What you’ll learn in this article:

• Why the buying committee, not the account, is the real unit programmatic needs to be built around, and how large that committee has actually grown since the pandemic changed the game

• What the open web gives you at the individual URL level that LinkedIn structurally cannot

• Why the data coming back out of a programmatic buy, not just the placement itself, is the more valuable half of the trade - and the value most buyers miss

• Where programmatic beats walled gardens on cost and on reaching the stakeholders LinkedIn under-indexes

• Why this capability gap has persisted for a decade, and what actually closes it

The wrong belief

Most B2B marketers have quietly written programmatic off, and the reasoning isn’t unreasonable: the entire discipline, cookie and device graphs, individual consumer segments, one-to-many optimisation, session-based conversion windows, was engineered for selling trainers and holidays to one person at a time.

Judged against a B2B sale involving six, ten, twenty stakeholders, that plumbing looks like the wrong tool, so programmatic gets treated as the channel you fall back to once the LinkedIn budget runs out. The cheap remnant inventory that nobody wants to prioritise.

Here’s the flip. Programmatic wasn’t built for B2B, and that part of the belief is correct.

Where it breaks down is the next step, the assumption that this makes the channel permanently unfit for the job. It doesn’t. It means the auction infrastructure, the real-time bidding, the exchange connectivity, the contextual crawling, is powerful technology aimed at the wrong unit by default. Point that same infrastructure at an account graph and an identity spine built to resolve real companies and real committee roles, and the channel that was supposedly wrong for B2B becomes the only one built to reach a full buying committee across the open internet at scale.

I often see the gap as data hierarchy. Programmatic thrived because it focussed on targeting end individual users (really end devices measured by cookie footprint but I’m trying to avoid being pedantic for a moment). That was always awesome for selling fast moving consumer goods, but missed the hierarchy levels of account and buying groups. The moment you can transpose those into the programmatic auction, including the other bits mentioned above - as well as optimisation, pacing, reporting, and targeting - then you have changed the game. You have weaponised some of the best optimisation AI in the game to work for the buying group within an account currency, which is a game changing upgrade

The buying committee is bigger than most media plans assume

Gartner’s own buyer research puts the typical enterprise buying committee somewhere between six and ten stakeholders today, up from 5.4 a decade ago, with larger deals routinely running past ten and some surveys putting the ceiling above twenty. Forrester’s figures land in a similar place: somewhere between two-thirds and three-quarters of the buying journey now happens before a prospect ever speaks to a salesperson.

That’s not a subtle shift. It means the individuals a vendor needs to reach, and the window in which they can be reached, have both expanded and moved earlier, largely out of view of the channels built around a form fill or a sales call (think dark funnel). It also means “the account” was never the right unit to plan media against. The committee is, and a committee of six to twenty people spread across procurement, security, legal, technical evaluation and the executive sponsor is not a group any single channel reaches on its own, least of all a channel built to sell one person a pair of trainers.

These 6-20 person clusters, who need targeting in unison not isolation, is the precise gap I have spent millions of dollars engineering to resolve.

This is where an account graph, tying together tens of millions of businesses, their firmographics, their technology stack, their inferred size and revenue, against an identity spine that makes the individual committee member targetable, stops being a nice-to-have and becomes the actual precondition for doing this properly. Without it, “reach the buying committee” is an empty slogan. With it, it’s a targeting spec, and it’s the specific piece of infrastructure the B2C version of programmatic was never built to include.

What the open web gives you that LinkedIn structurally can’t

Traditional programmatic formats, display, video, native, delivered against premium content sites and apps, come with one advantage that gets MASSIVELY underrated: the full URL of the page carrying the ad, both as a targetable signal going in and a reportable one coming out. That lets a business crawl and analyse the page itself, its text, its imagery, its video and audio, and understand semantically what it’s actually about, not just which segment the visitor supposedly belongs to.

Its resultantly easy to fall into a fallacy of believing the entirety of the value is in targeting what I like to call apex-fit pages.

But “rarer” is not “rare.” and “rare” does not mean “not at all”. The open web still carries an enormous footprint of contextually relevant page views, and while every published estimate of daily programmatic bid volume disagrees with the next by an order of magnitude depending on whose infrastructure is being counted, even the conservative end of those estimates dwarfs total impression volume across every walled garden combined.

That’s the opening.

The real prize is the data coming back out

The placement is only half the trade, and the only half that most buyers seem to value.

The depth of data returned from a programmatic buy, the raw event logs and bid-level detail sitting behind platforms like The Trade Desk, isn’t matched by anything a walled garden reports back. That observable signal, tied to the right account graph, is what turns a media buy into a data asset: knowledge of which named accounts, and ideally which individuals within them, engaged with which contextually-aligned placements.

That’s an underused opportunity in itself, because it lets a B2B marketer build a proprietary read on intent rather than depending entirely on third-party intent providers whose methodology is, to varying degrees, a black box. Feed that signal into your own model, and you can push it back out into every other channel you run: sync it against YouTube buys, sync it against social, use it to prioritise content syndication, and let your own observed behaviour, not a vendor’s inferred score, set the plan. This is a huge and massively under-appreciated component of programmatic advertising

The same principle extends to owned-property signal: an individual mobile ID, IP address or cookie behind a visit to your own site or a partner’s, tied back to a named account and ideally a named buying committee member, recognisable well before that person raises a hand. Put together, that’s the 360-degree loop the B2C version of this channel was never designed to close, and that most B2B media plans still don’t.

Where programmatic beats the walled gardens, and where it doesn’t

LinkedIn earns its premium. It reaches named accounts and named individuals with genuine precision, and its reporting is good. But it’s expensive, and its 2026 benchmarks show it: CPMs for tech and IT audiences commonly run $30 to $75, sometimes higher for senior or C-suite targeting, up meaningfully on 2023 levels.

Open-web programmatic display trades at a fraction of that, and the data which transforms a B2C generalist impression into a B2B one is what changes the cost, not Linkedin’s arbitrary pricing structure.

The cost is one thing, but much more meaningfully (in a negative sense) is the fact that LinkedIn structurally under-indexes on exactly the roles that don’t spend their working day in the feed: technical evaluators, legal, procurement, security. These are people who are absolutely central to whether a deal closes, and who are considerably easier to find in a technically-aligned podcast or a premium trade publication than in a LinkedIn auction built around front-of-house job titles.

Why this hasn’t happened already

If the case is this strong, the obvious question is why B2B programmatic spend has stayed this underweight for a decade. Surely not every major vendor has missed the memo on this?

The honest answer is infrastructure, not appetite. Purpose-built B2B programmatic capability, anchored to account-level data pipelines that can target and then report and attribute all the way through to a website visit, simply hasn’t existed as a category until recently. Most of the ecosystem is still running the B2C version underneath a B2B media plan and calling it done. This is the specific gap we built FunnelFuel to close, and it’s taken real investment, not a repackaged consumer DSP with a B2B label stuck on it, to make the targeting-to-attribution loop actually close.

That gap, more than any lack of belief in the channel, is why programmatic has spent the better part of a decade as B2B marketing’s most underinvested line item. It also means the opportunity is still there for anyone willing to bring in the expertise, whether that’s built in-house over time or borrowed while the programme stands up.

It would be a mistake to assume big vendors, especially big B2B tech vendors, are not materially running B2B programmatic - there’s tens of millions getting spent annually by individual big vendors that I know and we work with. The structural miss is probably outside of this land of the giants but which represents a huge chunk of the B2B industry

The position

Programmatic was built for B2C. On its own, unmodified, it’s exactly the channel most marketers still think it is: cheap filler, a segment-of-one tool bolted onto a committee-level sale, easy to deprioritise next budget cycle. Rebuilt on an account graph and an identity spine that can resolve a real buying committee, the same auction infrastructure gives you committee-level targeting an account list alone doesn’t, contextual reach LinkedIn structurally can’t replicate, a deeper and more attributable data return than any walled garden hands back, and a materially better cost basis for reaching the parts of the committee that never open LinkedIn.

That gap between the two versions of the same channel is, in my view, one of the more expensive things sitting inside a lot of 2026 media plans heading into 2027. So here’s the question I’d put to anyone still running programmatic as an afterthought: if two-thirds of your buying committee’s research is happening before your sales team ever hears about it, and the channel was never built to reach that committee out of the box, what’s actually in your plan to close that gap, and what’s it costing you that it isn’t?

It feels like precision because it’s specific. It isn’t precision. It’s arbitrary and often self constraining, and in a meaningful share of the accounts on any real target list, the title you’re searching for doesn’t exist on that org chart at all or doesn’t map into the accounts organisational structure. This approach totally disregards how organisations all set up differently; with different decision making processes and procedures, and assumes they are all the same.

Average tenure in a US role is now around 18 months, and B2B email addresses decay at roughly 25 to 30% a year. Even where the title does exist, the person holding it by the time your campaign goes live is frequently not the person your list was built against.

The key point is, we can not guess how each of our target accounts runs its procurement, who is involved at what stage and why, where job titles can diverge from common expected norms around their roles and a host of other factors. We can though so observable signal from the wider internet and infer buying groups out of behaviours to make a more scientific approach that is bespoke to each vendor.

There’s a radically better way which lets you find each companies buying groups and then target them, bringing the value of a more 1:1 ABM with the scale of 1:many

What you’ll learn in this issue:

• Why targeting fixed titles like CIO or CTO breaks the moment an account doesn’t have one

• What I’m calling the shadow buying group, and why it matters as much as the formal committee

• How device-level signal, first and third party, infers who’s actually in a buying group instead of assuming it from a title

• Why some accounts decide horizontally and others are run by one dominant voice, and how signal tells you which is which

• Why B2B’s economics, high transaction value against a low marginal cost per person, make a wider net the rational move rather than the wasteful one

• How signal-orchestrated buying groups become the account graph’s next frontier, beyond company and persona resolution

• What this actually unlocks for a paid ABM programme built to drive pipeline influence, not just impressions

Targeting job titles assumes every company is organised the same way

Job-title targeting has one structural flaw underneath it: it assumes an org chart that’s the same shape everywhere. It isn’t. Some businesses are horizontal and democratic, where a purchase like a new CRM gets input from six or eight people across ops, sales enablement and finance, none of them especially senior. Others are autocratic and founder-led, where a founder CEO carries a disproportionate share of the final call and the rest of the room is there to execute, not decide. Neither shape shows up reliably in a CRM field, and a title-first target list has no way of telling the two apart before it’s already spent budget on the wrong three people.

That’s the real cost of premeditated targeting: deciding who matters before you’ve looked at the account, rather than letting the account tell you.

This creates great wasted impressions (targeting irrelevant people) and even greater opportunity cost - the latter is significantly worse in a world of digital media where the unit cost of reaching a handful more people is low but the cost of missing them out could cost you a 7/8/9 figure deal.

What I’m calling the shadow buying group

Call it the shadow buying group: the sphere of influence operating and orbiting around the formal buying committee, made up of people who never show up on an org chart or a CRM record but who shape the decision anyway.

Every procurement process has a committee you could name if pushed, and a wider ring around it you couldn’t. The person the CFO quietly asks before signing off. The engineer whose opinion the VP always checks before committing to a vendor. None of that shows up in a lookup by title. All of it shows up in signal, if you’re reading the right layer of it.

Forester also indicates that the growing complexity behind the ever growing buying committee has extended the sphere to an average of 13 internal and 9 external consultant aids. The buying group can now extend beyond the FTE’s in a business, and the only hope we have of capturing these influential extras is to be open to letting signals shape our targeting even when they can’t reliably be connected to an account.

How signal actually finds the buying group

This is the part that moves the whole discipline forward from where the original account graph argument left it. First-party signal, someone completing a high-value action on a brand’s own site, researching pricing, downloading a spec sheet, returning three times in a fortnight, self-declares that device as plausibly inside the buying group or its shadow. Third-party signal, a device reading procurement-adjacent content across the open web, or watching a specific run of YouTube videos on the category, adds topic-level and individual-level intent from outside the brand’s own walls. Stack the two together at the account level and you’re no longer looking up a title. You’re watching a buying group and its sphere of influence assemble themselves in the data, procurement process by procurement process, for something specific like a Salesforce CRM renewal rather than for the account in the abstract.

That’s the shift worth sitting with: not “find this list of accounts, then find the CIO inside each one,” but “find the buying group inside each account, built from what its members are actually reading, watching and doing, for this specific procurement process.” Persona and job title become an output of that signal, worked backward, rather than the filter applied before any signal gets read at all.

Why B2B’s economics make the wider net the rational move

In B2C, casting a wide net is expensive relative to what you get back: most of the audience will never buy, so precision earns its keep by trimming waste. I remember back around 2018 when the programmatic world was obsessed with reducing waste, and movements like viewability were driven by this agenda.

B2B inverts that maths. The value of a single won account can run into hundreds of thousands of pounds, while the marginal cost of including one more plausible buying-group member in the media plan is small change by comparison.

That asymmetry is the argument for casting the net wide enough to capture the full committee and its shadow group, rather than narrowing to two or three titles and hoping they’re still the right ones. Missing a genuine influencer costs far more than reaching one extra person who turns out not to matter.

The account graph’s next frontier

The original version of this argument, back in July 2025, was built around two upgrades: account over person, and company over domain. Both hold up. This is the third: buying group over persona. Not just resolving which company is active and in-market, but which specific group of individuals inside it is running a specific procurement process, reconstructed from signal rather than assumed from a title list, refreshed continuously rather than looked up once and left to decay for 18 months.

That’s what a paid account-based media programme actually needs to drive pipeline influence rather than reach: not a bigger list of the same three titles, but a live, per-account, per-procurement-process reconstruction of who’s actually deciding.

The manifesto, shortened

Job titles are a lagging indicator of who decides, in an org chart that decays within 18 months and was never guaranteed to have the title you’re looking for in the first place. Signal, first party and third party, device by device, is a leading indicator not a lagging one. Stack the two together and you stop targeting a guessed committee and start targeting the one that’s actually assembling itself around a specific procurement process, shadow group included.

If you want a second opinion on whether your current target account list is still built around titles that have already changed, reply to this email and tell me the account. I read every reply myself.

If a colleague on your ABM or demand gen team is still buying media against a fixed persona list, send this their way, the share button’s right there.

Share The B2B Stack

This is exactly what FunnelFuel built to do. Its TAL Intelligence module and buying-committee scoring exist precisely to reconstruct a shadow buying group from first- and third-party signal, account by account, rather than hand you a persona filter and hope it’s still accurate. If you want someone who has built this rather than sold a slide about it, get in touch or see how it fits together at funnelfuel.io.

Is your own target account list built around a job title that might already be out of date, or a signal-derived group you’ve actually watched assemble itself? Drop your answer in the comments.

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What you’ll learn in this issue:

• Why TV and B2B never got on, and why precision alone doesn’t fix that

• What I’m calling the living room tax, and why it costs more than a wasted impression

• Where CTV context genuinely lines up with a B2B buying journey, and where it doesn’t

• How sponsorship adjacency lets you borrow credibility without the live-event price tag

• Why layering the account graph onto CTV, not just onto display, is the actual unlock

• When it’s worth breaking your own rules for the accounts closest to signing

• The one CTV window that’s more forgiving than any other, and it’s not prime time

Why TV and B2B never really got on

Go back a decade and the mismatch was obvious.

TV is a mass-reach medium built for demographics and dayparts, its adverts invented to fund soap operas in the daytime, which is as far removed from B2B as you’ll get. B2B is a narrow-audience discipline built for named accounts and buying committees.

Unless you were Dell, HP, or one of the handful of B2B brands with genuine consumer crossover, you weren’t on the soap opera ad break, and there wasn’t much reason to be.

Connected TV changed the instrument, not the mismatch.

Streaming penetration is now near universal across households, and identity graph matching means a DSP can, in principle, map a named contact to a device rather than guess at an age bracket.

The tool went from a sledgehammer to something closer to a scalpel. That’s why B2B has piled in over the past eighteen months, and it’s also why the industry is about to repeat the mistake it made with programmatic display a decade ago: assume the targeting precision does the whole job and the creative can stay generic.

The living room tax

Here’s the part most of the CTV sales decks skip. TV is a lean-back medium. People watch it to switch off, not to stay switched on. That matters more for B2B than for almost any other category, because the thing you’re selling into is the exact mental state the viewer is trying to escape.

Advertising has always been about the right person, right time and the wright place, and CTV challenges a marketer to be able to tick all 3.

I’ve heard the same reaction from colleagues often enough that I don’t think it’s a household quirk. It’s a structural cost, and most media plans don’t price it in.

Call it the living room tax: the negative brand equity you incur when a B2B ad interrupts a moment the viewer has deliberately carved out to stop thinking about work.

It’s the inverse of everything CTV is supposed to buy you. Used well, this channel builds brand memory, reinforces positioning, and keeps you present when a procurement process eventually kicks off. Used carelessly, in the wrong context at the wrong hour, it does the opposite of all three, and it does it in the one channel most B2B buyers have the least experience reading.

Where CTV context actually works for B2B, and where it doesn’t

Audio has a context advantage TV doesn’t. A commuter can be listening to a B2B-specific podcast on the drive in, learning as part of the working day. Television doesn’t offer the equivalent. Nobody’s settling in on a Tuesday night to watch a show about programmatic advertising, and pretending otherwise is how you end up serving the wrong ad into the wrong headspace!

There are exceptions, and they matter. Bloomberg, CNBC and the business news tier give you real contextual alignment, because the viewer’s mindset in that environment is already adjacent to work - but lets not kid ourselves about the scale - especially if we’re applying TAL and persona targeting on top. Its diddly squat or close to none existent, a tiny play which will struggle to spend a handful of bucks.

Outside that narrow band, the fix isn’t to avoid CTV. It’s to stop treating every slot the same way.

Borrowing credibility through sponsorship adjacency

Most major B2B vendors already sponsor something: Formula 1, golf, occasionally live cycling. The obvious move is to run the same creative you built for the live event and stop there. The better move is to look at what sits adjacent to it. If you’ve built a sport-specific ad for your F1 sponsorship rather than a generic B2B spot with a chequered flag bolted on, that asset has value well beyond the live broadcast. Streaming shows and documentaries in the same orbit, motorsport documentaries, adjacent sports coverage, give you a much larger canvas to run it against, at a fraction of the live-rights cost. This is smart contextual diversification and is all very doable.

It’s also worth building a wish list of sponsorship-adjacent territory you haven’t committed real budget to. Maybe you like the idea of aligning with the Tour de France or the Giro d’Italia but it’s never made the media plan. CTV is where you test that cheaply, with data-driven buying rather than a full sponsorship commitment, before deciding whether it earns a bigger slice next year.

Where the account graph actually earns its keep

This is the part that turns sponsorship adjacency from a nice idea into something systematic. Most people treat the account graph as a display and email targeting tool. It’s better used as an interest map for the exact accounts you’re trying to move.

If you know which named individuals sit inside your priority buying groups, you’re not limited to serving them the same B2B creative regardless of context. You can look at the wider interest segments those groups actually hold, the sports they follow, the adjacent content they watch, and align your CTV buy against that instead of against a demographic proxy. Done properly, that’s the difference between a blunt, cheap-feeling made-for-streaming ad and one that reads as built for the moment. It’s also the one piece of this a generic media agency running CTV as a line item won’t be set up to do, because it needs the account graph and the media buy sitting on the same side of the table.

When it’s worth breaking your own rules

Everything above is a case for restraint: read the room, match the context, don’t ambush the viewer. There’s one situation where the calculation flips, and it’s worth naming rather than leaving as an unstated exception.

If an account is showing the signals that actually matter, engagement velocity climbing across several members of the buying committee, an intent spike on your priority topics, direct site visits from multiple named contacts in the same fortnight, the living room tax stops being the deciding factor. You’re not interrupting a cold prospect’s evening for a brand impression they’ll resent. You’re staying present for someone already deep in an active evaluation, where a vendor showing up looks less like an ambush and more like confirmation you’re taken seriously. It is not without its risks and we do not want that negative association, but this could be the time to roll the dice and use a pure signals play to run a more generic TV commercial

This only works as a short, dynamically updated list, built from the account graph’s live signal, not the broader ABM tier. Keep it tight. Cap frequency very hard even here, because proximity to a decision doesn’t buy you the right to be repetitive, and let the creative do something a cold-prospect ad can’t: reference the stage they’re actually at, a case study in their vertical, a direct answer to the question their evaluation is likely stuck on, rather than top-funnel brand awareness they’ve already moved past.

Its worth flagging that there’s probably a psychological gap between later on a weekend (including Friday) versus earlier evening weekday spots, and the likelihood of people being fully switched off, or potentially answering some emails and dual screening on a midweek Wednesday. In the above scene, the user is working on the sofa, and the live signal of them being on the vendors website has been picked up and rapidly pipelined to enable TV retargeting. The proximity of signal to advert makes it excusable and more likely to be valuable

The window nobody’s using: office lunch breaks

There’s one CTV moment that avoids the living room tax almost entirely: the forty-five minutes someone takes over lunch, at their desk or in a break room, watching something on a laptop or a tablet they brought in to the office or whilst working from home. I see it in my own office constantly. That viewer hasn’t left work mode behind the way the Friday-night Netflix viewer has. A B2B ad landing there is a much smaller ask, and it’s a more precise buy too, because you’re not extending to the whole household the way a living-room CTV impression often does.

It won’t carry the emotional weight of the sponsorship-adjacent inventory above. It doesn’t need to. It’s a low-friction, low-cost slot most B2B media plans haven’t even considered, largely because CTV planning still defaults to evening viewing by habit rather than by evidence.

The manifesto, shortened

CTV gives B2B a real shot at brand memory, at getting past the dark funnel, and at being in the room before procurement starts.

It also punishes carelessness harder than almost any other channel, because the viewer’s guard is down for reasons that have nothing to do with you. Respect the moment: contextual environments where they exist, sponsorship-adjacent inventory built on creative that actually fits, an account graph doing the targeting rather than a demographic guess, a short exception list for the accounts already deep in evaluation, and the office lunch window nobody else is bidding on.

If you’re weighing whether now’s the moment to add CTV against an account you’re already close on, or you want the exception list built properly rather than guessed at, reply to this email and tell me where the account sits. I read every reply myself.

And if you’re trying to stand this up across multiple regions rather than one market, that’s its own problem, worth reading alongside this piece: Running Account-Based Advertising Globally: The Regional Fault Lines Nobody’s Mapped Yet.

I have spent half my lifetime in the programmatic advertising world, and I know 2 things to be true simultaneously - this is a hugely powerful channel that is often run terribly, and seldomly do B2B brands get the upside that it is capable of bringing.

What you’ll learn in this piece:

• Why segment-level data creates single-vendor dependency and kills attribution before a campaign even starts

• Why clicks are the wrong bellwether for an 18-month buying cycle, and what the buying committee stats actually say

• Why B2B can’t be traded like B2C, and what’s structurally missing when agencies try anyway

• Why attention, not impressions, is the more honest unit of currency, and what it does to your effective CPM

• Why intent data isn’t gospel, and what turns a market signal into an actual buying signal

• Why a 50,000-account target list is really dozens of different audiences wearing one CSV

The segment-level trap

Ask most trading desks how confident they are in their B2B data and you’ll get a strong yes. Ask them to prove which specific buying committee member, at which specific account, saw which specific impression, and the confidence disappears. That gap is the whole problem.

The way most B2B programmatic gets bought today, a desk (e.g. a holding company’s agency trading desk) licenses segment-level data from one of the big providers, points a DSP at it, and buys against the segment as a black box.

