initial TAL was horribly unaddressable, and a near perfect match rate is likely neigh on impossible. The procurement energy, whether from an internal vendor marketing team or from their agency of record can be (not always but definitely sometimes or even often) be focussed in the wrong direction - onto match rates not real proxies for success. This is the big fundamental flaw in relying on external partners - a brand vendor taking more ownership and focus in on better criteria for judgement, rather then just looking for the biggest match rate number.

This shifts risk to the buyer or vendor, because their agency conduits may be asking the wrong questions, and it means the buyer is investing potentially 8 figures a year through a vendor that was selected on the wrong input/s. This directly correlates to whether paid media ‘works for them’ and can make or break a CMO’s career.

The more opaque the match logic, the harder it is to negotiate on quality rather than scale, and the less likely that media dollars invested will map to real success

Sophisticated operators respond differently. They take ownership of their identity by working directly with identity vendors to shape their audiences upstream of activation. They structure these identity contracts with validation clauses. They focus on quality

Payment tiers tied to verified account-level match rates against closed-won cohorts.
Quarterly reconciliation windows.
Right-to-audit provisions on methodology changes.
Clawbacks where statistically significant degradation is proven.

Most B2B teams accept CPM uplifts for cookieless inventory without renegotiating data risk.

That is commercially naive.

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Measuring What Matters + Designing High Value Actions (HVA) models that withstand CFO scrutiny

If you want control back, you need internal proof of what actually correlates to revenue. This lays a foundation that can lead towards more outcomes based buying models, or at least, greater aligmnet on what wining really looks like.

Its all easier said then done when the one part of the digitalisation of B2B yet to happen at scale is the transaction, meaning the ultimate B2C source of truth - did I get sales - is not on the table to track, score and attribute towards. Clicks and other poor but easily tracked proxies got measured instead, and we still see CTR goals in 2026. Poor proxies are aplenty and can commonly include MQL velocity, CTR, last touch or modelled conversions.

Instead, the real starting point for determining B2B success, and account progression should be High-value account actions. These are micro conversions with proven value; steps that can be modelled from actual transactions, and analysed at statistical scale to ensure they are meaningful

Start with revenue backwards. CRM holds the book on which accounts closed.

Analyse two years of closed-won opportunities.

Identify behavioural sequences that statistically increased, linked to those accounts. (NB; this needs B2B account level analytics like FunnelFuel Journey - this account level lens is not in B2C solutions like GA4). In our CRM we are looking for signals like

• Pipeline creation probability

• Deal velocity

• Average contract value

Then we are mapping the account behvaiours back in our analytics, to find meaningful digital interactions. Examples often include:

• Repeated return visits across multiple stakeholders. More visits from more users in tighter clusters

• Deep technical documentation engagement, which indicates integration research

• Security or compliance page consumption.

• Trial environment provisioning events.

• Booking demos, configurators, and pricing interactions

Build logistic or survival models that quantify these relationships at the account tier.

Document confidence intervals.

Model false positives.

When a CFO sees that accounts exhibiting three specific behavioural markers convert at 4.3x baseline probability with a 95 percent confidence band, media becomes an investment instrument, not a marketing expense.

This is the spine.

Everything else should feed it.

Structuring identity vendor contracts in post-cookie reality

Most identity deals are volume-based.

Price per matched ID.
Price per resolved account.
Platform surcharge for cookieless coverage.

Very few are quality-adjusted.

A more defensible structure includes:

• Baseline reconciliation against historical closed-won cohorts

• Ongoing randomised control validation at account tier

• Degradation triggers that adjust pricing if match quality drops

• Explicit disclosure requirements when graph methodology materially changes

Identity vendors will resist.

That resistance reveals margin assumptions.

If identity is strategic infrastructure, it should be governed like infrastructure, not like display inventory.

LLM discovery further destabilises third-party identity

The next destabiliser is already visible.

LLM-driven discovery surfaces abstract user journeys. There is no tracking in them, and the nature of very long prompts and the extreme personalisation is a world much more removed from Google searches 1-3 keyword blunt inputs then many realise

Enterprise buyers increasingly start research inside AI interfaces that summarise vendor landscapes without traditional clickstreams.

Referral data is obfuscated, if a referal even happens (how many people really check the sources?)
User agents are masked.
Publisher signals weaken.

This compresses observable top-of-funnel signals further.

If your model depends on third-party behavioural breadcrumbs across the open web, LLM interfaces will erode them.

First-party instrumentation becomes more critical.

Direct traffic spikes.
Branded search lift.
Content depth anomalies. Account level views, even if imperfect - as they always will be

These become proxy indicators of off-platform discovery.

You cannot outsource interpretation of these patterns to a graph vendor who cannot see them.

The economics of renting versus owning over three years

Renting identity looks cheaper in quarter one.

No data engineering headcount.
No modelling investment.
No political friction across teams.

But over a three-year horizon, the economics invert.

Consider:

Annual identity and intent vendor spend.
CPM premiums for cookieless inventory.
Wasted impressions due to stale or incomplete committee coverage.
Margin compression from bidding on decayed signals.

Now compare that with:

One data engineer.
One revenue analyst.
Warehouse compute.
Modelling tooling.

Owning the signal spine creates compounding advantages.

Better forecasting accuracy.
More efficient bid multipliers.
Negotiation leverage with vendors.
Stronger board-level credibility.

Rental spend scales linearly with impressions.

Owned signal architecture compounds with every deal closed.

The delta becomes material by year two.

Operator implications

Serious B2B teams should:

1. Audit buying committee coverage mathematically, not anecdotally.

2. Model signal half-life empirically and align bids to probability curves.

3. Build account-level HVA models validated against historical revenue.

4. Renegotiate identity contracts around quality, not volume.

5. Instrument first-party data aggressively in anticipation of LLM-driven signal abstraction.

Media platforms should consume validated models.

They should not define them.

Zoom out:

Identity in B2B is becoming a capital allocation question.

Teams renting opaque graphs will experience gradual performance erosion masked by dashboard optimism.

Teams building proprietary signal architecture will convert marketing from spend to statistical advantage.

Programmatic is not dying.

Blind trust in third-party identity is.

The advantage over the next cycle will belong to those who own their spine.