Two structural weaknesses fall out of that immediately.

1. First, single-vendor dependency: there’s no cross-check on accuracy, just a bet (or should I say, hope) that the provider’s methodology holds up, because segment-level access rarely comes with the underlying signal to verify it against anything else.

2. Second, and worse, there’s no way to decompose a delivered million impressions back into which accounts and which buying committee members actually received them, in what sequence, on what device, at what point in a deal cycle. Buy against a segment and you get delivery. You don’t get a graph.

That second failure is the expensive one, when the number one one of marketing is to showcase their influence on pipeline. Without impression-level decomposition back to named accounts at a minimum and ideally personas aswell, you can’t build real attribution, you can’t show pipeline progression against spend, and you can’t put a credible marketing-to-pipeline view in front of the board. The campaign might have worked brilliantly. There’s no way to prove it, and “I think it worked” doesn’t survive a budget conversation. And it certainly won’t survive that conversation in 2026 when marketing budgets as a percentage of revenue are falling to as low as 7.5% from a post-pandemic peak of 12.9%. You’d have to be living in an echo chamber to not be noticing this is a tough environment where only the most performant budgets are going to survive - and this sort of shoddy programmatic execution is exactly what taints the whole channel, unfortunately

So even going beyond this segment issue, there are other structural problems with how B2B is generally traded by agency groups who are running a mix of consumer marketing alongside B2B advertising, whilst often retrofitting B2C thinking into a B2B workflow which is much more different than most laymen understand. this approach is further hampered by advertising technology that was always designed for the consumer marketing mega-budgets and not the intricacies of long sales cycle B2B marketing

Clicks are the wrong bellwether for an 18-month decision

It still amazes me how many B2B briefs come with the core KPI of delivering a strong click-through rate.

Clicks aren’t the intent signal that they are often hoped or assumed to be, and sadly this game is not that easy, and it comes back to one wrong belief: that a B2B campaign should be judged on the same short-cycle signals as a B2C one, because a click is a click. It isn’t, once you look at how long a B2B decision actually takes and how many people are in the room.

Forrester’s State of Business Buying, 2026 report puts the typical B2B purchase at 13 internal stakeholders plus nine external influencers, a number that climbs further for complex or strategic buys. Other research houses land on a lower but still substantial range, Gartner’s own tracking has shown buying groups roughly doubling in size over the past decade. I recently heard the buying committee referenced as a ‘buying network’, which is a really interesting extension of those nine external people referenced, meaning that a buying group of 13 people internally may all be impacted by a mixture of:

• ex-colleagues

• trusted industry friends

• wider industry experts and contacts that their industry friends loop in

• Industry WhatsApp groups and Slack channels

• ‘Influencers’ across social with a strong POV

• a whole wider network of people who they may lean on for an opinion or a point of view that may subtly redirect their whole procurement process and which forms part of our often discussed ‘dark funnel’.

Whichever numbers you prefer, the shape of the problem is the same: a genuinely multi-departmental, multi-week (often multi-year) group decision, with the actual proof of a sale usually arriving as a signed contract reconciled in a CRM many months after the first ad ever served. Clicks are always going to be a poor representation of the complexity of this journey, and at most they’re going to be such a tiny and therefore inconsequential part of the motion that they serve as noise - and that is assuming the clicks were all deliberate. As we’ve discussed on previous newsletters, the click metric is so poor and misguided by accidental clicks that I actually wish that digital ads did not redirect via clicks at all, and we could just reset our expectations around what good looks like

That’s the root of the metrics problem. With no fixed attribution point anywhere near the media, campaigns fall back on the nearest available number, which is almost always a click, because it’s cheap to report and easy to graph on a dashboard. But a click on a B2B display ad has never been a reliable stand-in for buying intent, and treating it as one pulls the whole campaign towards whatever inventory generates the most clicks, which is rarely the inventory a genuine buying committee member is actually looking at.

B2B isn’t B2C with smaller invoices

This is where a lot of agency trading desks quietly import a B2C playbook and don’t notice the seams. In consumer advertising, a trafficker optimises to return on ad spend by testing segments against a clean, fast anchor: the transaction. Placements, publishers, formats and time-of-day all get tuned against that anchor because it arrives quickly and it’s unambiguous.

B2B has no equivalent anchor sitting anywhere near the media. The purchase decision belongs to a committee, not a device, and the plumbing that could join those devices, sessions and identities back into one committee, and that committee back into an account, mostly doesn’t exist in the way B2C identity graphs do.

Every impression against every committee member gets measured in isolation, when the reality is that the committee will move as a group and then one person will go and sign. Trade B2B with a consumer mindset and you’re not just missing the graph, you’re missing most of the actual transactions, because the joins between devices were never built for this. Programmatic was never designed to map up beyond individuals and the audience data we collect about them - the whole construct of groups of people mapping to buying groups and then into their business is missing. There is no use case in consumer marketing and adtech was built for that.

There’s a second, more human version of the same mistake. The vast majority of the accounts on any target list aren’t in an active buying cycle right now. The LinkedIn B2B Institute, drawing on Professor John Dawes’s research at the Ehrenberg-Bass Institute, popularised this as the 95-5 rule: at any given time, only around 5% of a category’s potential buyers are actively in-market, and the other 95% simply aren’t shopping yet. Programmatic’s job against that 95% isn’t conversion. It’s memory: presence, association with premium environments, being the name that’s already familiar when a buying cycle does eventually start, which could be in five minutes or five years.

Chase clicks against a market shaped like that and you’ll end up buying cheap inventory that isn’t remotely aligned to B2B decision-making, often on mobile, often in gaming environments or obscure apps, sometimes in genuinely bizarre places.

We’ve seen B2B decision-makers’ devices turn up against potty-training apps and children’s games. The data wasn’t wrong exactly, it’s a reminder that devices get shared inside a household, and that a C-suite parent handing a phone to a toddler is putting on a game, not the Wall Street Journal. Context still matters, even when the underlying data is accurate.

Attention, not impressions, is the more honest currency

If clicks are the wrong bellwether, what replaces them? The most useful shift I’ve seen is trading impressions for attention, because attention starts to normalise the actual cost of inventory rather than just its volume.

Attention measurement specialists like Lumen Research have built a genuinely useful unit for this: attentive seconds per thousand impressions, and an attentive cost per thousand impressions (aCPM) that divides cost by that figure rather than by raw delivery. Lumen’s own research has found that only a small fraction of standard banner units are ever actually looked at, and that the ones that are tend to hold attention for barely more than a second. Run that maths against a premium, above-the-fold, genuinely B2B-adjacent placement and the picture flips: a $30 CPM in the right environment can produce a far lower attentive cost than a $3 CPM in the wrong one, because the cheap unit is mostly buying impressions nobody ever saw.

That reframing matters because it breaks the “more is better” instinct that cheap inventory keeps rewarding. The IAB and MRC’s formal Attention Measurement Guidelines, published in late 2025, are a signal that this is moving from a niche argument to an industry standard rather than a boutique metric a handful of agencies quote. Buying B2B media against attention, not raw impression counts, is one of the clearest ways to stop funding inventory that technically delivered and never actually reached anyone.

Intent data isn’t gospel, it’s a starting hypothesis

The other place programmatic quietly breaks is intent data, and specifically the assumption that a topic-level intent signal equals a signal for your product. An account showing intent around “CRM solutions” is not the same account showing intent for your CRM, and treating the two as interchangeable is how a perfectly good intent feed ends up wasting budget.

The upgrade here is combining that market-level intent with your own first-party behavioural data, web analytics, CRM activity, the account’s actual footprint on your own properties, so a wider category signal only counts once it’s corroborated by something the account has actually done with you. That’s the difference between intent as a market-wide hypothesis and intent as an actionable buying signal, and it’s a large part of what a genuine account graph, built from a partnership like the raw signal-level relationship we run with Bombora, is for.

Not all accounts on the list are equal

The final, and probably most under-discussed, failure is treating a target account list as one flat audience. A 50,000 or 100,000-account list looks like a single, coherent thing in a media plan. It isn’t. It contains wildly different company sizes, industries, technographic profiles and, crucially, wildly different deal sizes if a purchase ever happens. A small business and a prospective nine-figure enterprise account can sit in the same CSV, get the same frequency, and get treated by the platform as equally valuable.

The fix isn’t more reach against that list, it’s segmentation before the campaign even starts: pre-tiering by deal size, firmographics, technographic fit, and layered intent and first-party behavioural signals, so the accounts showing genuine live motion get bid harder and more frequently, while the rest of the list gets the steadier, background brand-memory treatment the 95-5 rule says most of the market actually needs.

Curated Deal IDs built off decomposed, signal-level data, rather than a single aggregated segment, are what make that tiering possible at the supply side, and it’s the difference between a campaign that treats a nine-figure prospect the same as a small business, and one that prices the two accordingly from day one.

Field Notes: the direction of travel across the industry backs this up rather than undercutting it. Forrester’s 2026 buying research and the IAB/MRC’s move to formalise attention measurement both point the same way, buying groups keep getting larger and harder to reach with a single generic segment, and the industry’s own measurement standards are shifting away from raw delivery towards something closer to genuine signal quality. Neither development was written with programmatic in mind, but both make the case for signal-level buying stronger, not weaker.

Do this properly and the return isn’t just cleaner reporting. It’s reach that’s genuinely broader than anything available on LinkedIn alone, because the vast majority of a buying committee’s actual time online happens across the open internet, not inside one professional network, provided the buy is built on a graph good enough to find them there.

So here’s the actual disagreement worth having: is the industry’s segment-level default a reasonable trade-off for scale, or is it the single biggest reason B2B programmatic keeps underperforming its own potential? I’ll take the second position and argue it in the comments if you don’t.

Sharper identity resolution, a persistent ID that survives a rejected cookie, a CRM finally stitched to organic and paid behaviour in one clean line. Give it another year or two, the thinking goes, and someone will crack it.

Nobody is going to crack it. Not this year, not in 2027, not ever, because the places your buyers actually do their thinking were never built to be seen. Private Slack channels, WhatsApp groups, one-to-ones over coffee, premium research behind a rejected cookie banner. That last one isn’t a side note. I wrote about why the consent architecture guarantees this outcome structurally, not accidentally, in The Consent Tax. Put the two together and you get a simple conclusion most marketing teams haven’t sat with yet: the dark funnel isn’t a temporary gap in your measurement stack. It’s the permanent shape of B2B research from here on.

So the question worth asking isn’t “how do we see it?” It’s “given that we can’t, what do we build instead?” The smart marketers in B2B are evolving their way around it, and thee are some highly valid tactics you can use to do so

What you’ll learn in this piece:

• Why treating the dark funnel as a tracking problem guarantees you’ll always be a step behind it

• The genericised trademark playbook, and why Google is the biggest, most geography-proof version of it (but I used Xerox in my title to void confusion with SEM/SEO and agentic ero click optimisation)

• Why B2B already has its own live example of a brand becoming a verb, and what that proves

• The three-rung ladder from “known vendor” to “unaided category recall” to “the word people actually use”

• Where brand memory gets built in 2026, now that clicks and cookies aren’t the mechanism

• Why this only works as a permanent discipline, not a campaign

Nobody designed the dark funnel. It just won.

Every dark funnel piece, including the three I’ve written before this one, tends to start from the same place: here’s what’s happening in the shadows, here’s a clever way to infer it or beat it. You can see some of those themes in the below pieces

That’s still useful work. It’s also a losing long game, because the environments themselves keep getting more closed, not less.

Private communities require an invite, and vendors aren’t likely to get one, for fairly obvious reasons. Premium content sits behind a paywall a rejected cookie banner won’t unlock. AI chat interfaces return an answer with no query string, no referrer, no trace that the conversation happened at all.

Treat that as a bug and you’ll spend the next three years chasing a fix that never lands. Treat it as the permanent operating environment and the whole question changes. You stop asking how to see the research your buyers do in private, and you start asking how to make sure your brand is already sitting in their head before they open that private conversation in the first place.

That’s not a consolation prize for giving up on measurement, and I sy that as someone who has built B2B analytics with an account graph over a GA4 style interface, and we extensively use that to de-anonymise. We constatly push the boundaries of what can be tracked, legally and ethically. However, in reality, some stuff just cannot be tracked. It’s the only strategy that was ever going to work inside a space you can’t measure, because it doesn’t depend on measurement to pay off.

The biggest, most geography-proof version of this playbook already has a name

There’s an old trick in consumer branding for exactly this problem, and most people know it without knowing they know it. Hoover became the word for vacuum cleaner in the UK. Sellotape became the word for adhesive tape. Neither company won by tracking every household’s cleaning habits. They won by becoming so synonymous with the category that the brand name replaced the noun.

That’s a nice piece of history, but it’s also geographically narrow. Ask someone in Chicago what a Hoover is and you’ll get a shrug. So if you’re looking for the one example that proves this works everywhere, in every market, in a way that translates instantly, there’s a bigger one sitting in plain sight.

Google didn’t just win search. It won the verb. Nobody says “let me run a web search for that.” They say “just Google it,” in English, in a dozen other languages that have absorbed the same construction, in markets where Google the product barely has meaningful share. AI and the growth of LLM’s may have slightly weakened this point, but I think most of us reading this will be old enough and experienced enough to understand the strong parallels here and just how synonymous with search Google was and is. The brand detached itself from the company and became the name for the action itself. That’s a different and much harder achievement than owning a category noun, and it’s the one worth aiming at, because it survives translation, survives platform switching, survives everything except actually being replaced as the default way people describe what they’re doing.

B2B already has its own proof this can happen, and it’s recent

Here’s the part that should make this land rather than feel like borrowed consumer theory. B2B has already produced its own version of this, inside living memory, inside software, which is the exact category most of us sell into.

Nobody says “let’s start a video conference.” They say “let’s Zoom.” That sentence survived the fact that half the people saying it are on Teams or Meet. Zoom became the verb for a category of behaviour so completely that it now has to be actively fought against by every competitor that launched after it, because the word people reach for by default is already taken. That’s not a marketing campaign that worked. That’s a company that got so deep into daily language that its rivals are permanently playing catch-up on vocabulary, not just features. you may be reading this and thinking, “Well, Zoom has had its day, and it’s not exactly at the forefront of video conferencing anymore.” That’s a valid point, but I would almost argue that it stands truer in the opposite sense because Zoom has been left so far behind by enterprise bundled solutions like Teams that come with Microsoft or Meets that comes with Google. The synonymous nature of its name is what keeps it in any conversations in 2026. What I’m arguing for here is not whether the brand wins or loses, but how do we ensure the brand is in the conversation. I believe that Zoom being so synonymous with video conferencing keeps it in the conversation today when it otherwise may not be if it wasn’t a noun - and that is the point here

If it happened once, in B2B SaaS, inside a five-year window, it isn’t a freak occurrence reserved for hundred-year-old consumer brands. It’s a playbook. The question for the rest of us is what it actually takes to run it deliberately rather than stumbling into it the way Zoom did.

The three rungs between forgettable and inevitable

Brand memory in B2B isn’t a single state you either have or don’t. It’s a ladder, and most companies never get past the bottom rung.

Almost nobody in B2B operates with rung three as an actual target. That’s exactly why it’s available.

You May Be thinking ‘Obviously This Would Be Great’ - But How? What We Are Talking ABout Is Good Old Fashioned BRAND Advertising

None of this comes from a landing page or a retargeting sequence, because the whole point is that it has to work in environments where nobody clicks anything. It gets built through repeated, low-friction exposure across the surfaces your buyers already spend time in, whether or not that exposure is trackable. I would call out the low friction part of this, because it is a post into-itself. TLDR when we go multi-surface, it is incredibly powerful, but we need to be respectful of environment and time of the day (think no B2B ads during Friday evening movie night)

Audio is one of the most under-used of these. Senior B2B buyers spend real time in industry podcasts, in music while training, in the dead time of a commute, and almost none of that time is being bought with any intent behind it. Contextual advertising is the other underrated lever, and it’s underrated for a specific reason: the moment cookies get rejected, contextual is one of the few signals still standing, which is why I’ve written before about how native and contextual carry signal on their own rather than acting as a fallback for when targeting fails. Physical presence does real work here too. A well-placed out-of-home campaign builds exactly the kind of ambient recognition that later surfaces as “I keep seeing them everywhere” inside a private conversation you’ll never see, and that’s a large part of why it functions less as a performance channel and more as a credibility signal that builds up over months rather than a single campaign.

B2B influencer and creator partnerships belong on this list for the same reason. The people carrying weight of voice in your category are precisely the people who get pulled into the private Slack threads and DMs where your buyers are actually forming opinions. Show up in their orbit and you’re borrowing distribution into rooms no pixel will ever enter.

The discipline is always-on, because you don’t get to choose when it counts

Here’s the tough part, including being tough on the budget line. Most of any target account list is not in-market right now. There’s a commonly cited rule that roughly 95% of B2B buyers aren’t actively in a procurement cycle at any given time, and whether that number is exactly right matters less than the shape of the problem it describes. Because of the dark funnel, you don’t know which slice of your list is in the 5%. You can’t reliably see who’s started evaluating, and pretending otherwise just means building a media plan around a guess dressed up as a signal.

Once you accept that, campaign bursts timed to imagined in-market windows stop making sense. The only strategy that survives contact with “we don’t actually know who’s looking” is presence that never switches off, built at the category and vocabulary level rather than the individual account level, running as business as usual rather than as a push tied to a quarter’s pipeline target.

That’s a harder thing to sell into a board than “we ran a campaign and it drove X meetings.” It’s also the only version of brand building that pays off inside a research environment that was never going to let you watch it happen.

The dark funnel isn’t going to get smaller. The question is whether your name is already the word your buyers reach for when they’re in there.

Are you building for unaided recall, or are you actually trying to own the verb? Tell me where you think your own category sits on that ladder, drop it in the comments.

In case you’re wondering what I am talking about, here is an example that we ran in NY in August to support some innovation sessions

So the wrong belief isn’t “B2B doesn’t do billboards.” It’s the belief sitting underneath most B2B media plans without anyone examining it: that the channel itself is the barrier. It isn’t. The barrier is that almost everyone running it is buying a consumer product and hoping it behaves like a B2B one. Broad polygons. Generic footfall. Bought like reach, measured like nothing, aimed at nobody in particular. Ouch, fortunately there is a better way

This isn’t an abstract argument for me. We’ve been running DOOH ourselves this week in New York, supporting some big meetings on the ground. FunnelFuel on a billboard on 34th Street isn’t what you’d expect to see, and that’s exactly why it lands.

It meant nothing to 99.9% of the people who walked past it. It wasn’t supposed to. We were seeking impact from a small, incredibly relevant handful, and I’d take a passing glance from one of them as a big win. An array of m NY team have all been in pitches and innovation sessions this week that the same screens were deployed to run behind their efforts. It’s the kind of thing that’s aided pitches before, amplified events before, built credibility for us before. I’m a believer, and I want to make the case for why properly aimed.

What you’ll learn in this article:

• Why I think about DOOH as “in-the-moment advertising,” a close cousin of contextual, and what that framing actually buys you

• Why the billboard is the oldest attention format in advertising, and what changed when it went digital

• How programmatic DOOH targeting works today, and why “targeted” means something structurally different here than in display or CTV

• The real adoption numbers behind B2B out of home, and why they complicate the easy “nobody does this” story

• What a precise account graph, down to polygon-level company footprints and live event schedules, actually does to a DOOH buy

• The mental math I run on attribution here, because it will never look like the clean 1:1 account-and-persona match you get elsewhere

• What sharp, best-in-class location marketing looks like for a B2B vendor prepared to treat this as infrastructure rather than a stunt

In-the-moment advertising

I’ve always thought about DOOH in a similar vein to how I think about contextual, related, but not the same trick.

Contextual works by borrowing relevance from the content sitting around the ad: the ad next to the right article inherits a bit of that article’s credibility and reader impact.

DOOH works by borrowing relevance from the moment sitting around the person: a screen that catches someone walking to a meeting, already in a work frame of mind, already thinking about who they’re about to sit down with, inherits a bit of that moment’s seriousness.

Timed right, at the point a buyer’s already in motion, already turning the meeting over in their head, already at an event and switched into deal mode, that’s not broad reach doing broad-reach things. It’s a single, oddly specific touchpoint arriving at the one moment it could possibly matter. That’s why I don’t measure it the way I’d measure a channel built for scale. The job isn’t impressions. The job is showing up in exactly the right five minutes of somebody’s week.

A very old channel pretending to be a new one

Outdoor advertising is the oldest format in this industry by roughly two thousand years, depending how generous you’re feeling about ancient Egyptian government notices carved into stone.

The billboard as we’d recognise it starts later and more specifically: Jared Bell’s fifty-square-foot circus posters, printed in New York in 1835. That’s the format’s actual birth certificate, and it’s worth sitting with, because the poster wasn’t invented to build brand affinity. It was invented to tell a travelling public, at a glance, that something specific was happening nearby and soon. Location and timing, not reach, were the entire point from day one. I think us B2B marketers can learn something from that

By 1889 the twenty-four sheet format had emerged and the industry was becoming organised, and by the interwar decades (The 1920’s and most of the 1930’s) outdoor had entered what’s generally called its golden age, hand-painted, highway-adjacent, built for a country that had just put itself on wheels. The Highway Beautification Act of 1965 then did what regulation usually does to a mature channel: it fixed the format in place, tying billboard siting to commercial and industrial land use rather than letting it sprawl unchecked. Outdoor spent the next few decades as the industry’s most reliable, least sophisticated channel. Big, dumb, and everywhere.

The digital transition didn’t change what a billboard fundamentally does. It changed what a billboard fundamentally knows. A static poster reaches everyone who drives past it for the four weeks it’s booked. A digital screen can decide, impression by impression, who’s likely walking past right now and what to say to them. We’re moving from blunt to reach to principals which align closer to the ethos of digital marketing. That is exciting and represents a much bigger opportunity for B2B.

So the format is not shifting, it is still a big screen. But that age old format is acquiring a nervous system, and it’s what makes the in-the-moment framing possible at all. You can’t time a static poster to a meeting. You can absolutely time a digital screen to one.

What “targeted” means once the screen goes digital

This is where most B2B marketers stop paying attention, because the word “targeted” gets used loosely and it’s doing very different work depending on the channel. In display or CTV, targeted usually means something close to one-to-one: a cookie, a hashed email, a household ID, a specific person or device shown a specific ad. The Account Based Ads that built my business were predicated on our ability to reach individual committee members in individual accounts, and report that impact back, impression by impression. Its sniper rifle advertising, picking off targets one by one.

DOOH was never built to do that and modern programmatic DOOH doesn’t pretend to. Instead it triggers creative off conditions that make an impression more or less relevant at the moment it’s served: proximity to a point of interest like a stadium or a venue, live data feeds, daypart, weather.

The whole model runs on signals rather than static assumptions, and deliberately doesn’t rely on identifying the individual standing in front of the screen. It relies on relevance instead. That’s not a limitation bolted on after the fact for privacy reasons, though privacy compliance is a welcome side effect. It’s the structural nature of the billboard surface. A screen bolted to a building can’t know who’s underneath it. It can only know where it is, when it is, and what’s statistically true about that combination.

Programmatic buying now makes up over 30% of DOOH ad spending and the segment is forecast to grow around 19% year on year, and the whole category is projected to climb from roughly $22.5 billion in 2026 to $56 billion by 2034. That growth is coming almost entirely from the shift away from manually negotiated static bookings towards automated, condition-triggered buying across DSPs and SSPs. The infrastructure is maturing faster than the strategy sitting on top of it, and that gap is exactly where the B2B opportunity lives.

The paradox, in the actual numbers

Here’s where my starting assumption fell over. A study from OneScreen.ai and Wynter found 46% of B2B companies are already using OOH, with adoption strongest among mid-market firms between 201 and 5,000 employees. That’s not a rounding error. That’s a plurality of the market already writing cheques for billboards and transit screens.

But look at why the other 54% are staying out, and the complication in this story shows up. Seventy seven percent cite a lack of measurement as their main concern, and 57% had ruled the channel out because they doubted it would ever reach their actual ICP. This really caught my attention, and thinking about the FunnelFuel ads on 34th street in NYC in August, it has to be a valid ask. The measurement one is a different problem, and I simply think we have to be more comfortable, as B2B marketers in 2026 and beyond, to accept the dark funnel stretches to dark advertising, but the whole ‘50% works, 50% doesn’t and I don’t know which’ is as old as advertising itself, and sometimes we have to cast a trawler net alongside the harpoon gun precision of other digital tactics. Gosh I am on a roll with weapon related analogies today, and can only apologise, but hopefully the point lands.

And here’s the genuinely interesting part, the bit that should stop any B2B marketer mid-scroll: 52% of marketers separately believe OOH can reach their ICP effectively. That’s not a market that’s decided against the channel. That’s a B2B vendor market arguing with itself, half convinced it works and half convinced it can’t be proven, which is precisely the state you’d expect a channel to be in when the buying mechanics haven’t caught up with the medium’s actual capability.

Fifty nine percent of marketers still buy OOH inventory only through direct deals rather than programmatically, which tracks with what I’d expect from a channel most B2B teams are running as an occasional set-piece rather than an integrated part of the media plan.

And 42% of marketers specifically want to use OOH to “swarm” events, dominating the conversation in airports, hotels and transit hubs around a major conference, which is the single most B2B-native instinct in the whole dataset and the one the industry is worst equipped to execute precisely. It’s also, in my view, grossly under-utilised even within that 42%, because most of it is still guerrilla presence outside a venue rather than a coordinated pincer with the stand you’re already paying for inside it. Events matter enormously in B2B, and DOOH is one of the few channels that works both sides of the door, and which can, digitally, support these events. It is therefore odd that it isn’t deployed more, but perhaps there is a knowledge gap in terms of what can be done?

So the honest headline isn’t “B2B ignores DOOH.” It’s this: a large minority of B2B brands are already spending real money on a channel they don’t trust, buying it the way a consumer brand would, and getting consumer-grade results back, which then confirms the very doubt that made them cautious in the first place. That’s a worse position than not running it at all. It’s spend without conviction, feeding a measurement gap that then gets blamed on the channel rather than on how it was bought.

What precision actually requires

Everything above describes DOOH bought against a broad location. What changes the entire equation is DOOH bought against an account graph, and the difference between those two things is the whole argument of this piece.

A location is a postcode, a radius, a commuter corridor. An account is a specific legal entity, sitting in a specific building, at a specific floor if you can get that granular, with a known parent-subsidiary structure, a known headcount, and a known technographic and intent profile sitting on top of it. Some vendors like D&B’s Commercial Graph anchors that identity in the persistent D-U-N-S Number, and at FunnelFuel scale that means roughly 370 million company records as the base layer, not a sample, the whole global population of registered businesses. Layer polygon-level building footprints on top of that, the kind of geospatial precision that maps a specific office to a specific rooftop rather than to a three-digit postcode, and a billboard stops being a location buy. It becomes an account list with a physical address.

Add event schedules and the account graph gets a second, more valuable dimension: time. Knowing that a target account’s building sits within four hundred metres of a screen is useful. Knowing that six named accounts on your target list all have people walking to the same conference venue between 8am and 9:30am on a specific Tuesday is a different order of precision entirely. That’s the account-based geofence done properly: not “somewhere a relevant person might walk,” but “this specific set of companies, moving through this specific corridor, in this specific window, because we know their office and we know the agenda.”

This is also where the honest limitation shows up, and it’s worth naming rather than talking around. The available inventory for this level of precision isn’t guaranteed everywhere yet, and surrounding a single target building with placements at scale is still the exception rather than the rule on most programmatic DOOH platforms. There’s also real wastage in shared or multi-tenant buildings, where the screen reaches every employee in the building rather than just the roles on your target account list. An account graph fixes the targeting logic. It doesn’t yet fix the physical constraint of a shared lobby, and anyone selling you a story where it does is overselling the inventory reality.

The mental math: forget the 1:1 you’re used to

If you’ve spent any time in a modern B2B programmatic stack, you’re used to a specific shape of attribution. A device ID resolves to a person. A person resolves to an account. A hashed email confirms the match. You get something close to a clean 1:1 line between impression and buying committee member, and DealID-level reporting that tells you, with real confidence, which accounts saw which creative on which channel.

I can’t do any of that with DOOH.

I can’t frequency-cap it against an account journey the way I can with CTV or display, or anything else we run at FunnelFuel. I can’t precisely target the account or committee 1:1 like I usually do. Attribution is highly probabilistic at best: MAID proximity to the billboard, subsequent site visits tracked with our analytics, that kind of estimate.

But sometimes we let incomplete attribution get in the way of stuff you just know has an impact. That’s always been the ads game. Trying to track every touchpoint is unrealistic. B2B lives in the dark funnel, and we’ve seen and heard the impact of this firsthand, many times over.

So yes, there are limitations, and measured purely on impressions there’s doubtlessly tonnes of waste. How many people walking down 34th Street this week could give a damn about FunnelFuel? Realistically, almost all of them. Which is exactly why DOOH probably shouldn’t be more than 5 to 10% of any serious B2B media plan.

But if a buying committee member at a key account walked down 34th Street this week on the way to an innovation session we were running, and happened to see it, then what? I believe it would have anchored credibility at a key moment, made a talking point, and elevated FunnelFuel pre-meeting. In that guise, it might be the most powerful account-level touchpoint you can drive, and no attribution model I currently have access to would ever show me that it happened.

How B2B brands can win with DOOH

Put the pieces together and the playbook for a B2B vendor prepared to do this properly looks distinct from anything in the current adoption data.

Start from the account graph, not the map. Pick the named accounts you’re already running paid media against elsewhere in your stack, resolve their real office locations at polygon precision rather than postcode centroid, and only then look at what screens sit inside a genuinely walkable radius. This inverts the usual OOH planning process, which starts with available inventory and asks who might walk past it. Starting from the account list first means every impression you buy is already qualified before the creative decision gets made.

Layer event schedules on top as a second targeting axis, not an afterthought. The 42% of marketers already trying to swarm events are onto the right instinct, but most are doing it geographically, blanketing the airport and the conference hotel. The sharper version is temporal as well as spatial: knowing which of your named accounts are actually attending, when their travel windows are, and flighting placements to intersect with those windows specifically, rather than running a flat schedule across the whole event and hoping for overlap.

Treat DOOH as the proof layer sitting on top of a reach layer built elsewhere, not a standalone channel with its own KPI. Its job is to make every other touchpoint in the account journey land better, the CTV ad more credible, the outbound email less cold, the sales meeting less like a pitch and more like a continuation of something the buyer had already half-registered. Measure it as a contributor to those other channels’ performance, not in isolation.

And be honest with the buying committee, internally, about what this channel can and can’t prove. The teams currently sitting in that 54% who’ve ruled DOOH out over measurement doubt aren’t wrong that the proof is harder to get. They’re wrong that harder proof means no proof, and they’re leaving a channel that’s already touching decision-makers at exactly the moments that matter, walking to a meeting, arriving at a conference, standing outside a client’s building, to competitors willing to buy it on faith and directional data instead.

Forty six percent of the market has already worked out that DOOH belongs in a B2B plan. The share that’s worked out how to aim it precisely is a fraction of that. That gap, not the channel itself, is the actual opportunity, and it’s exactly the kind of underpriced attention this industry keeps walking past because the measurement doesn’t look like the spreadsheet everyone’s used to filling in.

Think this is overstating the case for a channel most B2B teams still treat as vanity spend? Tell me why in the comments, I’d rather have that argument than not.

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In a nutshell: an identity graph is a solved problem. Stitching IP 1, 2, 3 and 4 to the same device, the same cookie, the same hashed email, that’s mature, commoditised plumbing, and most of the market has a version of it. Its job is to take something you already know about a person and extend the addressability of that fact across every device and identifier they touch. It’s as relevant to B2C as it is to B2B, arguably more so, because B2C never needs the second graph at all.

An account graph is an entirely different and much harder problem, specific to B2B, and hardly anyone building B2B media has actually built one. What gets sold as an “account graph” in most pitch decks is a well-branded identity graph with a company name bolted on top. That’s not a small distinction. It’s the difference between knowing which fragments belong together and knowing which businesses belong together, and B2B targeting lives or dies on the second one

What you’ll learn in this piece:

• Why an identity graph and an account graph solve two different problems, not one problem wearing two names, and we’ll take both apart properly

• What actually builds the account layer, the enrichment stack most vendors skip

• Where identity resolution comes back in, and why you still need it once the business structure is understood

• Why one graph, correctly built, does the job of five separate point solutions, from signal mining through to attribution

• What the canonical object of an account graph really is

• How the two layers interrelate, and where confidence is won or lost between them

• Why B2B specifically can’t afford to get this wrong

Before any of that, it’s worth being honest about what problems we’re actually trying to solve, because it isn’t just “better targeting.” It’s:

• Mapping user-level buying committees to accounts, and qualifying that mapping all the way from website traffic through to ad impressions

• Building an addressable, data-driven target account list from CRM exports, existing lists, or multi-variate inputs across firmographic, technographic, and other enrichment data

• Applying signal to segment that list properly, rather than treating every account as an equal-sized prize

• Targeting each of those accounts with the highest realistic probability of actually reaching the right people inside them

• Reporting on the impact of the advertising, from ad impression through to website analytics, on the same object throughout

• Attributing account progression back to marketing activity, so marketing-influenced pipeline is something you can demonstrate rather than assert

Solve that properly and you need two graphs, not one.

What an identity graph is actually doing

Start with the more familiar of the two, because it’s worth being precise about its job before anything else. An identity graph is an interoperability layer. It takes fragments, an IP address, a cookie, a device ID, a hashed email, and works out which of them are the same underlying thing showing up in different clothes.

IP 1, 2, 3 and 4 might all resolve to the same device. That device might share a hashed email with a mobile ad ID (MAID) seen somewhere else entirely. The identity graph’s whole job is stitching that interoperability together so a single known fact about someone can be addressed everywhere they show up. It doesn’t know or care what business any of it belongs to. Its job is to maximise addressable scale, full stop.

What an account graph is actually doing

An account graph starts somewhere completely different. It maps the relationships between real companies: which subsidiary sits under which parent, which entities share a global holding structure, which trading relationships connect one business to another, where the org chart genuinely sits versus where a stale firmographic database thinks it sits. It also looks like the people that constitute the company; which offices host which roles, where does the c-suite sit, how big is each department etc.

So the graph asks itself lots of key questions to help us understand precisely what a company does. Stuff like; Which office do the engineers actually work out of? What’s the org’s revenue range? What do they do, and what technologies do they run? Are they hiring AI engineers right now, and what does that tell you about what’s coming? None of that requires a single device ID or cookie to build. It requires enrichment, at scale, from sources that have nothing to do with the bidstream. Its job is to build the biggest, most joined-up repository of account-level signal you can, full stop, in the same way the identity graph’s job is to maximise addressable scale.

That’s the distinction worth holding onto, because the industry collapses it constantly. A big identifier graph is not a proxy for a well-built account structure, and a lot of what gets marketed as “account-level” targeting is really just identity resolution with a logo pasted over the top. Working out that a subsidiary in Frankfurt, a holding company in Delaware, and a trading entity in Singapore are all, functionally, the same buyer is a genuinely uncommon capability. Most of the market never gets past the identifiers.

Once you have the best, most complete account graph you can build, that’s when you stitch an identity graph into it, not the other way round.

Why the standard identity graph doesn’t fit B2B

Here’s the part most of the industry misses entirely. The standard identity graph is B2C-shaped. It thinks in households, matching one household’s IPs and devices to everyone living under that roof. In B2B, households are irrelevant. What’s missing instead is an entirely different layer of hierarchy that programmatic, as a discipline, was never built to think about:

• The account, where every relevant identifier maps up to a single global account level

• The buying committee, where every relevant identifier maps to the specific group of people actually involved in that decision

Neither of these is native to programmatic, because programmatic thinks in one unit: the end-user. That is equally true for B2C focussed walled gardens like Meta, YouTube and Reddit.

The standard ask of an ID graph could go like this; end-user-1 visited a sneaker site, added to basket, didn’t buy. Sneaker intender, retarget. There’s no buying group needed to sell trainers. There is one for $100m of enterprise server infrastructure.

Take a live example. Run a standard identity graph against a $100m enterprise infrastructure deal and the small handful of buying committee members who happened to visit a hyperscaler’s website get heavily graphed and retargeted, individually, as if they were five separate sneaker intenders. What that standard ID graph, the one everyone talks about having, can’t do is understand the relationship between those people, or how they map into a single buying group. Run the same exercise through an account graph and you’re picking up that same signal and mapping it back to an active buying committee inside a wider account.

An account graph becomes the ultimate B2B enabling technology

That single shift from individual identity [graph] to an account identity graph changes what you can actually do with your marketing. Now you are empowered to;

• Re-engage the whole buying group, or the whole account, rather than five disconnected individuals

• Manage frequency and pacing across the group rather than per browser (or even worse if you run half a dozen managed services, self serve seats and direct buys - by the provider

• And orchestrate a message at a buying committee level across channels so the CFO and the security lead aren’t seeing the same generic creative twelve times each while someone else on the committee sees nothing at all.

• Run properly joined up ABM programmes globally, across channels. This is why it matters as much as it does.

One graph, the whole spectrum

That last point is worth pulling apart properly, because it’s the difference between an account graph as a targeting nicety and an account graph as infrastructure. I very much see an elite B2B account graph as B2B infrastructure

Once every signal resolves back to the same account and committee object, rather than living in five separate point solutions that don’t talk to each other, the whole list of problems from the top of this piece stops being five separate problems.

Frequency and orchestration are the clearest case. Without a shared account and committee object, every channel caps frequency in its own silo, display doesn’t know what CTV already showed the same person, audio doesn’t know what DOOH just did outside their office, and the buying committee ends up over-served on one channel and invisible on the others. With a shared object, frequency gets managed at the account and committee level across the whole media mix, not per channel, which is the difference between reaching a buying group and repeatedly reaching the one person on it who happened to be easiest to identify.

The same object carries the rest of the spectrum too. Signal mining and analytics on your own website resolve onto the same node used for targeting, rather than needing a second identity exercise bolted on afterwards to make the two talk to each other. Imagine the power of GA4, Adobe analytics or any other such kit, but with the ability to resolve sessions back to the account graphs accounts and personas? How would that impact your list segmentation, and your campaign reporting?

Segmentation stops treating every account as an equal-sized prize, because the enrichment layer already tells you which ones are worth weighting harder. And reporting and attribution close the loop on the same object they started on, so account progression, a deal moving stage, a committee member re-engaging, attributes back to the specific marketing activity that touched it, rather than to a generic MQL fired by an anonymous cookie that happened to convert around the same time.

One graph, correctly built, does the job that five disconnected tools are usually stitched together, badly, to attempt.

What actually builds the account layer

The account graph is built from a multi-variant stack of enrichment partnerships, not from anything captured in an auction. This is where the unglamorous, expensive part of the work sits: firmographics, technographics, industry classification, open job roles as a hiring-intent signal, credit ratings, ownership and trading-relationship records, and the varying, often contradictory, levels of data quality that exist across different providers covering the same company. Marry this together with extensive web crawling, on contextualisation, big stream modelling and other data ingestion across APIs, MCPs, and all other available data sources. It is one heck of an exercise to do it well.

Every one of those sources answers a different piece of “who is this business, and who is it connected to.” Technographic scans tell you what they run. Job postings tell you what they’re about to need. Ownership records tell you whether the account you’re targeting is actually three accounts wearing different domains after an acquisition nobody updated. None of this is identity work. It’s the business-structure work that has to happen before identity resolution means anything at all, because resolving a device to the wrong account, cleanly and confidently, is still wrong.

Where identity comes back in - to make an account identity graph

Understanding the business doesn’t buy you a single impression.

Three hundred million known businesses mapped and structured is a genuinely useful asset, but it’s not much use if you can’t reach any of them.

To actually activate against it with live programmatic advertising, you need to map real, ephemeral signals back onto that business structure: hashed emails, probabilistic and predictive identifiers, mobile ad IDs and device IDs, contextual signals, hyperlocal signals, network-level IP signals.

This is identity graph work again, but it’s no longer standing alone and mapping to end users singularly. It’s being resolved onto a business structure that was built independently of it, which is the part most of the market skips. Mapping identifiers to business-relevant hierarchies like the account and the buying committee is what makes identity actually mean something in B2B, rather than just meaning something in general.

This is also where FunnelFuel’s actual definition sits, and it’s worth being direct that it isn’t industry-standard vocabulary, because it isn’t. An account graph, as we build it, is the combination of two layers: the account-relationship structure (who owns whom, who trades with whom, who sits under which parent) and the identity-resolution layer mapped onto it (which real signals, in a live auction, actually belong to which node in that structure). Neither layer alone is the account graph. The identity layer without the business structure is just a very good identity graph. The business structure without live identity resolution is a static database you can’t activate against. It’s the combination that’s rare, not either half in isolation.

These things are tedious, capex-intensive, and genuinely difficult to build. We’ve conservatively spent £8m and counting building ours. The focus throughout has been maximising scale on both sides at once, the widest possible map of known accounts, and the deepest possible stack of identifiers, so we can confidently reach them. Confidently is the operative word. It means not taking any single data vendor’s word for it, accepting that even the best providers get things wrong or have gaps, and using machine learning at scale, alongside deep contextual mining and signal aggregation, to actually understand the confidence behind each individual identifier before it gets used. Then it’s about deploying the most advanced programmatic workflows available to unearth what each of those validated signals is actually worth. This isn’t a pitch, it’s the honest scale of the R&D behind doing this properly. And before anyone reads this as something you knock out with an afternoon of AI-assisted coding, most of that £8m has genuinely benefited from automated development and the efficiencies it brings.

That’s not a contradiction. It’s what the sharpest edge of B2B programmatic actually looks like in 2026, built faster because the tools got better, not built any less seriously.

It is also why I get miffed when this type of technology asset is fired up to drive CTR!

The canonical Account Object, properly defined

Every account graph needs one true representation of each real company, and it has to be positioned correctly inside its actual ownership structure, not just deduplicated by name.

A business might present as four domains after a rebrand, a dozen device fingerprints across regional offices, and three conflicting entries across different firmographic providers who don’t agree on where the parent-subsidiary line sits.

The canonical object is the answer: one node per real company, correctly nested under its actual holding group or parent, versioned as the structure changes through acquisition or restructuring, with every identity signal you resolve attaching back to that specific node as an edge rather than spinning up a duplicate elsewhere in the graph. Get the hierarchy wrong here and you don’t get one clean account graph, you get several contradictory ones, bidding against each other for the same buying committee without anyone noticing it’s the same committee.

Where it agrees, and where it doesn’t

This is where the two layers actually meet, and it’s where confidence is won or lost. Once you know a business’s structure and you’ve resolved a set of identity signals onto it, the real question is whether those signals agree with each other and with the structure underneath them.

Does a hashed email match land inside the same corporate entity that a device ID resolved to? Does the network IP range the auction traffic came from sit inside the parent company you’d expect, or does it belong to a subsidiary that was sold off eighteen months ago and never got updated in a firmographic database somewhere?

Contextual and hyperlocal signals corroborate or contradict in the same way. Every additional signal that lands on the same node from an independent source, one that had no reason to agree unless the underlying answer is correct, raises confidence in that resolution. A single signal, however clean, never gets you there alone.

Why B2B specifically can’t afford to guess here

This isn’t an abstract precision argument. Heck, an account graph is expensive to operate against, so it has to earn its keep. In B2B, I know it does (disclaimer; I have seen the CRM data to prove the model)

B2B deals of any real size are decided by committee, and that committee has been getting larger. Gartner’s most recent buying research puts the typical committee at 6 to 10 stakeholders for deals above $100,000, up from 4 to 6 as recently as 2017, and Forrester’s research on deals above $1 million puts that figure at 14 to 23 stakeholders. Median cycle length on enterprise deals above $100,000 runs to roughly 11.5 months.

Every one of those stakeholders is a separate identity that needs correctly resolving back to the same account, over a buying cycle measured in months, not days. Get the account structure wrong, or resolve even a handful of those stakeholders to the wrong node in it, and you’re not slightly off target, you’re running a campaign against a buying committee that doesn’t exist, for the length of an entire sales cycle, without anyone downstream knowing why the pipeline never showed up. That’s the specific reason B2B can afford to pay more for confidence in a way that high-volume, single-decision-maker B2C targeting never has to.

Reach graph, or proof graph

Put the two layers together properly and you end up with one of two things, and it’s worth being honest with yourself about which one you’ve actually got.

A reach graph is what you get when the identity layer is doing all the work and the account layer is thin or borrowed. It covers a lot of ground, matches on almost anything, and looks identical to a properly built account graph on a coverage slide. It behaves nothing like one in a live campaign mapped to outcomes, because a device resolved cleanly to the wrong node in a business structure that was never properly mapped is still a wasted impression, just a confidently wasted one.

A proof graph is what you get when both layers are built deliberately and cross-checked against each other before any money moves. It costs more, because enrichment partnerships and hierarchy mapping are expensive and corroboration takes real engineering, not just a bigger identifier database. It also wastes considerably less of the budget sitting on top of it, for exactly the reason the stakeholder numbers above make concrete: in B2B, the cost of confidently targeting the wrong node in the structure compounds over months, not minutes.

A bit of history, since it’s relevant: the last time the industry got genuinely excited about “identity graphs” as a category was 2016-18, when Drawbridge, Tapad, and LiveRamp were the names everyone name-dropped. That entire generation solved cross-device identity for a single consumer, one person across several screens. It never had to solve for a holding company with forty subsidiaries and a buying committee spread across three of them. The vocabulary carried over into B2B. The problem it was actually built for didn’t.

Field Notes

Two data points worth sitting with, both landing within the last year and both pointing the same direction.

A December 2025 DemandScience survey of 750 senior B2B marketing leaders found that the large majority believe their own intent signals are unreliable or inflated, and only a minority of those signals convert into qualified pipeline at all. That’s not a data-availability problem. Every one of those organisations had plenty of signal. What they didn’t have was a business structure solid enough to tell them which of it to trust.

Separately, a 2026 industry benchmarking piece on B2B identification vendors made a point worth repeating precisely because it’s an admission rather than a claim: no independently audited accuracy benchmark exists across visitor-identification vendors. Every accuracy number in the category is vendor self-reported, on the identity layer alone, with no mention of whether that identity was ever resolved onto a correctly structured account in the first place.

So: when you next hear someone describe their “account graph,” ask them a more specific question. Are they describing a business structure, correctly mapped, with identity resolved onto it and cross-checked? Or are they describing an identity graph with a nicer name? Subscribe to the B2B Stack, and I’ll keep outlining how this industry works, and the key questions to ask

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Disagree that the two are worth separating this sharply? Tell me why in the comments, I’d genuinely rather have that argument than not!

Tighten the dark patterns, ban the pre-ticked boxes, make reject as easy as accept, and the mechanism does its job. I don’t think that’s right. I think the mechanism is the problem, and I will get into why today.

Before I do, if this is all sounding a bit alien, this is what we are talking about - a European problem which has been exported to our friends in North America.

Ten years of trying to put users in control of their data has done real, measurable damage to the thing that data was meant to fund, and to the users it was meant to protect.

Now it’s colliding with an AI-driven internet nobody designed it for. This is the law of unintended consequences playing out at internet scale. The clearest proof is that the EU push to unwind its own law is coming from people who built it.

What you’ll learn in this piece:

• Why the EU is trying to dismantle a consent architecture it only finished rolling out a few years ago

• What a decade of “click yes” banners actually did to publisher revenue, user trust and user comprehension, at the same time

• Why hundreds of vendors sitting behind one banner broke understanding rather than building it

• Why swapping the banner for a browser-level toggle doesn’t fix the underlying economics, it just moves who controls it

• What’s really being exchanged when you click accept, and why the fear-based framing outruns the actual risk more often than not

• Why AI’s zero-click search makes this the worst possible moment to strip publisher monetisation further

• What an architecture built for today’s internet, rather than 2002’s, would actually need to do

You didn’t design ‘cookie’ consent this way. Nobody did.

I have quoted cookie because this extends far beyond a mechanism which itself has all but died a death. This is not just cookies, its essentially all tracking.

With that caveat out of the way, the legal root of the banner sitting on every site you visit is Article 5(3) of the ePrivacy Directive, EU law from 2002, before smartphones existed. It said, reasonably, that storing or reading information on someone’s device needs their consent first. GDPR arrived in 2018 and gave that requirement teeth.

Neither piece of legislation mandated the thing you actually experience today: a full-screen modal, a tabbed settings panel, a scroll of toggles for vendors you’ve never heard of. How is an average user of the internet ever going to make head nor sense of that?

That modal UX got built by the market responding to liability, not by regulators specifying a UI.

Consent Management Platforms scaled up. The IAB’s Transparency and Consent Framework standardised the plumbing and then got pulled over the coals for being illegal anyway, all of which points to a massive mess.

The Global Vendor List that sits behind most of those banners now runs past 1,200 registered vendors, ad servers, measurement providers, DSPs, SSPs and agencies, most of them invisible to the person clicking through. Somewhere in that scaling, a lightweight consent requirement turned into an entire industry of consent engineering, complete with its own dark-pattern arms race - pre-ticked defaults, reject buttons buried three taps deep, copy written specifically to nudge you toward “accept all” because that’s the outcome that keeps the lights on.

Nobody sat down and designed this. It’s what you get when a simple legal requirement meets a commercial incentive to maximise consent rates, repeated across millions of sites for a decade.

This is all a nuance of web based advertising, and noise which largely gets worked around within social ‘walled gardens’. This led to the de-funding of the open web, which reflects a pivotal moment in the defunding of quality journalism in favour of the proliferation in AI slop which has filled these walled gardens.

B2B vendor investment measured against attention time has never been weaker in the open editorial internet, and never been stronger then it is in these sloppy garden waters, all of which represents a missed opportunity - and these privacy laws have to take their share of the blame.

Three unintended consequences of one messy law

Here’s where I part with a lot of the privacy commentary on this.

The usual framing is that cookie banners are annoying but at least they’re protecting you. I’d argue they’re failing on all three fronts they were meant to help, simultaneously, for the same underlying reason: nobody priced in the value exchange they were regulating.

Publisher monetisation. Every rejected consent is inventory a publisher can’t monetise properly, on content they gave you for free. The consent banner sits between the reader and the business model that funds the article. For a decade that’s been treated as an acceptable cost of privacy protection. It isn’t. It’s a consent tax on the one business model that keeps the internet open rather than paywalled, and publishers have been paying it quietly for years. This defunds journalism and forces publishers down routes that jeopardise their real part in creating the value of the Internet - forcing behaviours like deploying more AI slop to replace the human crafted word because the latter isn’t economical anymore, and cramming more crappy user experience ads onto the page because they add a few precious cents of revenue to pay the bills.

User comprehension. Open a serious news site’s cookie panel and you’ll find a vendor list running into the hundreds, most of them companies you’ve never heard of and have no way to evaluate. That isn’t transparency. Transparency means understanding what you’re agreeing to. A four-figure list of unfamiliar businesses does the opposite. It manufactures distrust of a data exchange most users don’t have the context to assess, and it asks for that decision in the ten seconds before someone actually wants to read the article they clicked on. When you pull this apart, it borders on ludicrous. If I am trying to read a quick piece of industry news that my colleague has shared, in-amongst doing 101 other things, in what world am I going to inspect what sits behind a cookie banner? and if the alternative to clicking ‘accept’ is either being blocked from reading the news or having to pay, what choice am I really exercising? this is consent theatre and I call BS on it.

User experience. The annoyance is well documented and doesn’t need much defending. What’s less discussed is what that annoyance trains people to do: click accept without reading anything, on autopilot, which is the exact opposite of informed consent. A mechanism built to create meaningful choice has trained an entire generation of internet users to treat the choice as friction to clear as fast as possible.

Three failures, one root cause. The law tried to solve a data-literacy and market-structure problem with a consent-click, and a consent-click was never going to carry that weight.

Brussels wants to undo its own law now

This is the part that should give the “just needs better implementation” crowd pause.

The European Commission’s Digital Omnibus proposal, working through Parliament this year, aims explicitly at reducing cookie banner frequency and shifting consent toward centralised, one-click preferences set once at browser or OS level.

Privacy NGOs have gone further, with a “Kill the Cookie Banner” campaign pushing for exactly that outcome.

Google’s submission to the consultation argued for deleting the relevant provision outright, on the grounds that keeping it anchors the reform to what it called a proven-failed architecture. Meta went further still, arguing for scrapping Article 5(3) itself, the clause that requires consent before storing information on a device in the first place. I certainly don’t always agree with big tech and its big personal agendas, but they are right. We are now in danger of tweaking something that is fully not fit for purpose.

Read that lineup again. The regulator that built this, the privacy advocates who fought for it, and the platforms who profit from it are all now pointing at the same mechanism and saying it doesn’t work. They disagree fiercely about what should replace it, and the reform is reportedly getting bogged down in exactly that disagreement. But the premise that the current banner model has failed is close to consensus. Ten years after GDPR was supposed to have settled this, that’s a remarkable place to end up.

The strongest privacy argument, and where it still misses

I want to give the other side its due here, because waving away every consent requirement as bureaucratic overreach is its own kind of unintended-consequences trap.

The strongest version of the privacy case isn’t about banners at all. It’s about “pay or consent” models, where sites (Meta being the highest-profile example) offer a binary choice: accept tracking, or pay to avoid it. The European Commission fined Meta €200 million in 2025 for this, on the grounds that a subscription fee steep enough to be unrealistic isn’t a genuine alternative, it’s coercion wearing a choice’s clothing. The privacy zealots are still missing the point that any business, whether it is a publisher or a social media platform, has the right to charge money to use their services. This ingrained ‘the internet is free model’ is naive and these are big businesses. If their preferred model is advertising funded then they can bias their charging models towards that outcome, just like a shop can reduce the price on red sneakers to try and sell them over their green counterparts.

BEUC, the EU’s largest consumer group, says Meta’s revised model still doesn’t offer a fair one. Fair critique, and a different problem to the one I’m describing: it’s about power asymmetry between a gatekeeper and a user with no real second option, not about whether consent-by-click is the right mechanism in general.

Where the argument overreaches, in my view, is assuming the fix is more consent infrastructure rather than less. Shift the “consent moment” to the browser or the OS, which is the direction both the Digital Omnibus and the anti-banner campaigners are pushing toward, and the coercion problem hasn’t gone anywhere. It’s just moved. The problem this brings is the chance for the user to make one decision, going through the few extra clicks to select reject because it is a single ‘set and forget’ that then switches off monetisation for publishers. This will push more and more to charge, and that blocks content access for many users who are unable to afford it. And it goes against the model that publishers have been happy to extend since 1993 - offering their content free on an ad funded basis. I don’t see the losers in this model.

Additionally, if the browser contains universal consent, you’ve handed the decision to whichever company controls the browser or the device, which today means Google or Apple almost by default. That isn’t a neutral piece of infrastructure letting a technical signal travel with the user. It’s consolidating the exact leverage everyone’s worried about into two companies that often behave like monopolists, instead of distributing the friction across the open web. Fixing pay-or-consent coercion by centralising consent into a gatekeeper’s settings menu solves one power problem by creating a bigger one. Who is to say Google, for example, couldn’t or wouldn’t leverage that to push more of their own services. Their zero click search is being leveraged to retain users within Google, and distributing about 2% of the clicks that their original model distributed.

This is roughly where I land on browser-level consent generally: worth considering, dangerous if it’s the only lever. If you want the fuller argument for why, that’s a subscriber-only follow-up I’m working on now.

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What you’re actually agreeing to, probably

Strip away the fear-mongering language most banners are written in, and the actual data handshake behind most “accept all” clicks is anonymised, segmented browsing behaviour used to serve more relevant ads. Not a file with your name and address in it. Not something saying Mike has been buying xyz and viewing these websites and looking at these health concerns. Just a random alpha-numeric identifier used in aggregate to pick up interests on a topic level to tie back to more relevant advertising. That’s meaningfully different to what the four-figure vendor list and the ominous copy imply.

I’ll hedge this properly rather than wave it away. There are legitimate re-identification risks when enough anonymised signals get combined across data brokers, particularly at scale and particularly with location data. “Anonymised” isn’t a synonym for “no risk.” That’s a real argument and it deserves better regulation than a click-through banner gives it. But the banner as currently built doesn’t communicate that nuanced risk either. It communicates blanket, undifferentiated alarm across every vendor on the list, whether that vendor holds sensitive location history or an anonymised interest segment used once to decide which ad to show. Undifferentiated fear isn’t better data literacy than no information at all. In some ways it’s worse, because it looks like transparency while functioning as noise.

This is the worst possible moment to make monetisation harder

Set the consent debate against what’s happening to publisher traffic right now and the timing gets worse, not better.

Chartbeat data cited in the Reuters Institute’s 2026 predictions report found publisher referral traffic from Google organic search down by roughly a third globally over the past year, driven by AI Overviews answering queries directly on the results page instead of sending users onward. Zero-click behaviour on AI search products like ChatGPT Search, Perplexity and Google’s AI Mode runs far higher again. ChatGPT’s outbound referrals to publishers have grown, but industry reporting is consistent that the growth isn’t close to offsetting what’s being lost elsewhere.

So where does this leave real journalism?

Publishers are watching two structural forces hit at once. A legal architecture restricting how they monetise the readers who do show up. A search and AI ecosystem sending them dramatically fewer readers to monetise in the first place, while training its models on the content those publishers produce.

Neither force was designed with the other in mind, because neither existed when the other was built. The ePrivacy Directive is 2002 legislation for a pre-smartphone internet. AI Overviews are technology from an internet nobody in 2002 could have modelled.

Layer today’s consent architecture on top of today’s search behaviour and expect the publisher economics to hold up, and you get the same mistake stacking on top of the first one. Call it the consent tax, round two.

What actually needs to change

None of this is an argument for scrapping consent altogether, and it’s not an argument that the open, free-to-read internet we’ve had since 1993 doesn’t deserve protecting. It’s the opposite. Most users, I believe, if the trade-off were ever explained to them properly, would choose that open internet over a pay-walled alternative. Protecting it means changing the mechanism, not just its cosmetics. All the consent leavers are find until it comes at an actual cost to you, and who in 2026 is feeling flush enough to start splashing the cash on buying more subscriptions?

Three things, in order of how tractable they are.

First: real technical enforcement of what’s already promised, blocking data flows before consent rather than after, rather than the cosmetic layer most CMPs currently provide.

Second: whatever replaces the current banner needs a genuine competition safeguard built in from day one, not bolted on afterwards, so “simplify consent” doesn’t become “hand two platforms the keys.”

Third, and this is the one nobody in the current Brussels fight is really discussing: publishers need an actual value-exchange model that doesn’t depend on a four-figure vendor list nobody reads. Fewer, better-understood data relationships tied to a specific, explainable exchange, rather than the current approach of maximising the number of parties behind one blanket toggle.

That third point is close to the thesis I’d argue professionally, not just as a newsletter opinion: the industry needs infrastructure built around a small number of legible, accountable data relationships, not thousands of anonymous ones hiding behind a single click. If you’re building on the buy or sell side and want to talk through what that looks like in practice, drop me a line at mike@funnelfuel.io.

Killing the cookie banner without fixing what it was trying to do won’t save the open internet. It’ll just move the consent tax somewhere less visible, and hand publishers a second structural headwind at the exact moment AI is already collecting on the first one.

Where do you land: is a browser-level, one-click consent signal actually a fix, or just a more efficient version of the same problem?

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I’ve got a personal stake in saying this. I built PowerLinks around native, B2B and contextual adtech, back when the whole industry was trying to solve banner fatigue.

The OG premise was strong. The internet has morphed fast from desktop devices to mobiles, and banner ads didn’t really fit on mobile sized screens. Web pages, across both desktop and mobile were increasingly getting splashed in prodigious volumes all over the pages, causing breaking, ads covering content and the proliferation of video formats, many of which interrupted the content - was all driving users away. Pages were loading slow, and the web was starting to look like a dumpster truck of competing ‘impact formats’.

Native promised something different. Where display ads won attention by standing out, native ads won attention by blending in. Where display ads were typically only aligned to users by audience signals (think retargeting), native had a greater requirement to align more contextually to the topic of the page because the ad looked like any other story in the publishers news feed. Native also stood to align the approach taken on social platforms like Facebook and Linkedin - ads that looked like any other posts, using titles, images or videos and other assets that populated to look like they belonged. They were, and are, hard to visually skip.

A lot of these points remain as true today as they were around 2015. V1 didn’t scale but V2 just might.

I personally stepped out of native specifically around 2020, when I went all in on B2B and broader programmatic through FunnelFuel. Watching it come back, this time with account-level signal underneath it, is the closest thing adtech gets to a sequel that’s actually better than the original. This is personally interesting, and maybe this format can be weaponised properly this time around with some next gen thinking - which I will dive into below

What you’ll learn in this piece:

• What native advertising actually is, and the real difference between advertorial-style native and programmatic in-feed native

• Why native faded from view after its first wave, and what’s changed structurally since

• Why B2B specifically is the environment where signal-aware native has the best shot at working

• How an account graph turns a generic native placement into an account-targeted one, and what that requires technically

• How to measure it properly, and where it fits alongside rich media display rather than instead of it

WTF is “native advertising”?

Native advertising is any ad format built to match the look, feel, and function of the environment it sits in, rather than interrupting it. That’s the whole definition. Everything else is a variation on how far that blending goes and believe me, people in native will spend plenty of time arguing over that.

below is an example of a native in-feed format, ringed in red. Notice how the shape and size of the image, the colours and fonts, and general website styling is broadly adhered to. In order to really highlight the value, did you even notice the 300x250 MPU in the top right hand corner and call out it is a Dell ad? if not, that is kinda the point here.

There are two broad versions worth separating, because B2B marketers tend to conflate them.

Advertorial-style native is editorial-shaped sponsored content: a branded article, a “presented by” feature, something written to read like the publication’s own journalism. It’s bought directly with a publisher, it’s slow to produce, and it doesn’t scale past a handful of placements at a time. It has genuine value for brand storytelling with a specific title or publication, but it’s not a media buying strategy. IMO, it is best used to seed premium memory building content and to ensure you have amplification rights - because this stuff gets really powerful when you can promote the story. Think about that above example on Entrepreneur. The advertiser is PayPal for business. PayPal could promote that story around how to attract more customers using Entrepreneur magazine as the advertiser, driving reads within their premium website. The win is hijacking trust in a way that a banner click to a vendor site can’t replicate

Programmatic in-feed native is the format I care about, because it’s the one with scale and signal. These are ad units auctioned through exchanges like TripleLift and StackAdapt, served through a DSP, and designed to sit inside a content feed, a recommendation widget, or an article layout as a component: image, headline, short description, and a call to action, styled to match the host page rather than sitting on top of it as a foreign object.

The distinction matters because only the second version can be targeted, measured, and scaled the way a B2B media plan needs it to be.

Why native faded, and why that’s the wrong lesson to take from it

Native’s first wave was a response to banner blindness. Standard display had raced CPMs toward the bottom, and readers had learned to stop seeing bright, boxed-in ad units entirely. Native’s pitch was the opposite of standard display’s logic: instead of standing out through size and motion, stand out by blending in. A well-built native unit, with the right image and title working together, earns a moment of attention that a banner in the sidebar never gets a chance at. Good creative can then rapidly anchor brand memory to context - which is psychologically compelling.

Native didn’t replace display, and it was never going to. What actually happened is more interesting: display was built around audience targeting, following a person across sites based on who they were assumed to be, while native’s contextual roots meant it was better suited to matching content, not chasing identity. When the entire industry’s targeting foundation started shifting away from identity (think the death of the cookie and the growing net being cast by privacy regs globally), native’s original strength turned out to be the one built for what came next.

That shift is not theoretical anymore. More than three-quarters of global internet traffic now runs through environments where third-party cookies are limited or unavailable, and behavioural targeting depends on signals that are disappearing faster than most budgets have adjusted to. Google’s own reversal on Chrome cookie deprecation hasn’t changed the practical picture much: Safari and Firefox already block third-party cookies by default, and Apple’s App Tracking Transparency framework has already collapsed most mobile-side signal availability. Contextual targeting isn’t the fallback option anymore. For a growing share of the open web, it’s the only option that still works cleanly.

Why B2B is the environment where this actually lands

B2B buying happens inside trusted, researched environments. And yes this still remains the case despite the role of AI, which is very real and is taking audiences away, but it misses the point that great volumes of audiences remain in these environments today.

Buying committee members read trade press, analyst content, and industry newsletters as part of doing their job, not as leisure browsing. That’s a fundamentally different attention state than a consumer scrolling a feed, and it’s exactly the state native advertising was built to work inside: matching an ad’s tone and placement to content the reader already trusts and is actively engaged with.

Pair that with where B2B buying decisions actually get made. Most of the real research and internal debate happens in what’s often called the dark funnel: research reports, peer conversations, internal Slack threads, none of it visible to a vendor’s tracking.

A component of the dark funnel, which is both real and often not referenced, is the web browsing that happens in cookie-killed web environments and within trusted trade press. If you rely on signal alone, you miss it, if you lean on native with contextual targeting dialled up, you can win those placements. If your ad gets clicked and drives through to a trackable page, that your website, then signal exhaust is now generated - and native ads drive at least double the CTR of banners. So native becomes a compelling way to capture signal out of hard to capture environments, like cookie-less display and to resolve the account on your website.

An account showing up in a trusted editorial environment, reading content adjacent to a category you sell into, is one of the few observable moments where dark funnel activity briefly surfaces - and that has to be the best moment to expose that buying committee member to your brand and to work to win a click to your website. Serve a native ad into that moment well, and it reinforces intent that’s already forming rather than trying to manufacture it from nothing. Serve a loud banner into it instead, and you interrupt exactly the research behaviour you should be trying to support and the risk is the ad goes unseen. Serve both - well targeted display ads using placementID to find the good actors and to avoid buying disruptive garbage ads AND at the same time serve native ads, all against the same signal, and you get the ultimate in in my opinion. This is next level programmatic thinking - using signals like placementID to find the great ad slots, using different formats with different objectives to dial up the brand memory and impact we can make on the researcher, aiming to drive dark signals from context into ones we can infer through website traffic acquisition. This is the only time I argue in-favour of aiming to drive CTR. Everything has a time and a place.

The part native was missing: knowing which account you’re actually in front of

Here’s the honest gap in native’s original pitch. Contextual alignment tells you the page is relevant. It doesn’t tell you whether the specific business reading that page is one you actually want to reach. A contextually perfect placement served to the wrong company is still wasted spend.

This is where an account graph earns its place in the stack, and it’s the piece most B2B advertisers haven’t connected to native specifically. To illustrate in a real example; FunnelFuel’s (B2B programmatic specialists) graph maps 75 million companies across 370 million business locations globally, resolved against up to 300 attributes per account and stitched together from all the major signals across cookie and cookieless techniques; hashed email signals, network-level IP resolution, and hyperlocal contextual data. Layer that onto a native placement and the targeting question changes from “is this page relevant” to “is this specific account, at this specific moment, in a content environment adjacent to what we sell.”

The technical piece that makes this work in practice is the ad server itself. When I designed one of these, I aimed to index a placement’s characteristics on the page it’s served against: the surrounding CSS, the colour palette, the typography, the tone of the surrounding copy. Standard display creative then gets adapted to take on native’s ‘visual grammar’ for that specific placement, rather than every advertiser needing a bespoke native unit hand-built for every publisher. That’s what lets native scale as a programmatic buy instead of staying a boutique, publisher-by-publisher exercise.

Measuring it properly, and where it fits next to rich media

The upside is real, but it needs the right measurement frame or it gets dismissed as a vanity format. Click-through rate on native in-feed units consistently outperforms standard banner display in B2B environments, largely because the format earns a moment of genuine attention rather than being scrolled past on reflex. Attention time, not just clicks, is the metric worth tracking here: a native unit that holds a reader’s eye for two extra seconds inside a trusted editorial context is doing something a banner impression can’t claim credit for. Attention metrics were not even a thing when native launched, so joining the dots to link account level attention to native ad exposure, alongside clicks, is logical and exciting in equal measure.

As I reflect on native ads today, with much less of a vested interest then I did a decade ago, my framing to you would be that you should think about native as a signal layer, not a standalone channel. Combine it with account-level intent data and high-value-action tracking, and native becomes part of how you influence an account already in motion, not the thing generating that motion on its own. Judged against last-click conversion alone, it will always look weaker than it actually is.

Rich media display still has a place, and I’d argue the strongest B2B media plans in 2026 run both together rather than picking one. Rich media earns impact through scale and format, big expandable units in ad real estate that was never built for native conversion in the first place. Native earns relevance through contextual and account fit, served inside feed placements where a large expandable unit would feel like an intrusion. Run them in parallel and you get memory-building impact from one format and trusted, in-context relevance from the other, rather than treating it as a choice between them.

If I was a B2B advertiser in 2026, how would I run it?

If you’re briefing a DSP, agency or managed service partner like FunnelFuel to run native this year, the question worth asking directly is whether they can accept component-based creative, meaning image, headline, and description supplied as separate elements rather than one fixed banner asset, so the placement can render it in the native style specific to that page. If the answer is no, you’re leaving in-feed inventory on the table that your competitors’ media plans are already picking up. I’d also want to know what signals they can leverage for native, and if they have any workflows like bespoke ad serving to bring native principals into my display ads.

That last point is huge, and should be re-surfaced. I’d be mandating a native-first display ad approach in 2026, because they are proven to win more attention. They don’t cost more yet they drive more attention and action - so why wouldn’t you want that? I’d be asking for the ability to deliver genuine native experiences into the right placementIDs for a native approach, and then synergising with big rich media formats to combine the best of both worlds. I’d be asking how this can uncover dark funnel research and cast a light on some of it, whilst impacting more through well placed exposure

Native advertising isn’t back because it’s nostalgic. It’s back because it was built around content relevance rather than identity tracking, at exactly the moment identity tracking stopped being reliable, and because B2B buyers already spend their research time in the trusted editorial environments this format was designed to sit inside. The question isn’t whether native belongs in a 2026 B2B media plan. It’s whether your current stack can serve it against the right account, or whether it’s still just guessing at the page.

Are you running native as an account-targeted signal layer, or still treating it as content-farm leftovers from 2016?

If any part of this doesn’t square with how your stack currently handles in-feed inventory, reply to this email. I read every one. If you want to talk through what account-graph-targeted native could look like for a specific campaign, reach me directly at mike@funnelfuel.io.

What you’ll learn in this article:

• Why UK and EU privacy law are quietly pulling apart for the first time since GDPR, and what that means for addressability on each side of the Channel

• Why the US isn’t “looser” than Europe, it runs on an entirely different consent architecture, and why the difference is more significant than most vendors let on

• Whether the old floor trader’s instinct, hashed email in the US, clusters and personas in Europe, actually holds up against the evidence

• Why Singapore tops the global AI adoption tables while running a stricter consent regime than most of the region, and why those two facts sit together perfectly well

• What high-context and low-context culture actually means for B2B messaging and campaign sequencing, and why the “one deck, three markets” approach still quietly underperforms in 2026

The UK and the rest of Europe used to be one privacy story. They aren’t any more.

For most of my career, “European privacy law” has meant one thing. GDPR plus the ePrivacy Directive, enforced with local flavour but pointed in the same direction everywhere. I watched that direction get set in 2018, when GDPR went live and half the industry spent that May frantically re-papering consent flows they’d been ignoring for a decade. It caused carnage in agency land and a wave of premature in-housing happened to manage legal workflows at the cost of client performance. I watched it harden further when the Belgian regulator went after the IAB’s own Transparency and Consent Framework and the CJEU backed them up in 2023, which told every publisher and every DSP that the industry’s own self-built consent plumbing wasn’t going to save anyone. Reject the banner, and that was that, wherever you were standing.

That assumption is now out of date, and it’s the UK that’s broken the pattern.

Since February 2026, UK cookie law has been changing shape under the Data (Use and Access) Act. New categories of storage and access technology no longer require consent at all, and the ICO has gone further than that. It’s signalled openly that it intends to submit evidence to government on which advertising activities could earn a statutory exemption from consent, provided the privacy risk is genuinely low. This is something I have been privately advocating for yonks, and especially in B2B where the targeting currency becomes ‘the account’, with measurement mapped to that aggregation of people in an office; I genuinely think B2B sits squarely in that very low risk category.

The regulator’s own finalised guidance makes an argument I’ve been making in agency pitch rooms for a decade without much luck: treating a cross-site behavioural profile and a single contextual ad as the same category of risk was always a very blunt instrument. That’s the ground I covered in the piece on PECR reform a couple of months back, and nothing’s actually switched on yet, advertising still needs consent in the UK today. But the regulator is the one holding the door open, which is a genuinely new posture for a UK data authority to take. For the first time in a generation the privacy direction of travel has changed, and I really do think that is positive. The wider ecosystem of an ad-funded internet enabled by addressable advertising is what has kept the ‘web free since ninety-three’ as OG adtechers like to say, and that privacy cost has been blown out of all proportion. The ad ecosystem isn’t looking at bank account numbers and personal phone numbers, its pseudo anonymous identifiers that map back to signals like which business you may work for or what news story you were reading in which region of which country.

Before we get excited and think the whole continent of Europe, of which the UK has always had one foot in and probably a bit more than that out since Brexit in 2016, they are in-fact on another track entirely. The EU has gone the other way.

The long-stalled ePrivacy Regulation, which had been in negotiation since I was still cutting my teeth on RTB, was formally withdrawn in February 2025. Rather than loosening anything, the Commission’s Digital Omnibus package folds cookie consent straight into GDPR through two new articles. Article 88a restricts how often a site can re-ask for consent after someone’s said no. Article 88b will eventually make browser-level consent signals legally binding on every controller, which hands more power to the rejection, not less. And enforcement hasn’t slowed down to match the political mood. The French regulator fined Google and Shein a combined €475 million in a single day in September 2025 for cookie violations, and Brussels separately fined Meta €200 million over its consent-or-pay model.

So for the first time since GDPR landed, “Europe” isn’t one privacy regime moving in one direction. The UK’s exploring a risk-based exemption for low-risk advertising. The EU’s consolidating and tightening. Anyone running the same programme across the UK and the rest of the continent on the assumption that the compliance posture is identical is working off an assumption with an expiry date, and that date is this year, not some hypothetical future one.

All of this is explored in my ultimate guide to B2B programmatic advertising in 2026

The US was never “looser.” It’s built on a different foundation entirely.

Every European media buyer I’ve ever trained has walked into their first US campaign with the same instinct: this market’s simpler, less regulated, more room to move. I had exactly that instinct myself the first time I ran budget into the US, many years ago, and it’s not quite right, and getting it wrong costs you.

The UK and EU run on opt-in. Nothing gets stored on a device for advertising until the user actively says yes, and the default state is off. That’s true whether you’re dealing with the original 2011 UK implementation of the EU cookie directive, which is the piece of regulation that actually started the banner era before GDPR existed, or the version we’re living under now. The US runs on opt-out, and it still does even in its most regulated states. As of March 2026, no US state requires affirmative consent before a cookie is placed. The twenty states with comprehensive privacy laws, California’s CCPA/CPRA leading the pack, give consumers a right to opt out of targeted advertising and data sales after the fact. The default is collection. The user has to go and switch it off, and almost nobody does. That’s not a looser version of the European rule. It’s a different rule built on a different premise about who has to do the work.

That single structural difference is why individual-level, device-level signal has persisted at far greater scale in the US by default, and it’s exactly why deterministic identity, hashed email matched to mobile advertising ID, grew up there as a discipline in the first place. This manifests into being the go-to identity tactic for North American adtech businesses and about the last tactic to try for a European business. These legal norms determine the entire approach to identity that European versus North American adtech businesses take, which is an interesting nuance which probably often gets forgotten.

I strongly believe that any B2B vendor with global advertising ambitions are better working with a European adtech business, precisely because they have had to adopt the widest range of tactics to build deep addressability. The combined scale of the US market combined with the default to ‘on’ for data collection makes everything easier - and a rude awakening when US businesses come across the pond and collide with Europe’s default to ‘off'‘. As a UK based founder, I am bias, but I do think there’s validity in this argument.

I remember the identity graph wars properly kicking off around 2016 to 2018, Drawbridge and Tapad slugging it out over cross-device matching, LiveRamp building what eventually became RampID out of the old Acxiom onboarding business, and all of it assuming a US-style ocean of persistent, matchable signal to work with. Then GDPR hit in 2018 and a huge amount of that infrastructure simply didn’t translate to European traffic, not because the vendors got worse at their jobs, but because the underlying signal they needed to match against had been legislated into scarcity on this side of the Atlantic. Many packed their bags and left the UK/EU market entirely.

One identity vendor is blunt about what happens when a US-built graph gets pointed at non-US traffic: match rates fall off a cliff, because the hashed-email-to-device associations that make the whole thing work were never built at density outside their home market. It isn’t purely a regulatory story. It’s a data-density story too, and the two feed each other. Roqad, one of the European identity players, is upfront that it exists specifically because the US-style deterministic model doesn’t fit the region it operates in, which tells you the industry itself has already made this call, it’s just rarely said out loud to the client.

The old trading-floor instinct, hashed email in the US, clusters in Europe, mostly holds up

I’ve heard some version of this from nearly every experienced trader I’ve worked with. Individual-level resolution in the US, aggregate towards persona and account clusters in Europe. I went in wanting to test it rather than just repeat it, and it holds up better than I expected, with one real gap.

The mechanism runs in both directions at once, which is what makes it durable rather than coincidental. Regulation constrains what can be collected and how long it can be kept, and pushes European and UK activation towards contextual and account-level signal rather than individual identifiers, and that’s been true since 2018 and is about to get more true again as the EU tightens further through the Digital Omnibus. But underneath the regulation, the raw identity plumbing is thinner in Europe regardless of what the law technically permits, because the panel data and the deterministic HEM-to-MAID (matching hashed email signal to targetable device level signals) pairings that make US-style resolution work were built in the US first and were never rebuilt at the same density elsewhere.

Bombora’s own account of how B2B identity resolution actually works is a useful tell here. It leans on a blend of deterministic hashed business emails, IP-to-domain matching, SSO data and a proprietary work-from-home probabilistic model, precisely because no single deterministic method reaches workable B2B coverage on its own, and I’d bet money that blend leans harder on the probabilistic side the further you get from North America. This approach has led to Bombora re-gaining ground in the EU where their US cousins have lost it.

The gap, and I want to be straight about this rather than pretend I found something I didn’t, is Singapore and the wider APAC region. There’s a reasonable amount written comparing US and European identity coverage. There’s almost nothing written with any rigour about Singapore-specific match rates or graph density, and that silence is itself informative. It tells me the industry hasn’t scrutinised APAC identity infrastructure with anything like the attention it’s given the transatlantic comparison, and anyone who tells you with confidence what B2B match rates look like in Singapore today is very likely extrapolating from the US or European picture rather than working from actual regional data. Given we’ve got a genuine Singapore desk, that’s arguably a more useful place to point our own delivery data than re-proving the US-Europe split, which the wider industry has already covered reasonably well. All of this is exasperated by the markets focus on demand and direct response tactics like content syndication.

Singapore is the AI adoption story I didn’t see coming - and this directly impacts INTENT data capture

If you’d asked me a year ago to guess which market leads the world on AI usage intensity, I would not have said Singapore, and I’d have been wrong more than once, because it isn’t a fluke ranking. Anthropic’s own Economic Index put Singapore second globally behind Israel on usage relative to working-age population back in September 2025, and by the March 2026 update, Singapore had moved into first place outright. This is important because of the dark funnel and the fact AI is starting 51% of all B2B buying journeys now and is a part of upwards of 92-94% of them - which is explored in detail in the below piece around steering the dark funnel

The Singapore ranking is interesting. The reason behind it is more useful, and it maps directly onto how B2B buying decisions actually get made in that market. Forrester’s regional AI research found that in APAC, 33% of enterprises put the CEO in direct ownership of AI strategy, against 18% in North America and just 8% in Europe. APAC firms also back that ownership with real budget: 26% invest between $400,000 and $500,000 in generative AI, against 19% in North America and 17% in Europe.

Forrester’s own read is that European firms operate under tighter regulation and stronger labour protections, which pushes their AI posture towards governance rather than speed, while APAC firms are competing in faster-growing markets where speed itself is the edge, and they’re prepared to fund that with capital and put it directly under the CEO rather than diffusing it across a committee. This is a fasinating global split in attitude, pace of market and general approach

If you’re advertising to, or selling into, B2B audiences in Singapore, that’s not a trivia point. It means you’re more often talking to a buying committee where AI-adjacent purchasing decisions sit close to the top, and where the appetite to move on a new platform or data partnership is structurally higher than it is in the UK or the rest of Europe. That’s a different sales motion, not just a bigger TAM.

Its also worth considering the impact on intent data - which is often largely formed out of observable behaviour in publishers and vendor websites. If their market is driven more by AI, which gives off no exhaust fumes and they mainly focus on content syndication, which captures data off-domain (not on the vendors core-site) then this market is more limited in its ability to capture intent, period.

It’s worth setting that against the UK’s own mood, which sits at the opposite end of the same spectrum. A UK and Ireland survey of 277 B2B marketing leaders, published in January 2026, described the finding in its own title as a sense of hesitancy: strong budget growth and near-universal tool adoption sitting right alongside real reluctance to hand AI anything genuinely strategic. That isn’t a contradiction of the Singapore story. It’s the other end of the exact spectrum Forrester’s ownership data describes, and if you’re running one global content calendar across both audiences, you’re pitching confidence to one room and caution to the other, and most decks I’ve reviewed don’t seem to know that.

Messaging doesn’t travel as cleanly as most global campaigns assume, and I learned this the expensive way

This last one is less about infrastructure and more about the actual words on the ad, which ties to general advertising tactics, and it’s the mistake I’ve made personally, more than once, before I knew better.

Years ago I ran a campaign built for a UK and US audience out into an APAC launch with nothing more than a language swap. Same structure, same direct ask up front, same “here’s the number, here’s the CTA” cadence that had worked perfectly well at home. It underperformed badly, and for a while I assumed it was a targeting problem, because that’s always the first thing you check. It wasn’t a targeting problem. It was a message architecture problem, and the anthropologist Edward Hall had already explained why decades before I ran into it, if I’d bothered to read him sooner.

Hall’s distinction between high-context and low-context cultures still holds up as a genuinely useful practical filter for B2B messaging. The UK, the US, Germany and the Netherlands are consistently cited as low-context markets that respond to direct messaging and explicit value propositions: state the claim, back it with a number, close with an ask.

Much of APAC runs on the opposite instinct, where relationship and shared understanding carry more of the message than the literal copy does, which is exactly why B2B campaign sequencing across the region so often opens with thought leadership or a webinar to build trust before anything resembling a direct lead-gen ask ever appears. One localisation specialist puts it about as plainly as I’ve seen it put: North American programmes can lead with a direct lead-gen ad from day one, while APAC programmes typically need the trust-building content to run first, or the direct ask simply doesn’t land, which is precisely what happened to me.

Singapore sits in an odd middle position here, English-speaking and commercially direct by regional standards, but still embedded in a higher-context region, which is probably why so much generic “APAC” creative guidance doesn’t fit it especially well either. Honestly, this is another spot where our own delivery data could add something the wider commentary hasn’t nailed down.

What this actually means if you’re running an account-based programme across two or more of these markets

Pull the threads together and a handful of practical calls fall out fairly cleanly.

Treating “Europe” as a single addressability zone is no longer safe. The UK and the EU are diverging, and a programme built on the assumption of identical consent posture on both sides of the Channel is going to misjudge one of them within the year. Treating the US as a bigger, looser UK misreads the actual mechanism, it’s an opt-out architecture sitting on top of denser deterministic identity infrastructure, and the two together are why individual-level resolution travels so well there and so poorly everywhere else. The instinct that Europe leans on clusters and personas rather than individuals is basically right, but it’s driven as much by thin identity plumbing as by regulation, and Singapore in particular remains under-documented enough that anyone with a confident opinion on APAC match rates is very likely borrowing it from the US or European picture rather than the region itself.

The takeaway is that a vendor with global advertising aspirations would be better using an EU-native business to stand it up, and translating that architecture into the US works better because it IS easier. Taking the US approach to the EU makes the market feel smaller then it is.

And on messaging, a single global deck with a language swap is a weaker starting point than most teams assume, especially running UK or US-style direct-ask creative into APAC without the trust-building sequencing that market usually needs first, a lesson I paid for once so hopefully you don’t have to.

None of this is gospel and is my own interpretation on data. It’s a first, properly sourced pass at a question worth answering with our own numbers eventually. The honest next step, and the one a reader effectively asked for at the start of all this, is pulling our actual delivery data across London, New York and Singapore and seeing where it agrees with the wider industry picture and where our own patch of the market turns out to be the exception. If it holds up, that’s a piece worth writing on its own, and a better one than this.

If you’re the person in the room who has to explain why the UK deck doesn’t work in Singapore, this is what The B2B Stack is for.

Fifteen years in, most of what I read on global B2B advertising is either a generic localisation checklist or a regulatory update with no practitioner behind it. This is written from the trading desk out, one genuinely useful read a week on B2B programmatic, intent data, and the regional detail that never makes the vendor deck. No recycled listicles, no press releases dressed up as insight.

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Sources and further reading

The case for UK privacy law diverging from the EU rests on the ICO’s finalised guidance on storage and access technologies and Covington’s analysis of the ICO’s more permissive signalling, read alongside our own earlier piece on the PECR climbdown. The EU’s opposite trajectory is covered in Secure Privacy’s breakdown of the Digital Omnibus and Consenteo’s 2026 practitioner guide, which carries the CNIL and Digital Markets Act enforcement figures. The US opt-out architecture is set out in Recording Law’s state-by-state guide, and Singapore’s consent framework in Chambers and Partners’ 2026 Singapore data protection guide.

On identity resolution, Echo Analytics’ piece on coverage asymmetry, Roqad’s identity buying guide and Bombora’s explanation of B2B identity resolution between them cover the deterministic-versus-probabilistic split referenced above. The AI adoption data comes from Forrester’s regional AI adoption research and Anthropic’s own Economic Index, with the March 2026 Singapore ranking update via the Singapore AI Observatory. The high-context/low-context framing draws on Oban International’s cultural differences primer and Elevation Marketing’s B2B localisation guidance.

Whatever the CRO’s views on marketing performance, and I’m sure we’ve all seen some strong views, the disagreement begins with definitions and the success criteria that fall out of them. Definitions that were never jointly agreed before the dashboards got built. Both teams pulled from the same CRM. Both numbers are usually “correct.” They’re answers to different questions wearing the same name. And we still talk about sales and marketing alignment.

Big statement: account-based measurement is the single most confused reporting topic in B2B.

Nearly every definition in circulation was written by a vendor with a platform to sell, so nearly every definition flatters whatever that platform happens to measure well. Amazing at capturing web analytics data? Guess where the metrics will focus. Awesome at capturing advertising insights? You get the picture.

Nobody has written the whole thing out neutrally, from the perspective of the people who actually have to defend these numbers in front of a board.

So here it is. Structured as an actual reference: every entry follows the same format (what it means, where it gets confused, the operator’s take), the head-to-head terms are laid out in strict parallel, and there’s a copy-paste definitions block at the end that you’re actively encouraged to lift straight into your own reporting deck. Use it, don’t credit it, I genuinely don’t mind. The point is that the argument stops.

What you’ll learn in this article:

• The foundational units of account-based measurement (ICP, TAL, buying groups, ABM tiers, the dark funnel) and why getting the unit wrong corrupts every metric downstream

• How engagement and qualification actually work: engagement scores, intent data, vendor-branded states like the 6QA, and the full MQL/MQA/SQL/SAL/SQA/PQA stack, term by term

• The progression metrics almost nobody reports properly: coverage, buying group penetration, and velocity

• Pipeline sourced versus pipeline influenced versus multi-touch attribution, and why conflating them manufactures the quarterly marketing-versus-sales fight

• The correction layer: self-reported attribution, incrementality testing and MMM, and when each earns its place

• A complete reporting framework by audience and cadence, plus a copy-paste definitions block for your next board deck

How to use this

Read it once top to bottom, because the sections build on each other. After that, treat it as a lookup. Every term is defined in a self-contained entry, so you can land on any single definition cold and it’ll still make sense. That’s deliberate. It’s the difference between a glossary and an essay with headings.

If you’re reading this in a browser, bookmark it now. That’s what it’s for.

One prerequisite before any of it makes sense: get clicks out of your measurement stack. Click-optimised systems learn to find clickers rather than buyers, a big share of programmatic clicks carry no intent at all, most legitimate B2B inventory gets filtered out before a click is even possible, and entire channels your buying committee actually lives in (audio and CTV among them) don’t produce a click in the first place. I wrote the full argument here: The Observer’s Paradox. The one-line version for this glossary: if a metric still has “click” anywhere in its lineage, treat it as noise. And ask your platform vendor what percentage of their engagement score is click-derived. The answer, and how long it takes them to produce it, will tell you a lot.

Part 1: The foundations

Account-based measurement fails most often not at the metric level but at the unit level, because teams keep measuring accounts with instruments designed for individuals. Five terms establish the unit properly.

ICP (Ideal Customer Profile)

What it means: The firmographic and behavioural definition of the companies you should be selling to. Size, industry, region, technology stack, business model, and increasingly signal-based criteria like observed in-market behaviour.

Where it gets confused: Teams treat the ICP as a list. It isn’t. It’s a filter, a written definition against which any company can be tested. The list it produces is the TAL, below.

The operator’s take: The most common ICP failure is building it from aspiration (”who we’d love to sell to”) rather than evidence (”who actually closes, retains and expands”). Rebuild it annually from your own closed-won and churn data, not from the pitch deck. The other common mistake is having one big ICP when the business actually serves three or four distinct buyer types. Break those out into their own ICP clusters and everything downstream sharpens up.

TAL (Target Account List)

What it means: The named list of specific companies that pass the ICP filter and that you’ve deliberately chosen to pursue in a given period.

Where it gets confused: The TAL is the denominator for almost every metric in this glossary, which is why a badly built one corrupts everything downstream very quickly. If the TAL contains accounts that will never buy, your qualification rates, coverage figures and engagement trends are all being measured against a work of fiction.

The operator’s take: Most measurement problems that get blamed on attribution are actually TAL hygiene problems in disguise. Before commissioning an attribution project, audit the TAL. It’s cheaper, and it’s usually where the bodies are.

Even in the second half of 2026 it astonishes me how poor many TALs are. No standardisation, no refresh cadence, stale entries from two planning cycles ago, and millions being spent against them anyway.

The single biggest win available to most B2B vendors is investing proper work in their TALs. Plural is not an accident either: they should be segmented, and they should be fresh. At FunnelFuel we built a system with over 700 live connectors for exactly this reason, to join and combine sources, interrogate them, match them to ICP, and make sure the matching engine reflects the intent behind the TAL, meaning who you actually want to reach, rather than producing an inflated match rate that looks good in a QBR and targets nobody in particular.

The TLDR: nail your TAL, everything else swims off it. And if you want help thinking yours through, my inbox is open.

ABM tiers (1:1, 1:few, 1:many)

What it means: The standard structure for segmenting an account-based programme by investment level. Tier 1 (1:1) is a handful of accounts getting fully bespoke treatment. Tier 2 (1:few) is clusters of very similar accounts getting lightly personalised programmes. Tier 3 (1:many) is programmatic ABM, the full TAL receiving targeted but scaled activity.

Where it gets confused: Teams define the tiers for activation and then forget them at measurement time, reporting a single blended number across all three. But the tiers have completely different economics and should carry different expectations. A Tier 1 account might justify a year of patient buying-group development. Tier 3 lives or dies on efficient qualification rates.

The operator’s take: Never report a blended ABM number. Blending Tier 1 and Tier 3 produces a figure that describes neither. We regularly see lists where accounts genuinely worthy of Tier 1 attention get bundled into one big undifferentiated pile, because big is always better, right?

I always advocate for TAL splitting by at least one or two firmographic dimensions alongside the tiers. Breaking banks out from healthcare brands, for instance, opens up an easy win: you can run more scaled but still nuanced 1:many that starts to borrow from the 1:few playbook, cast for dramatically more signal, and feed the 1:1 accounts with better data as a result.

My bet is that technology erodes true 1:many over the next couple of years and lets world-class paid media run against 1:few audiences at 1:many economics, which is genuinely exciting. But only if the up-front investment in the TAL gets made. It always comes back to the TAL.

Buying group (buying committee)

What it means: The set of people inside an account who collectively make the purchase decision. In enterprise B2B this routinely runs to double digits across multiple functions, with different members researching different questions at different times, most of it invisibly. In 2026 Forrester estimates that buying groups are 13 people across 4 departments, sometimes with upwards of 9 external stakeholders brought in to aid the decision

Where it gets confused: The buying group is the reason lead-level measurement breaks in account-based motions. One enthusiastic individual tells you almost nothing about whether an organisation is in-market or where the entirety of a consensus driven committee’s collective heads are at. Moderate engagement from four different functions in the same account tells you a great deal. Every serious account-based metric aggregates across the buying group; every legacy metric doesn’t.

The operator’s take: Take the highlighted point above, and this in a nutshell is my argument for where programmatic falls over itself for B2B. Programmatic and many ad buying platforms are designed to hit individual users but they entirely lack the ability to map them up into a buying committee, and then up into the business they work for. The buying committee construct is critical and is the reality of B2B buying journeys.

If mapping your ICPs correctly to output the best possible set of TAL’s is critical, and it really is, then the next big job is to really understand who is making buying decisions.

Now different organisations will approach this differently. Some are smaller and leaner in their decision making, which doesn’t have to mean in their total org size. Some small businesses are slow and ponderous, and may have huge committees. The avatar of decision maker and influencers can at best be approximated and I love a signals based approach.

That means, which deviceIDs are showing intent, and they get mapped, where possible into people using an account graph. This then lets us understand the types of roles that are coming on your website, mapped to accounts and what we know from a firmographic POV about those businesses. This, with machine learning approaches, allows an estimation of buying committee avatars. CRM compounds the workflow. Then we take a broader approach to communicating with these avatars, which means casting a bigger net, because the reality remains that we do not know if any one organisation has a faster and looser, or wider and slower approach to buying - so we should err on the side of caution and use advertising to try and find out

To finish this off, I internalise that the account is the unit and the buying group is the texture within it, and the rest of this glossary follows logically. Every vendor dashboard you use will keep trying to pull you back down to the individual, because individuals are what cookies and form fills can see. Resist it.

Dark funnel

What it means: The portion of the B2B buying journey that happens on surfaces your measurement stack cannot instrument: peer recommendations, Slack and WhatsApp groups/threads, podcast mentions, community discussions, internal meetings, and increasingly the LLM a buyer consults before ever touching your website. None of it fires a pixel. Research across the intent vendors consistently puts the majority of the buying journey in this category, and buyers routinely arrive with a shortlist already substantially formed.

Where it gets confused: Teams treat the dark funnel as a measurement problem to be solved with more software or even just more effort. It cannot be. No platform can track a Slack recommendation. The dark funnel is a structural condition to be corrected for, which is what Part 4’s correction layer is for.

The operator’s take: The dark funnel is not an excuse for unmeasurable marketing but equally we have to accept real structural limitations, and not waste time trying to work around them where there is no viable way - that is not defeatist or small minded, it is pragmatic and realistic. It is the reason the correction layer (self-reported attribution, incrementality) has moved from nice-to-have to mandatory. If your measurement stack has no mechanism that can see the invisible journey, your dashboards are precise descriptions of the visible minority.

Part 2: Engagement and qualification

Account engagement score

What it means: A composite number, usually 0 to 100, representing how “engaged” a target account is with your brand, aggregated across every buying group member and every measurable channel, over a rolling window. Common inputs: website visits and content consumption, email engagement, ad exposure, event attendance, and third-party intent signals.

Where it gets confused: There is no industry-standard formula, and most platforms will not fully disclose theirs. The weighting logic varies wildly on five axes:

• Recency decay - does a whitepaper download from six weeks ago still count, and at what discount to yesterday’s?

• Committee weighting - does an engaged VP score the same as an engaged intern? Should they?

• Channel weighting - does a demo request live on the same scale as a viewable ad impression?

• Anonymous resolution - how much of the score rests on de-anonymised account-level activity versus known contacts, and how confident is the match?

• Normalisation - is the score comparable between a 200-person account and a 200,000-person account, or only meaningful against that account’s own trend line?

This is why the same account can legitimately score 85 in one platform and 40 in another with no data quality problem anywhere. Both are “correct.” They are different instruments measuring different things under the same label.

The operator’s take: Stop searching for the platform with the “right” formula. There isn’t one. Pick a methodology, document its logic in plain English so anyone can defend it in a leadership meeting, and use the score as a trend indicator for a given account over time. An account moving from 40 to 70 over six weeks is signal. The number 70 itself, in isolation, is decoration.

My approach with scoring has been to make it composable, which means the vendor teams can input and shape the scoring that we run for them. Use my tech to capture data you cannot capture today, use my tech to pipeline data between systems like web analytics and media buying which is very difficult to do, but bring your own scoring model to make your own reality out of this data pipelining.

Intent data and surge scores

What it means: Third-party signals indicating that an account is actively researching a topic, typically derived from content consumption patterns across networks of B2B publishers and research sites. A surge score flags that an account’s consumption of a given topic has spiked meaningfully above its own historical baseline.

Where it gets confused: Two big ways.

First, topic-level intent is not vendor-level intent: an account surging on “account-based marketing” is researching the category, not necessarily shortlisting you.

Second, intent providers differ fundamentally in how signal is collected (co-op publisher networks versus bidstream inference/modelling versus review-site activity), and those methodologies have very different noise profiles. Treating all “intent” as one substance is how bad TAL prioritisation happens.

The operator’s take: Intent data is an input to engagement scoring and TAL prioritisation, not a metric to report upward in its own right. A surge is a reason to act, never a result to claim. The moment “accounts surging” appears in a board deck as an outcome, the programme has lost the plot.

We need to be realistic about intent data in 2026 too. This is not having a go at any players in the ecosystem, and I partner with them all, and many are friends. However, the elephant in the room.

The same dark funnel behaviours which are impacting the tracking of your funnel is impacting the collection of intent data. Whether it is bid stream modelling (which we do a form of at Funnelfuel using audience containers to understand usage and intent), or publisher co-ops, these are two different ways to get to the same end point - understanding what people are reading on the content web. great - but - increasingly they use LLMs instead which sit squarely in the dark funnel. Buying committees are beginning their research journey 51% of the time in the dark funnel, and 94% - yes 94%! - of buying journeys now utilise LLMs. This is obvious in 2026.

However those 94% of journeys that use AI, or the 51% that start in an LLM used to start on observable content websites online, like G2, like IDG, like deeply specialist research sites which were capturable in 2020 via co-ops and modelling, and which are now feeding the funnel via citations which we never see. This data is thus thinner now then it was before LLMs. That is a fact

There is still value here but understand its limitations and it is another signal to use alongside others. That is why we say this is the signals era.

Vendor-qualified account states (6QA and equivalents)

What it means: Proprietary qualification thresholds productised by ABM platforms. The best known is 6sense’s 6QA: an account the platform’s model judges to have crossed into a late buying stage based on its combination of fit, intent and engagement, at which point it is flagged as ready for sales. Demandbase, Terminus and others run equivalent constructs under their own branding.

Where it gets confused: A vendor-qualified state is an engagement score with a threshold and a trademark. The model is proprietary, the weighting is opaque, and the qualification logic is configurable, which means a “6QA” at one company is not the same object as a “6QA” at another. Teams routinely compare these states across organisations, or present them to boards, as though they were standardised. They are not.

The operator’s take: These are useful operational triggers and I am not dismissing them at all; a well-configured one beats a home-rolled score for many if not most teams. But never let a vendor-branded state become a board-level metric without a plain-English definition of what it means in your configuration. “We had 40 6QAs this quarter” is not a sentence a CFO can act on.

What I am seeing is big enterprise clients, often using AI, understandably now believe that they can create their versions, without having to crowbar their own data into a pre-canned model. That is why more and more big players want lots and lots of raw data exhaust fumes, so they can run their own computations. A year ago we got a lot of asks to work with these platforms, today we definitely still do but the second cluster of DIY signal builders is growing by the week. This backs up the SaaS-apocalypse narrative.

The qualification stack: MQL, MQA, SQL, SAL, SQA, PQA

Six terms, because this is the layer that causes the most cross-functional friction, and it is almost always friction over definitions. For each one, hold three questions in mind: what is the unit (person or account), who does the qualifying, and what is actually being claimed.

MQL (Marketing Qualified Lead). Unit: a person. Qualified by: a marketing scoring threshold. The claim: this individual has shown enough interest to be worked. The trap: one junior researcher can generate an MQL while the five people who will actually sign the contract have never heard of you. That is not a data error; it is the structural limit of measuring individuals in a buying-group world.

The dream ticket would be migrating this from one person to a marketing qualified buying committee, especially if the committee had been mapped by marketing.

MQA (Marketing Qualified Account). Unit: an account. Qualified by: aggregate engagement across the buying group within a defined period. The claim: this organisation is showing coordinated signs of being in-market. The trap: check the aggregation logic, because one hyperactive contact should not be able to trip the threshold alone. If your MQA definition can be satisfied by a single person, you have renamed your MQLs, not replaced them.

Most scoring around this fails to take into account the buying committee in its broader context, so it really just becomes another term for account engagement score.

SQL (Sales Qualified Lead). Unit: a person. Qualified by: sales, after an actual conversation. The claim: this individual represents a real, workable opportunity. In account-based motions the SQL is a legacy unit, increasingly absorbed into the SQA, but it survives in most CRMs and most compensation plans, so it is worth defining even if only to retire it deliberately.

SAL (Sales Accepted Lead, or Account). Unit: either, depending on your motion. Qualified by: sales, on receipt of the handoff. The claim is procedural rather than evaluative: sales formally acknowledges the qualified record and commits to follow up within an agreed SLA. This is the forgotten middle step, and its absence is why the two oldest complaints in B2B (”sales never followed up” and “marketing sends us rubbish”) are both permanently unfalsifiable in most organisations. Nobody signed for the parcel, so nobody can prove anything about what happened to it.

SQA (Sales Qualified Account). Unit: an account. Qualified by: sales, after investigation. The claim: there is evidence of a real buying process, meaning an identified need, a champion, budget movement or a defined evaluation underway. The critical word is accepted. An SQA is a joint claim, agreed by both functions, not a marketing claim awaiting validation.

PQA (Product Qualified Account). Unit: an account. Qualified by: product usage signals, meaning seat adoption, feature depth, trial behaviour. The claim: usage suggests conversion readiness. Only meaningful if you run a self-serve or trial motion alongside enterprise sales. Managed-service and pure enterprise teams can ignore it; it appears in vendor glossaries mainly as a completeness gesture.

Where it all actually breaks: Almost always at the MQA-to-SQA handoff, for the same reason the MQL-to-SQL handoff was always contentious: the two functions are rarely working from a jointly written, jointly signed threshold. Here is the test. Ask your sales leadership, without a deck in front of them, what specifically distinguishes a sales qualified account from a marketing qualified one in your organisation. If the answer takes more than two sentences, you do not have a qualification stack. You have a naming convention.

The operator’s take: The fix costs nothing: a one-page written definition of each state, agreed by both functions, before the dashboard is built rather than after the argument starts. And add the SAL step even if it feels bureaucratic, because it converts the eternal “sales never followed up” versus “marketing sends rubbish” stalemate into a measurable SLA with a name attached.

Many of these terms are as dated as the funnel concept, and like many scores, they are only as good as what can be tracked. The scores limiting factor is data.

Part 3: Progression metrics

Between qualification and pipeline sits a set of mid-funnel metrics that are genuinely useful and almost universally under-reported, mostly because they do not flatter anyone and as a result nobody is keen to own them

Account coverage

What it means: The percentage of your TAL in which you have the assets required to actually run the motion: identified buying group contacts, identifiers to reach them with ads, working contact data, and active engagement channels.

Where it gets confused: Teams report programme metrics against the full TAL when the programme is only operationally live in a fraction of it. A TAL of 500 accounts with real coverage of 120 is a 120-account programme wearing a 500-account budget, and where budget is getting pushed to potentially saturated accounts in the 120 list

The operator’s take: Report coverage before you report anything else, because every downstream percentage is computed against it. It is also the most honest early indicator of whether an ABM programme is real or aspirational.

Buying group penetration

What it means: Within a given account, the proportion of relevant buying group roles you have actually identified, reached and engaged.

Where it gets confused: This is the metric that catches the classic false positive: an account that looks hot on aggregate engagement where every signal traces back to one person. Engagement scores can be inflated by depth-on-one-contact in exactly the way penetration exposes.

The operator’s take: Penetration is the honest counterweight to the engagement score, and the two should always travel together. High engagement plus low penetration is not a hot account; it is a research project with one enthusiastic author.

Deal velocity (and stage velocity)

What it means: How quickly accounts move between defined stages: TAL to MQA, MQA to SQA, SQA to opportunity, opportunity to close. Usually expressed as median days per stage.

Where it gets confused: Velocity means nothing without a baseline. “Our MQA-to-opportunity velocity is 47 days” is not information; “it was 68 days before the programme started” is.

The operator’s take: Velocity is the most CFO-legible metric in the entire account-based canon, because it converts marketing activity into a language finance already respects: time and rate. If nobody in your organisation captured a pre-programme baseline, that is this quarter’s first job, because every future efficiency claim depends on it. So in other words, for the CFO - if I give you $1m today, when can I expect to get returns on this?

Part 4: Pipeline, attribution and the correction layer

Now the main event: the source of the meeting from the opening of this piece.

Pipeline sourced vs pipeline influenced

The single most important distinction in this glossary, so it gets the table treatment:

Pipeline sourcedPipeline influencedDefinitionValue of opportunities where marketing generated the first touch that led to the opportunity’s creationValue of opportunities where marketing touched any buying group member at any point in the cycle, regardless of who originated the dealAttribution mechanicFirst-touchMulti-touch, typically over a lookback window running backward from opportunity creation and forward through to closeThe question it answersWhich activities create net-new pipeline that would not otherwise exist?Where is marketing accelerating and supporting deals across the whole motion?CharacterClean, conservative, deliberately narrowBroad, generous, deliberately inclusiveNatural audienceCFO, board (budget allocation)CRO, programme leads (programme effectiveness)Failure modeUnder-credits everything that isn’t first-touch, punishing brand and mid-funnel work”Influenced” can mean a single ad exposure to a single contact, so an unlabelled 70% influenced claim destroys the whole report’s credibility

These are not two versions of the same number. They have different definitions, different mechanics and different denominators, and most B2B attribution conflict is simply a marketing team presenting an influenced figure into a room that is mentally expecting a sourced one.

The operator’s take: Report both, always together, always explicitly labelled, never blended into a single “marketing pipeline” figure. Presenting one without the other is what manufactures the argument. And put a one-line definitions footer on every deck (there is one at the end of this piece you can lift verbatim).

Multi-touch attribution (MTA)

What it means: The rule set for distributing credit across touchpoints when more than one contributed to an outcome. It is the mechanism underneath the influenced number, not a separate metric. The standard models:

ModelHow it distributes creditWhat it’s forFirst-touch100% to the first interactionDemand creation questions (this is what “sourced” runs on)Last-touch100% to the final interaction before conversionDemand capture questions; systematically over-credits bottom-funnelLinearEqually across all touchesA neutral default when you refuse to make weighting decisionsTime-decayMore credit to touches closer to conversionLong cycles where recency plausibly mattersU-shapedHeavy credit to first touch and conversion touchBalancing creation and captureW-shapedHeavy credit to first touch, lead creation and opportunity creationMotions with a meaningful mid-funnel milestoneAlgorithmicModel-derived weights from your own dataSounds most sophisticated; hardest to explain in a boardroom

Where it gets confused: Two things every operator should internalise. First, the model choice changes the influenced number materially, which is why two teams both “doing multi-touch” can produce different figures and both be technically right. Second, and more fundamentally: MTA can only distribute credit across touches it can see, and in B2B most of the journey is dark funnel. MTA is a precise accounting of the visible minority.

The operator’s take: Pick one model, write down why, and stop shopping models until you find the one that flatters this quarter. The credibility cost of changing attribution models mid-year exceeds any insight the new model provides.

View-through measurement

What it means: Crediting outcomes to ad exposure without a click: an account was served impressions, and subsequently visited, engaged or converted. In a post-click world, and especially in channels like CTV and audio where clicks do not exist, exposure-based measurement is structurally necessary.

Where it gets confused: View-through is necessary and gameable at the same time. Loose view-through windows and generous matching logic let platforms claim credit for outcomes they merely coincided with. The question to ask any platform reporting view-through conversions: what is the window, what is the match methodology, and what does the same report look like with the window halved?

The operator’s take: Accept view-through as a directional input to account-level measurement, interrogate its settings, and never let a platform grade its own homework as the final number. Which is the perfect segue to incrementality.

Self-reported attribution (SRA)

What it means: An open-text “how did you hear about us?” field on every conversion form, and the same question asked verbally on every first sales call, with the answers logged and reviewed as first-class data.

Where it gets confused: Teams dismiss it as anecdotal precisely because it is analogue. Yet teams running software and self-reported attribution side by side routinely find radically different channel mixes: what software logs as “direct” or “organic search” was, by the buyer’s own account, a podcast, a peer recommendation or a community thread that triggered the branded search. Neither source is complete. Software tells you which touchpoints occurred; self-reported data tells you which ones mattered.

The operator’s take: Two implementation details determine whether SRA works or becomes theatre. Keep the field open text, because dropdown options collapse the interesting answers into “social media.” And look for buying-group patterns: three people from the same account citing the same source is a channel finding, not an anecdote.

Incrementality (lift testing)

What it means: The experimental answer to “did this activity cause pipeline, or just correlate with it?” Hold out a matched segment of the TAL from an activity, run it for the rest, and measure the difference in outcomes between exposed and held-out groups.

Where it gets confused: It is the only method in this glossary that measures causation rather than correlation, which is exactly why finance teams trust it and why it is quietly displacing attribution modelling as the top-of-house proof mechanism in sophisticated organisations. The honest caveats: it needs enough account volume to build meaningful test and control cells, it needs the discipline to genuinely withhold activity from the control group, and it answers big questions slowly rather than small questions instantly.

The operator’s take: Use incrementality for the material budget decisions, not the weekly optimisation. One well-designed holdout test per quarter, on the channel carrying the most budget or the most scepticism, is worth more board credibility than a year of attribution dashboards. A lift result is the only slide in the deck the CFO cannot argue with.

MMM (Marketing Mix Modelling)

What it means: Statistical modelling of the relationship between marketing investment by channel and business outcomes over time, using aggregate data rather than user-level tracking. The old brand-world technique, back in fashion because it needs no cookies and sees channels that attribution cannot.

Where it gets confused: MMM is regularly pitched to B2B teams whose data cannot support it. It wants years of weekly spend and outcome data with real variation in it, and meaningful ongoing investment to maintain. Below a substantial annual media budget, the model’s error bars are wider than the decisions it is meant to inform.

The operator’s take: For most B2B teams, MMM is a later-stage tool. Get SRA and periodic incrementality testing running first; they answer most of the same questions at a fraction of the cost. When media spend gets large enough that channel-level allocation errors cost real money, MMM earns its seat.

The layered stack, stated plainly

The mature measurement position is not one method but four layers, each doing what it is actually good at:

1. Platform measurement (engagement scores, qualification states) for operational speed

2. Sourced and influenced pipeline (separately labelled) for programme reporting

3. Self-reported attribution as the standing correction for what software cannot see

4. Periodic incrementality tests as the causal anchor for the numbers that decide budgets

Any one layer alone is either fast but shallow, or rigorous but slow. The stack is the answer.

Part 5: The reporting framework

Strip everything above into a cadence and it looks like this:

CadenceAudienceReportDo not reportWeeklyABM / demand teamEngagement score movement across the TAL, MQA and SQA counts, coverage and penetration by tierAnything as a revenue claimMonthlyCRO and sales leadershipSourced and influenced pipeline (separately labelled), MQA-to-SQA acceptance rate, stage velocity vs baselineBlended “marketing pipeline” figuresQuarterlyCMO and CFOSourced pipeline as the primary number, influenced as programme context, velocity trends as the efficiency story, incrementality results as the headline when you have themEngagement scores as anything other than a leading indicator

And one rule that governs all three rows: never present a metric to an audience that has not been given its definition. Most account-based measurement does not fail in the data. It fails in the room.

The copy-paste definitions block

This is the artefact the whole piece has been building to. Lift it verbatim into the footer of your reporting deck, adjust the lookback window to match your model, and the quarterly definitions argument ends:

Definitions used in this report. Pipeline sourced: opportunity value where marketing generated the first touch leading to opportunity creation (first-touch attribution). Pipeline influenced: opportunity value where marketing engaged any buying group member during the cycle (multi-touch attribution, [90]-day lookback, through close). These are different metrics answering different questions and are never blended. MQA: an account meeting the aggregate buying-group engagement threshold jointly agreed by marketing and sales on [date]. SQA: an MQA reviewed and accepted by sales as showing evidence of an active buying process. Engagement scores are leading indicators of account attention, not revenue claims.

Six sentences. It costs nothing, and it is the difference between a report that gets trusted and one that gets re-litigated every quarter.

The close

None of this is complicated once it is written down in one place. That is the entire problem with account-based measurement: every term above is well-defined by someone, somewhere, but almost never by the same someone twice, and nearly always by someone whose platform benefits from the definition. So every in-house team ends up rebuilding the Rosetta Stone from scratch, under deadline pressure, in a deck that half the room quietly distrusts.

Bookmark this one instead.

What is your organisation’s actual, written definition of “influenced,” and would your sales leadership agree with it without checking?

If this was useful, it was built to be kept, not just read. Subscribe below and The B2B Stack lands in your inbox on Fridays. And if the definitions block saves you one quarterly argument, forward it to whoever you usually have that argument with.

Vendor-qualified accounts / 6QA / engagement scoring

• 6sense Glossary (buying stages, 6QA definition): https://6sense.com/glossary/ and https://6sense.com/glossary/6sense-qualified-account/

• 6sense support docs — default 6QA qualification logic (buying stage, profile fit, configurability): https://support.6sense.com/docs/6sense-qualified-accounts-6qas

• 6sense on 6QA misattribution (useful supporting read for your attribution section): https://6sense.com/blog/the-6qa-mistake-how-teams-misinterpret-a-key-buying-signal/

MQA and the qualification stack

• SaaSTrack, “How Do You Define an MQA?” (MQA/SQA distinction): https://www.saastrack.ai/blog/how-do-you-define-an-mqa

• Archstone Digital MQA glossary (fit/intent/engagement model): https://archstonedigital.com/glossary/marketing-qualified-account-mqa/

• INFUSE MQA glossary: https://infuse.com/glossary/marketing-qualified-account-mqa/

• Clay MQA glossary (buying-committee rationale): https://www.clay.com/glossary/marketing-qualified-account

Sourced vs influenced pipeline

• Metadata.io, “Sourced vs Influenced Pipeline”: https://metadata.io/resources/blog/sourced-vs-influenced-pipeline/

• ZoomInfo Pipeline, marketing-sourced pipeline benchmarks and framework: https://pipeline.zoominfo.com/marketing/marketing-sourced-pipeline-trending-down

• Rework, “Marketing-Sourced vs Influenced Pipeline: The Distinction”: https://resources.rework.com/libraries/marketing-sales-alignment/marketing-sourced-vs-influenced-pipeline

• Saber glossary entries: https://www.saber.app/glossary/marketing-sourced-pipeline and https://www.saber.app/glossary/marketing-influenced-pipeline

• Prospeo, marketing-sourced pipeline (includes the Forrester 70%/48% tracking-gap stat): https://prospeo.io/s/marketing-sourced-pipeline

Self-reported attribution / dark funnel

• Refine Labs, Hybrid Attribution Framework study (the 90% measurement gap, podcast 53% vs 0% finding): https://www.refinelabs.com/article/hybrid-attribution-framework

• Refine Labs, “The Attribution Mirage” (methodology detail: mandatory open-text field, dark social close-rate analysis): https://www.refinelabs.com/article/attribution-mirage

• Prospeo, self-reported attribution guide (2026 summary of the same study): https://prospeo.io/s/self-reported-attribution

Incrementality / lift testing

• Measured, holdout test explainer (includes the catalog 14% vs 40% claimed example): https://www.measured.com/faq/holdout-test/

• AdSights incrementality glossary (test design best practice, minimum detectable effect): https://www.adsights.ai/resources/glossary/general/incrementality-testing

• Amplitude, incrementality testing overview: https://amplitude.com/explore/experiment/incrementality-testing

• Academic grounding if you want it: Lewis & Rao lineage via arXiv, “Latent Stratification for Incrementality Experiments” (calls holdout experiments the gold standard for causal inference): https://arxiv.org/pdf/1911.08438.

• Why AI-mediated research has become the most closed, least instrumentable version of the dark funnel that’s ever existed - and frankly there are no fast ‘fixes’

• Why buying an ad slot inside the model itself is live in some platforms, reversed in another, and absent entirely in Claude, and why that’s the wrong place to focus anyway

• How omnichannel paid media builds the muscle memory that gets your brand typed directly into the prompt, which sidesteps the retrieval contest altogether

• How the same paid reach can condition the corpus a model actually reads, by amplifying third-party proof into the exact sources providers are now licensing directly

• What to do with the rare, highly qualified traffic that does leak back from an AI-cited answer

• Why an account graph is what turns all of this from a hunch into something you can target, prove, and improve

Today I am exploring an enterprise-grade workaround to the dark funnel problem. Not a set of challenger brand style gorilla tactics to get cited today and gone tomorrow, nor some reverse engineering style thinking to understand which accounts have seen you in an LLM - but a credible, and powerful way to influence the prompts that buying committees use to pre-=condition them to include your brand on their shortlist.

The dark funnel just moved somewhere you genuinely can’t see it

We’ve talked about the dark funnel for years, that stretch of research happening away from your owned properties, invisible to your CRM and your intent tooling.

What’s changed is that it’s now happening inside a genuinely closed conversation. No cookie. No pixel. No retargeting tag. No leaked query string to mine. A buyer opens a chat, forms an opinion about your category, and you get nothing back, not even the fact that the conversation happened.

The scale of it is the part worth taking note - the TLDR is that, pretty much, every B2B journey is now impacted by LLMs. This isn’t niche, you should assume every buying journey is impacted.

G2’s March 2026 survey of 1,076 B2B software buyers found 51% now start their research inside an AI chatbot rather than a search engine, up from 29% a year earlier. Forrester’s global numbers put overall usage higher still: 94% of B2B buyers used AI during their most recent purchase process, up five points on the year before [2]. These stats are pulling on different threads - G2 is saying the journey begins in AI more often than not, and Forester is saying that essentially all buying committees then use it at some subsequent point in the research journey afterwards.

The above usage stats don’t surprise me one bit - but this one does. Of those buyers, 69% ended up choosing a different vendor than the one they’d originally planned to, and a third bought from a vendor they’d never even heard of before that research began [1]. Research has long shown that the vendors that start the process at the front of the list often end the process with the DocuSign safely stowed in their inbox. Now it seems that buying committees are trusting the LLMm so much that it can, and the majority of time does, change their mind. So essentially the LLM is another member, perhaps the most influential one to boot, of the internal buying team.

That’s not a future-state slide. That’s the shortlist being rewritten, right now, inside the accounts on your target list, in the one part of the journey that has always been the hardest to reach and has just become instrumentation-proof into the bargain.

We have to get the LLM on-side, but fortunately we also know that any LLM is incredibly influenced by the prompt/s that go into it, and frankly they have a high agreement rate with the sentiment of the prompt.

I’m not saying any buying committee member would be this excitable in their prompt, but here is Perplexity being steered by the prompt

But this exaggerated point aside - how can we influence LLM’s and the incoming prompts with targeted approaches, which can sit alongside earning more citations for less steered prompts - lets dive in

Why you can’t simply buy your way in, and why that’s the wrong question anyway

The obvious first instinct is to treat this like any other new surface: buy an ad on it. Worth answering honestly, because the picture is more mixed than it looks from the outside.

OpenAI began testing ads inside ChatGPT’s free and low-cost tiers in February 2026, priced around $60 CPM through managed placements rather than open self-serve.

Google has extended ads into AI Overviews and its AI Mode, now appearing alongside roughly a quarter of AI-generated results [3]. Perplexity tried the same path first and reversed it, concluding sponsored placements undermined the trust its answer engine was built on [4]. Anthropic runs no advertising in Claude at all [5]. And even where the ad slot exists, OpenAI has said plainly that paid placement doesn’t influence which sources the model actually cites in the answer itself [6]. eMarketer’s own estimate for 2026 is that over 80% of AI advertising spend sits beside the answer, not inside it [7].

Does this mean its not worth trying their ads? no - of course not. However the blunt reality with ChatGPT ads is that its on their free tier of users, and how many enterprise B2B buying committees are using a free LLM, and not an enterprise version? This strikes me as great if you’re selling B2C widgets but not when you’re selling into enterprise B2B committees.

The referral economics make the same point from another angle. Cloudflare’s mid-2026 crawl data has Anthropic’s crawlers hitting somewhere north of 11,000 pages for every referral sent back to a site, OpenAI’s nearer 857 to 1, against Google’s roughly 5 to 1, and all AI chatbots combined were sending under 0.3% of referral traffic as of May 2026 [7]. Even a genuine citation mostly isn’t sending you a click. Does that matter and mean you shouldn’t be aiming for citations? no, its yet another example of clicks being an over-rated currency of B2B buying journeys but either way, don’t expect avalanches of traffic from LLMs, period.

Put plainly: the direct route into the model is patchy, platform-dependent, and structurally separate from the thing you actually want to influence, which isn’t the ad slot. It’s what the model says, and which brand the buyer types into the box in the first place.

Conditioning the buyer, not the model - lets flip the thinking 180

This is the real lever, and it’s the oldest job advertising has ever had, applied to a genuinely new interface.

If a buyer opens a chat and simply asks how your brand compares to a competitor, the model isn’t running a retrieval contest. You’re already inside the prompt. That’s not an SEO outcome. It’s a memory outcome, built long before the chat ever opens, and it’s exactly what sustained reach across CTV, audio, and DOOH, working alongside the rest of the paid stack, is built to do. Build brand memory, so when the procurement process kicks off, your brand is on that initial list

There’s early evidence this compounds rather than sits still. Reporting on Profound and Semrush’s 2026 data found brands with strong AI visibility saw 18 to 34% more branded search volume than comparable brands with equivalent SEO but weaker AI mention rates, describing a two-way loop: being named in an AI answer drives a direct branded search afterwards, and a market that already holds you in memory is more likely to name you in the prompt to begin with [8].

Treat that loop as the target. The job of omnichannel media in an agentic world isn’t winning a citation you can’t instrument or buying a slot that doesn’t move the answer. It’s building enough category-linked muscle memory that the buyer does your targeting for you, unprompted (no pun intended), the moment they open the box. Call it prompted recall if you want a name for it. It’s mental availability with a new proof point. Advertising 101

Conditioning the corpus: the same reach, pointed at what the model actually reads

The second lever is less obvious, and it’s closer to what a media plan already does, redirected slightly.

Several AI providers are now striking direct licensing deals with the platforms their models draw on, Reddit, Stack Overflow, G2, Linkedin, News Corp and AP among the publicly reported examples [9]. That means being reflected in, and amplified across, that same tier of licensed and high-authority environments, analyst platforms, review sites, trade press, syndication partners, is doing double duty. It earns a citation on its own terms, and it sits inside the exact pool several providers are now paying to draw from directly.

This is where paid reach and content syndication do something a slot inside ChatGPT can’t. They drive distribution, freshness, and third-party validation density around the proof points that actually get cited, at the accounts that matter. It’s worth being honest about the risk sitting underneath this, too. Every vendor can currently claim it has the best-fitting audience or the strongest proof, in much the same way every URL once claimed to be every IAB category simultaneously in early programmatic, and an agent negotiating on unverified claims has no more ground truth than a DSP matching against a self-declared category string used to. Amplification only compounds an advantage if what’s being amplified can actually be verified against real account-level signal, not just asserted in a deck.

Which is what the diagram below is doing. It’s not the headline of this piece, it’s the plumbing underneath it: the same three environments paid media is trying to condition, an owned site, containerised audiences, and the walled gardens, unified into one graph that turns “we think this landed” into something you can actually target, prove, and retarget against.

Three signal sources, owned website, containerised audiences, and walled gardens, feeding into a central account graph, which produces agent-ready validated signal as output

Closing the loop on the traffic that does leak back

The last piece is instrumentation, not acquisition. AI-referred sessions are rare, by the numbers above, but they tend to arrive further along and better qualified than an average organic visit. Tagging and separately retargeting the accounts that do land via an AI-cited link, rather than folding them into the same nurture flow as a cold click, is a modest build on infrastructure most teams already have sitting inside an account graph and Deal ID layer, and a genuinely underused one.

What this means practically - here’s my thinking to save, share and swipe

We’ve been on a good run of these lately, so if this one landed, forward it to the person on your team still treating the dark funnel as unreachable rather than unmeasured. That’s the conversation worth having next.

Need help with any of this?

B2B paid media is hard, and its only getting tougher. If you need a hand with any of this, especially programmatic B2B advertising, reach out to me via mike @ funnelfuel .io or reply to this email.

Sources

1. G2, The Answer Economy: How AI Search Is Rewiring B2B Software Buying (survey of 1,076 B2B software buyers and decision-makers, fielded March 2026, published 15 April 2026). g2.com/answereconomy

2. Forrester, The State Of Business Buying, 2026, drawing on Forrester’s Buyers’ Journey Survey (published 21 January 2026). forrester.com/press-newsroom/forrester-2026-the-state-of-business-buying

3. Reporting on OpenAI’s ChatGPT ad pilot (launched 9 February 2026, ~$60 CPM) and Google’s AI Mode/AI Overviews ad expansion (approximately 25.5% of AI results as of early 2026), via digitalapplied.com, “AI Search Advertising: ChatGPT vs Google vs Perplexity” (March 2026)

4. Reporting on Perplexity’s reversal of its advertising programme, via ALM Corp, “Perplexity AI Abandons Advertising” (February 2026)

5. Stackmatix, “Advertising on AI Platforms: The Complete 2026 Landscape” (April 2026), confirming Anthropic’s Claude carries no advertising programme as of 2026

6. Built In, “How to Make Brand Content More Citable in AI Search” (May 2026), reporting OpenAI’s own statement that ad placement does not influence which sources ChatGPT cites

7. eMarketer’s estimate (over 80% of 2026 US AI advertising spend sitting beside rather than inside AI answers) and Cloudflare’s mid-2026 crawl-to-referral ratio data, both via smalk.ai, “AI Search Advertising in 2026: Beside the Answer, Not Inside” (June 2026)

8. Reporting on Profound and Semrush’s 2026 analysis of AI visibility and branded search lift, via get-ryze.ai (July 2026). This is a secondary write-up of third-party research rather than the underlying Profound/Semrush study itself, so treat the 18 to 34% figure as directional rather than precise

9. Ayzeo, “Strategies to Get Your Brand Cited by AI Chatbots” (January 2026), on publicly reported AI content-licensing deals with Reddit, Stack Overflow, News Corp, and AP

Note on sourcing: figure 2 (94% AI usage) is Forrester’s own reported year-on-year figure. Some further sub-splits of that survey are quoted only in secondary write-ups of the Forrester report rather than in material we could verify directly against Forrester’s own release, so they’ve been left out of this piece rather than risk overstating them.

What you’ll learn in this article:

• Why optimising toward clicks trains your system to find clickers, not buyers, and why that’s structurally different from a targeting problem

• What the evidence actually shows about who clicks on ads, and how much of it has nothing to do with intent

• Why B2C-style click optimisation routes budget toward suspect environments, and why in-app games are the clearest example

• How brand-safety filtering and click-chasing bidding combine to filter most real B2B impressions out before they ever reach a DSP

• Why clicks don’t exist at all in audio and barely register in CTV, and what that means for attribution design

• A practical framework for what to measure instead, channel by channel in order to maximise your investments into programmatic paid media

The pattern, restated

Here’s the setup, because it’s worth being precise about it. A GTM team invests in intent data, which is using an array of observable signals across the internet, job boards, pixel tracking to infer tech stacks and other such sources, to indicate which accounts may be in motion - so far, so good. They layer this into an ABM platform or LinkedIn to run some ads. Builds a high intent TAL. Launches the campaign. Then optimises the whole thing toward whoever clicked.

Not who engaged meaningfully. Not which accounts showed multi-signal buying behaviour. Not which members of the buying committee consumed content across three separate touchpoints over three separate visits. Who clicked.

Its a proxy B2B teams have fallen back to since paid media began, largely because unlike our B2C cousins, we do not have the observable (and short) path to trackable purchase as the ultimate source of truth. In b2C, where we may be selling sneakers, ‘all’ we need to do is pick a handful of data segments and bake them off measured on ROAS. This luxury is not in the B2B building.

So when we deploy the worlds best advertising tech to chase clicks, their elite AI optimisation engines go looking for segments of users who look like prior clickers. The algorithm duly learns to find clickers, based on looking for matching behaviours in the new cohorts of users today which matches those behvaiours shown from previous users who went on to click. You may instantly think this doesn’t really matter if they are within your TALs, but it does. It matters because clickers, it turns out, are very often not buyers. They are not the super-high intent cohort within the TAl that they are often assumed (or hoped?) to be.

This isn’t a targeting flaw you can patch with a better audience or a tighter frequency cap. It’s closer to what statisticians call the observer’s paradox: the act of measuring something changes the thing you’re measuring. Optimise toward what’s countable and the system quietly stops looking for what actually matters, because what actually matters was never the thing being counted.

B2B buying is slow, deliberate, and largely invisible to a dashboard. Gartner’s research (see sources at the end of this newsletter) shows buyers spend only around 17% of their total buying time in direct contact with potential vendors, meaning roughly 80% of the journey is self-directed, and Gartner’s 2026 research puts 70-80% of the B2B buying journey inside this dark funnel before any vendor contact form is filled.

So on B2B’s much [over] cited 95/5 rule, only 5% of your accounts are maybe in market for what you sell today. Of those 5%, 80% are likely still in the dark funnel, and maybe 1% of the overall total TAL is both in-market AND showing observable signal.

Forrester’s 2025 survey found the average B2B purchase now involves around 13 stakeholders inside the buyer’s organisation and nine outside it, spanning three or more departments. None of that shows up as a click. Almost all of it happens before anyone lands on your site.

So when a campaign optimises purely on click-through, it isn’t finding the buying committee. It’s finding whoever happened to be holding a phone at the wrong moment.

What clicking actually measures

If you want to see how weak the link between clicking and intent really is, you don’t need to look at B2B specifically. The clearest evidence comes from the environments where clicks are cheapest to generate: mobile display and in-app gaming, which happen to be a huge share of the inventory that flows through open-exchange, B2C-style DSPs.

A widely cited Retale survey found that 60% of clicks on mobile banner ads are mistakes, most often happening while people are checking the news, scrolling social media, or playing games. This, in my opinion, does not render the impression value-less, I think there IS still strong value in the wider, persistent muscle memory building of repeated exposure across environments. When a buying journey does begin, being front of mind is critical to being part of the small segment of prospective vendors that the researching buying committee will speak to - but the click in this instance is not valuable. Its indeed likely to be annoying. To the extent that I sometimes wonder if the fact internet ads can be clicked is a disadvantage.

An earlier Online Publishers Association study put the figure at roughly half of all mobile ad clicks being accidental, and the mechanism is exactly what you’d expect: touchscreen games place control buttons close to the ad unit, and the closer the ad sits to something a person is actually tapping to play, the higher the accidental-click rate climbs.

So when you’re training your DSP to chase clickers, they are going out there actively trying to buy these ad placements in a way they would not be doing if you were not chasing clicks to begin with.

It’s got worse, not better, as interfaces have evolved. Speaking at App Promotion Summit London in 2025, a Kayzen partnerships lead presented data showing video ad click rates on mobile climbing from 10-20% three years earlier to over 80% today, particularly on iOS, driven by interface changes that make accidental taps more likely.

In one heatmap study, 75% of the unintentional clicks clustered around skip buttons were responsible for only 13% of the resulting app installs. Publishers know exactly what’s happening here: the shift from clickable buttons to full-screen clickable ad units, combined with smaller and slower skip buttons, has been a deliberate industry-wide trend.

The TLDR? the publishers know the game the advertisers are playing and they are gaming the system to get the advertising dollars.

Then there’s the deliberate version of the same problem. A meaningful share of app inventory monetises through rewarded formats, where users are paid in coins, tokens, or in-app currency to tap an ad. Security researchers describe these as incentivised clicks: technically generated by real humans, but with artificial intent, since users are clicking to earn a reward rather than out of interest in the advertiser’s offer.

Even by the ad tech industry’s own accounting, more than half of the top US non-gaming apps in a major performance index carry some incentivised ad format, and the practice is especially entrenched in gaming, where roughly 15 of the top 25 media sources sell incentivised inventory alongside their other formats

Put these together and you get a click-through rate that looks encouraging on a dashboard and means almost nothing. A high CTR in this kind of environment isn’t a signal of engagement. Security vendors flag it as one of the clearest tells of a low-quality or fraudulent placement: an abnormally high click-through rate paired with poor post-click engagement is itself a warning sign, not a result to celebrate

This is the part that should worry anyone running B2B programmatic through the same infrastructure built for B2C performance marketing. If your bidding algorithm is trained to chase clicks, and a large share of the cheapest, most abundant clicks in the ecosystem come from someone’s thumb missing a “skip” button in a mobile game, your model is not learning what a buyer looks like. It’s learning what a thumb looks like.

This is all a totally avoidable longtail of online display, which sullies its name and drives B2B brands away from the space. The good news though is that when you do it right, you are buying high intent, high quality attention at significantly better rates then in environments like LinkedIn, Meta and Google - so understanding this, working around it and building better systems is disproportionately worth your time.

Filtered out before you ever see it

Here’s the part of the story that gets missed almost entirely, and it’s the reason so much genuinely B2B-relevant inventory never shows up in a DSP like The Trade Desk or StackAdapt in the first place.

Programmatic brand safety is mostly still run on blunt keyword and category blocklists. It’s designed to protect brands from genuinely harmful adjacency, but it’s calibrated so conservatively that it takes out enormous amounts of perfectly legitimate content along with it.

Industry research on this shows that aggressive blocklists and broad category exclusions routinely knock out legitimate news publishers because news is treated as a blanket risk category, health and wellness content because medical terminology trips keyword filters, and large swathes of non-English inventory because classifiers are less confident outside core markets. The same research puts the scale of the problem starkly: keyword-based brand safety tools and overly strict filters can eliminate 40% to over 60% of otherwise high-quality inventory, and up to 70% of what gets blocked later turns out to have been unnecessarily restricted

Trade press, business analysis, financial commentary, cybersecurity reporting, defence and industrial coverage: this is precisely the category of content a B2B buying committee actually reads, and it’s also precisely the category most likely to trip a legacy blocklist. A programmatic sales lead described the mechanism bluntly: requests for a news private marketplace regularly fail to scale once you trace the problem back to a keyword list, and in some cases buyers are simply blocking news as a category outright, using an old legacy filter that was one of the IAB’s original presets.

The absurdity of the approach became visible during Euro 2024 coverage, when roughly half of one major publisher’s tournament coverage was blocked for containing football terms like “shoot” and “attack”. The same crude logic runs across B2B-adjacent verticals every day: earnings terminology, security incident coverage, macro and geopolitical commentary, all of it liable to trip a filter built for a completely different risk profile.

So the inventory most likely to carry a genuine business audience is disproportionately filtered out before the auction even happens. What’s left in the open exchange, disproportionately, is the cheap, high-volume, high-accidental-click inventory that survives the blocklist precisely because it’s bland enough not to trigger it: casual games, low-quality utility apps, content farms.

Then click-optimised bidding does the rest. Even where quality B2B-adjacent inventory does clear the brand-safety filter, it rarely wins a click-chasing auction against a mobile game with an 80% click rate, because the algorithm was never taught to tell the difference between a genuine click and a thumb slip. Two filtering layers, stacked, and the result is the same: a meaningful share of the impressions your buying committee is actually exposed to never gets bought at all, while budget concentrates in exactly the environments the evidence above describes.

This is the mechanism worth sitting with. It isn’t that B2B advertisers are choosing bad inventory. It’s that the combination of blunt brand safety and click-chasing optimisation systematically filters out the inventory that would have reached real buyers, and systematically rewards the inventory that generates the most low-intent taps.

Clicks don’t exist everywhere else

Assume, for a moment, that you fix all of the above within display. You still have a channel problem, because most of a modern B2B media plan doesn’t run through a clickable surface at all.

Audio has no viewability standard and no clickthrough benchmark to speak of, which is one reason WARC found that while listeners spend around 31% of their time with audio, brands allocate less than 9% of total media budget to the channel. This is changing fast and something we see at FunnelFuel is a rapidly increasing appetite from leading vendors to buy the underpriced audio inventory representing industry podcasts and other quality media. Research shows that the c-suite spend 51 minutes a day on audio - often whilst in the gym, commuting or walking the dog. The lack of clicks is no longer holding back the biggest vendors, hey want in, especially now ABM can be run end to end in audio environments.

CTV is worse still on this click predicated dimension. A viewer watching a spot on a connected TV device simply cannot tap a link, and the default attribution windows on most DSPs are still built around click-through behaviour that the channel structurally cannot produce. Video completion rate tends to run high in CTV precisely because most ad formats there are non-skippable, which makes it a useful signal for detecting gross problems but a poor one for anything more granular. B2B buyers are not going to be scanning QR codes for offers either, so we have to think differently now.

The channels that carry the most weight in a considered B2B buying journey, audio and long-form video among them, are exactly the channels where a click-based measurement stack goes silent. If your attribution model can’t register anything in the channel, the channel gets underfunded relative to its actual influence, and budget drifts back toward the clickable surfaces described above by default, not by decision.

The industry’s answer here is more mature than in B2B, and worth borrowing wholesale.

CTV outcome measurement typically leans on some combination of view-through attribution, household-level matching that links exposure to downstream activity, modelled or probabilistic attribution, and lift testing that compares an exposed group against a genuine control. I know CTV can be bought targeting and reporting against an account, because I helped design the design that enables it. The rest can therefore be achieved once the account signal is known. This is doable today.

Multi-touch attribution across devices tends to be more accurate than last-click for capturing CTV’s upper-funnel contribution, even though it isn’t foolproof, and brands running incrementality tests have consistently found CTV view-through contributing 15-25% lift to site visits and conversions that a last-touch model would miss entirely. For a channel with no click, the answer isn’t to force a click-shaped metric onto it. It’s to build a measurement stack that was never expecting one.

What attribution should actually look like

So: what would you optimise toward if clicks didn’t exist? A workable answer looks less like a single metric and more like a layered confidence model, built around three shifts.

Move the unit of analysis from the user to the account. A click happens to one device, at one moment, possibly by accident. A buying decision happens across a committee, over months. Any signal you weight has to be capable of aggregating across a real buying group, not a single cookie or device ID, or you’re structurally incapable of seeing the thing you’re trying to measure.

Weight engagement depth over engagement occurrence. Whether someone clicked is a binary. Whether they read to the end, returned within the attribution window, consumed content across formats, or showed up across multiple members of the same account is a much richer signal, and it’s available in the same data most teams are already collecting but not weighting. Dwell time, return visits, and cross-format consumption within an account correlate with buying-stage progression in a way a single click never will.

Replace channel-specific proxies with channel-appropriate ones, then unify them at the account level. View-through and household matching for CTV. Completion rate and frequency for audio. Genuine multi-touch weighting, not last-click, for anything with a clickable surface. None of these individually solves attribution. Together, rolled up to the account rather than the device, they build something closer to a confidence score than a click count, which is the actual point.

Practically, this means auditing where your current optimisation signal is actually coming from before you touch anything else. Pull the placement-level breakdown behind your best-performing “engagement” segment and check what proportion of it is running in-app, on mobile, in casual gaming or utility categories. If a disproportionate share of your best-performing inventory by CTR is running there, you already have your answer about what the algorithm has actually learned to find. Then look at what’s been filtered out on the other side: run a domain and category report on your blocklist exclusions and ask, honestly, how much of what’s excluded is genuinely unsafe versus simply unmeasured by an outdated keyword list.

None of this requires new inventory. It requires a different question at the point where the bid is placed and the model is trained: not “did this generate a click,” but “does this look like the kind of exposure a buying committee accumulates on the way to a decision.” That’s a harder thing to build. It’s also the only version of the question that was ever worth asking.

If this resonated, the honest next step is auditing where your own optimisation signal is actually coming from, not adding another tool on top of it. Subscribe below for the next instalment, or reply and tell me what your placement-level breakdown looks like once you actually pull it.

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Sources referenced:

Here’s the full source list with URLs, matched to what each one supports in the piece:

Accidental clicks

• Online Publishers Association study (via VentureBeat) — ~50% of mobile ad clicks accidental: https://venturebeat.com/mobile/conversions-over-clicks-how-mobile-app-developers-can-drive-results-for-advertisers/

• Retale survey (via MediaPost) — 60% of mobile banner clicks are mistakes: https://www.mediapost.com/publications/article/268266/60-of-all-mobile-banner-ad-clicks-are-accidents.html

• Pixalate accidental click data (2017 programmatic analysis by device/format): https://www.pixalate.com/blog/accidental-clicks-mobile-ads-data

• Kayzen / App Promotion Summit London 2025 findings (10-20%→80%+ CTR trend, skip-button heatmap study) via Business of Apps: https://www.businessofapps.com/news/mobile-video-ad-click-rates-surge-raising-questions-about-user-intent/

• Pontiflex/Harris Interactive survey (47% of app users click ads by mistake) via MarketingProfs: https://www.marketingprofs.com/charts/2011/4365/mobile-app-users-click-on-ads-mostly-by-mistake

• Tolomei et al., Yahoo Research, accidental click detection paper: https://arxiv.org/abs/1804.06912

Incentivised / fraudulent clicks

• HUMAN Security click fraud industry guide: https://www.humansecurity.com/learn/resources/click-fraud-industry-guide-protection-prevention/

• TrafficGuard on click fraud types (rewarded/token clicks): https://www.trafficguard.ai/blog/types-of-click-fraud

• AppHarbr on forced/deceptive click ads: https://appharbr.com/mobile-forced-clicks-ads/

• AppsFlyer on rewarded advertising prevalence in gaming apps: https://www.appsflyer.com/blog/mobile-marketing/rewarded-advertising-good-bad-ugly/

• ClickGuard on fraudster click monetisation mechanics: https://www.clickguard.com/blog/how-fraudsters-make-money/

• Wikipedia, Click fraud (background/definitional): https://en.wikipedia.org/wiki/Click_fraud

B2B dark funnel / buying committee stats

• Gartner/Forrester figures via Similarweb: https://www.similarweb.com/blog/marketing/marketing-strategy/dark-funnel-b2b/

• Gartner/Forrester figures via Medium (Sterling Phoenix): https://medium.com/@IamSterlingP/the-dark-funnel-is-not-a-measurement-problem-it-is-a-buying-system-shift-ed03081327d3

• ENaiBLD on Gartner 2024 buyer research (17% direct contact time, stakeholder counts): https://enaibld.com/resources/buyer-behavior-and-the-modern-sales-cycle/gartner-forrester-research-confident-misunderstanding-b2b-buying/

• Iliana AI on Forrester 2025 Buyers’ Journey Survey (13 internal/9 external stakeholders): https://ilianaai.com/dark-funnel-explained/

• Brixon Group on Gartner’s 80% rule: https://brixongroup.com/en/the-modern-b2b-buying-journey-why-buyers-complete-80-of-their-journey-alone-and-how-you-can-still-remain-visible

Brand safety / keyword over-blocking

• MGID on brand safety over-blocking (40-70% figure): https://www.mgid.com/blog/brand-safety-in-programmatic-advertising

• AdMonsters on Jana Meron / news over-blocking: https://www.admonsters.com/rethinking-brand-safety-lessons-from-jana-meron-on-news-advertising-in-2024/

• Digiday, programmatic sales lead confessional on news blocking: https://digiday.com/media/they-are-blatantly-blocking-news-confessions-of-a-programmatic-sales-lead-on-brand-safety-filters-impact-on-publishers-direct-sold-ads/

• Peer39 on the cost of keyword blocking: https://www.peer39.com/blog/costs-of-keyword-blocking

• ExchangeWire on 2024 Euros “shoot/attack” over-blocking: https://www.exchangewire.com/blog/2024/12/10/rewriting-the-rules-how-2025-will-redefine-brand-safety-in-media/

Audio / CTV clickless attribution

• Adweek/Adweek partner content on audio measurement (31% time / 9% budget): https://www.adweek.com/partner-articles/when-it-comes-to-audio-measurement-think-programmatic/

• AI Digital on CTV measurement methods: https://www.aidigital.com/blog/ctv-measurement

• Adtaxi on proving CTV performance without last-click: https://www.adtaxi.com/blog/proving-ctv-performance-without-last-click-attribution/

• Singular on CTV attribution: https://www.singular.net/ctv/

• Influencers Time on CTV attribution windows / DV360 config, 15-25% lift figure: https://www.influencers-time.com/creator-content-on-programmatic-ctv-specs-and-rights-guide/

• Strategus / Gigawatt Media on VCR/PCR as CTV metrics: https://www.strategus.com/blog/ctv-measurement-performance-metrics-attribution / https://gigawatt.media/the-basics-of-analyzing-programmatic-campaign-performance/

Over the 6 years since the pandemic, it’s often been said that this era is the exponential era. This is driven by the rapid changes in technology, and nothing personifies that better than the AI revolution. You’d have to have been living in a cave to not have come across the impact this technology has on everything from work to day to day life, but as the VC funded ‘grow at all costs’ era of AI starts to draw to a close, we have a new paradigm to consider alongside the usual questions around AI adoption, how to use it, where not to, how people fit alongside this tech and all the other big questions.

The one question that hasn’t really been part of this argument is cost. To date the technology has been priced ridiculously cheaply to drive adoption and reliance. As the VC era closes and the AI companies need to drive closer to sustainable growth, the costs will rise. There’s been some viral stories around this out there, and I’m picking it up to run a wider founder focussed through experiment on AI

Every piece I’ve read on the AI cost crisis treats it as an operations problem. Set smarter model defaults, like for example, maybe you don’t need the highest octane model to figure out where Microsoft’s head quarters are or what the bath temperature should be for a 6 month old. Cap the tokens. Route to something cheaper and more aligned to the task. Get finance to build a dashboard.

All of that is possibly true, and all of it is downstream of the actual decision, which doesn’t sit with an ops team or a procurement function. It sits with the founder or business ownership - but in this piece I’ll refer to it as the founder, as that’s my lens.

Whether a business ends up with a second payroll and how well that second payroll is managed is a founder-level capital allocation call, made with the same weight as a hiring plan or a fundraise, not a line item that gets discovered after the fact when the invoice lands. Or at least it should be, so my message to any reader of this is to help bring this awareness to your organisation if it’s not there yet.

Employees experience AI adoption as a tool getting better or worse, faster or slower, more or less annoying to use. More or less job threatening. More or less helpful in their day to day role and responsibilities

Founders experience it as a structural decision about what the company is actually made of. Those are not the same lens, and most of what’s been written about this crisis has been written through the wrong one.

What you’ll learn in this article:

• Why a Series B founder’s viral Anthropic bill is a warning sign, not an edge case

• The two cost lines that defined my first business, and why founders, not employees, are the ones who actually feel them

• Why AI spend behaves like a UK income tax cliff edge, and why a founder’s job is to plan for the cliff before it arrives

• Why the founder’s real skill here is balance: using AI as a genuine catalyst for speed and quality without losing the human in the loop

• Why this cost crisis balances the books with headcount - meaning AI is much less job threatening and the time for employees to lean in is now - to ensure they are part of the AI native thinking and org structure of tomorrow

• Why getting this balance right might let a new generation of founders build serious companies without the headcount their predecessors needed

• Where I think this settles, and why it could end up healthier for everyone — including the graduates and early-career people the current system has been squeezing out

The bill that went viral

Marty Kausas, co-founder and CEO of Pylon, posted that his company’s Anthropic bill was about to jump from $400,000 to $1.4 million a year. At this point we can see that mature AI adoption is rapidly becoming a real cost line to the business and therefore needs addressing differently to the AI 1.0 ‘adoption era’

The bit that grabbed my attention was that this bill leap was not because usage exploded.

It was because Pylon was about to cross 150 seats, the threshold at which Anthropic moves customers onto enterprise pricing and switches off the subsidised token rates that came with the smaller plan. Every token, overnight, priced at full API rate.

The post went viral for a reason that has nothing to do with schadenfreude. It resonated because almost every founder scaling headcount right now is sitting somewhere on the same curve and doesn’t know exactly where the trap door is.

Kyle Poyar’s follow-up piece on Growth Unhinged is the best breakdown I’ve read of what happens next: engineering burning roughly £3,100 a head a month equating to roughly the cost of a paired junior developer working alongside each team member (hence the second payroll aspect to this thought experiment), support running an AI-driven investigation on every single ticket at real inference cost (which may have been cheaper with junior employees), marketing spending hundreds a head on copy that, in Marty’s own words, still isn’t very good (AI slop costing real money to produce when a skilled copywriter could have nailed it better). He talked to CFOs at Vanta, ClickUp and SentinelOne for that piece, and the pattern across all of them was the same — spend had climbed faster than anyone’s ability to say with confidence whether it was justified. Marty’s own line stuck with me: most people, including him, weren’t conscious of what they were spending. That has to change, he said. I’d go further. It has to change at founder level first, because everyone below that level is simply responding to the incentives the founder set.

Two cost lines, and the one we’ve been lucky to avoid

My first business ran on two major cost lines. Cloud computing, specifically the internet’s RTB ad calls representing programmatic advertising’s glutinous volumes of ad requests repressing ad opportunities, which at scale which now gets to 1 trillion + a day, is one of the most punishing bid-request economics in software. Everything else cost wise was a rounding error by comparison. Those two lines set the ceiling on how fast you could grow and how much margin you kept while doing it. It was a cost line that squeezed margin and starved people based investments which would have driven growth, and left the business reliant on VC money to scale.

In this AI 1.0 era of subsidisation, there’s still a window to build AI enabled business at artificially reduced costs, which has to be front of mind for any founder right now. The AI companies VC’s are sponsoring by proxy this wave of startups, and FunnelFuel is one of them

it won’t last though and then we’ll be back to what I faced previously, scaling up to cloud computing once the subsidy era was over

Here’s the thing about running a business against two structural cost lines like payroll + cloud (or AI): it isn’t something an employee experiences. An engineer sees the compute bill as a technical constraint to optimise around. A salesperson never sees it at all. The founder is the only person in the building who feels both lines simultaneously, who has to decide in real time which one absorbs the next unit of growth, and who carries the consequence if that call is wrong. That’s not a criticism of employees — it’s simply a different job. Founders hold the balance sheet in their head in a way nobody else in the org is structurally required to.

FunnelFuel has, deliberately and somewhat luckily, avoided that dynamic so far. We’re not running compute-heavy infrastructure at the core of the business model, and our biggest cost line by a distance is still people. AI is still subsidised. That’s helped us grow the way we have, from £1.2M in year one to £12.1M in three years and somewhere around £20m in year four, a business that made it to 33rd on the Sunday Times Fast Growth 100, without a second major cost line dragging on the model as we scaled. I’ve been quietly grateful for that every year we’ve grown. But I don’t think we get to keep it forever, and increasingly I think the founders who assume they will are the ones who’ll be caught out.

AI adoption is starting to look like a second payroll

Here’s the thought experiment. If AI tools genuinely drive the level of adoption and dependency that vendors and investors want them to — if Claude, or whichever frontier model your org standardises on, becomes as load-bearing to daily output as your people are — then AI spend stops behaving like a software subscription and starts behaving like a second payroll. It’s a starker choice to make

Payroll has a headcount lever, a comp band, a hiring plan, a budget owner, and a finance function that reviews it quarterly on principle, because founders and boards have spent decades building the discipline around it. It’s easier to manage

AI spend, for most fast-growing companies right now, has none of that scaffolding. It scales invisibly with adoption, sits several budget owners removed from anyone with headcount-level authority, and as per Marty’s own admission about a $4,000 weekend query he ran himself, even the CEO often doesn’t know what he’s spending until the invoice lands. As a founder, I would put myself squarely in that category too. I’m running around using Ai to prototype, fix problems, run thought experiments, iterate and to generally make myself more productive. I’m not, yet at least, thinking about the cost.

That’s the first version of the second payroll: shadow, unbudgeted, growing with usage rather than with a deliberate decision anyone actually made. The second version is worse, and it’s the one I think matters more for founders specifically. It’s what happens when the subsidy currently propping up frontier model economics goes away, and a cost line that felt like a convenience starts behaving like a second headcount plan nobody signed off on. A plan with no interview process, no comp band, and no natural ceiling except the vendor’s next pricing announcement.

The UK tax cliff edge, but with more ways round it

The UK income tax system has a genuinely brutal cliff edge. Earn over £100,000 and you start losing your personal allowance at 50p for every additional pound, creating an effective marginal rate north of 60% in a specific band, before it flattens out again above roughly £125,000. It’s not a smooth curve. It’s a trap door that a huge number of people don’t see coming until they’re standing on it, and the ones who do see it coming plan around it; pension contributions, salary sacrifice, timing of bonuses etc all need factoring months in advance, because that’s what a good accountant does for a client who’s about to cross a line.

Pylon’s 150-seat threshold is the AI-cost version of that trap door. Below it, subsidised usage. Above it, full enterprise API pricing, applied instantly, with no smoothing and, per Marty’s account, very little warning. Cross a headcount line that has nothing to do with how much value you’re actually getting from the tool, and the bill triples.

The one meaningful difference, and it’s worth dwelling on, because it’s the whole reason this is a founder problem rather than just a finance problem — is that unlike the UK tax system, there are genuinely more ways round the AI cliff edge. Creative accounting has not been eradicated from the AI game plan yet.

How may we do that?

You can split functions across providers so no single vendor sees your full seat count. You can route by task to cheaper models rather than defaulting everyone to the most expensive one. You can hold a team deliberately below a threshold, the way a small business sometimes holds revenue just under a VAT registration line, buying time to plan the transition properly rather than being forced into it. None of that is available to a UK taxpayer staring down the £100k trap. It is available to a founder who treats model spend with the same forward planning as a good accountant treats a tax year.

But “more ways round it” only helps the founders who are actually looking for the trap door before they hit it. Most aren’t, because right now this still feels like a subscription decision made once in procurement, not a structural bet that needs revisiting every time headcount moves.

The founder’s real job: finding the balance, not picking a side

This is where I think the conversation needs to move on from “control the cost” and toward something more interesting, because I don’t think the founder’s job here is defensive at all.

Used well, AI is a genuine catalyst. A catalyst for speed, for the sheer volume of output a small team can produce, and in the right hands, for quality that a leaner team couldn’t have matched a few years ago. I feel that directly. There are things happening at FunnelFuel right now, in how quickly we can turn a partnership brief into a working demo or a data model into a client-ready narrative, that simply weren’t possible with the headcount we had even eighteen months ago. That’s not a cost line to be managed down to zero. That’s leverage, and any founder who treats it purely as a risk to be contained is going to get out-built by one who treats it as a genuine unlock.

But leverage without a human anchor degrades fast, and this is the part that gets lost when the conversation stays purely financial. Pylon’s own experience is the clearest evidence of this: AI-written marketing copy that, by their CEO’s own admission, still isn’t very good, still repeats the same mistakes, month after month, regardless of spend. Sales tooling that got built, adopted enthusiastically, and then quietly abandoned once the novelty wore off, because it wasn’t good enough to survive contact with a real quota. Spend went up. Judgement didn’t improve to match it. That’s the failure mode a purely cost-driven response to this crisis will never fix, because it isn’t a pricing problem - it’s a problem of where the human stays in the loop and where they don’t.

So the founder’s actual job, as I see it, isn’t “adopt AI aggressively” or “control AI spend tightly.” It’s holding both at once: pushing hard enough that the business genuinely captures the speed and leverage on offer, while staying disciplined enough about where a person still needs to be doing the thinking that the second payroll doesn’t quietly become a worse version of the first one — expensive, sprawling, and short on judgement. That’s a much harder balance to strike than either extreme, and I think it’s going to separate the founders who compound advantage from the ones who either overspend blindly or under-adopt out of fear.

In my world - specialised B2B advertising, there is clearly a value in the person who has been ingratiated with the nuances of the industry which will not get exposed from a pure LLM model. However can that person work better with an LLM tag team buddy? yes I absolutely think they can. The model should blend lifting the best of the persons knowledge with the agility, critical thinking, document ceating, challenging aspects of an LLM.

Why this might be good news for the next generation of founders

Here’s the part of this I find genuinely exciting rather than just cautionary, and it’s the reason I don’t think this piece should read as a warning against AI adoption.

Every generation of founder before mine built against a headcount ceiling that was, in practice, non-negotiable. You needed a certain number of people to run support, to write the copy, to build the first version of the product, to chase the pipeline — and the businesses that won were often simply the ones who could afford to hire that headcount fastest. That’s the model I grew up in. Its the model that favoured the markets, like the USA, with better access to venture capital. It’s the model that gave us the two-cost-line problem I described earlier, where payroll and infrastructure both scaled roughly in step with ambition, and where the founders with the deepest pockets or the earliest funding round had a structural head start that had very little to do with the quality of their thinking.

A founder who gets the balance right; real leverage from AI, real judgement retained where it matters, isn’t just managing a second payroll well. They’re building a company that can credibly compete on ideas and execution quality rather than on how many people they could afford to hire in year one. That’s a genuinely different starting position for a founder than the one my generation had, and if enough people figure out how to do it properly, I think it changes who’s able to start a serious company at all. Not because AI replaces people, but because it removes some of the headcount-as-moat dynamic that’s shaped who gets to compete for the last twenty years. The founders worth watching over the next few years, I suspect, won’t be the ones who spent the most on AI or the ones who avoided it hardest — they’ll be the ones who worked out, earlier than everyone else, exactly where the leverage was real and exactly where a person still had to be in the room.

Where I think this settles

Here’s my speculation, and I want to flag clearly that this is speculation, not something I’d bet the business plan on.

I think the current AI pricing environment is a temporary subsidy, not a stable equilibrium - venture-funded model providers burning capital to win adoption before the enterprise pricing cliffs kick in, exactly as Pylon experienced. As that subsidy unwinds across the industry, I think a meaningful number of AI-native businesses end up looking a lot more like my first company: two major cost lines, cloud-and-compute (now AI credits rather than RTB calls) and payroll, both requiring genuine operating discipline, both capable of capping how fast you can grow profitably. The founders who’ve spent this window building the muscle to manage that rather than assuming the subsidy is the new normal are the ones who won’t be caught flat-footed when the pricing resets.

If that’s right, the leadership skill of the next few years isn’t “adopt AI aggressively” or “resist AI adoption.” It’s finding the right ongoing balance between headcount growth and AI spend as two competing claims on the same budget, which is precisely ClickUp’s CFO’s framing of zero-based budgeting, treating AI as an equal competitor for spend rather than a costless add-on sitting above the org chart.

And here’s the piece I think gets underweighted in most of the commentary I’ve read. If AI spend stops being artificially cheap relative to junior human labour, the economics of hiring graduates and early-career employees get more attractive again, not less. This pendulum will swing again. It may take some aggressive government intervention to aid it, such as employment cost subsidies for the young, or it may not, because it will normalise again.

A junior hire who can be taught to do a task reliably, who improves with tenure, and whose cost doesn’t triple overnight because your company crossed an arbitrary seat threshold, starts looking like better risk-adjusted spend than a heavily-subsidised AI workflow that might reprice 3.5x on a vendor’s timeline rather than yours. I’d genuinely like to see a version of the labour market that rebalances toward that — less distorted by a false economics window created by investor subsidy, more accretive to the graduates and early-career people who’ve had a genuinely hard few years finding their first foothold, and more welcoming to the next wave of founders who won’t need the headcount my generation did to prove a business works.

I don’t think we’re there yet. I think we’re still mid-subsidy, mid-FOMO, and mid-figuring-out where the real value is. But I’d rather start planning for the world where the subsidy ends, and where the balance between people and AI is the whole game, than get caught at seat 151 wondering what happened to the bill.

Where do you think this settles for your business - are you already managing AI spend like a second payroll with a founder’s discipline behind it, or still treating it like a subscription someone else will notice eventually?

Sources and further reading

Primary source

• Kyle Poyar, “The AI cost crisis is entirely self-inflicted (and Fable 5 just made it worse),” Growth Unhinged, July 8, 2026:

https://www.growthunhinged.com/p/the-ai-cost-crisis-is-entirely-self-inflicted-and-fable-5-just-made-it-worse

The viral post itself

• Marty Kausas (co-founder/CEO, Pylon), LinkedIn post on the Anthropic bill jump: https://www.linkedin.com/posts/martykausas_our-anthropic-bill-is-about-to-jump-from-activity-7470505066148352000-zqnt

On Anthropic’s enterprise pricing shift

• The Information, “Anthropic Changes Pricing, Bills Firms Based on AI Use Amid Compute Crunch” (April 2026): https://www.theinformation.com/articles/anthropic-changes-pricing-bill-firms-based-ai-use-amid-compute-crunch

On broader frontier model pricing dynamics

• WSJ, on OpenAI considering “drastic price cuts”: https://www.wsj.com/tech/ai/openai-considers-drastic-price-cuts-anticipating-war-for-users-with-anthropic-9b8c178e

• Developer’s Digest, Fable 5 cost-per-task analysis: https://www.developersdigest.tech/blog/claude-fable-5-pricing-cost-per-task-analysis

CFO commentary quoted/paraphrased in Kyle’s piece (and referenced in yours)

• John McCauley, CFO, Vanta

• Dan Zhang, CFO, ClickUp

• Sonalee Parekh, CFO, SentinelOne

Adjacent context referenced in the original piece (not directly used in yours, but worth knowing they’re there)

• Tesla’s $200/week AI spend cap: https://www.theinformation.com/articles/tesla-caps-employee-ai-spend-200-per-week-adoption-push

• Uber’s $1,500/employee/month cap: https://techcrunch.com/2026/06/02/uber-caps-employee-ai-spending-after-blowing-through-budget-in-four-months/

• Ramp’s AI spend intelligence launch: https://ramp.com/blog/trillion-dollar-ai-blindspot

Your own framing (no external source — original to your piece)

• The UK income tax cliff-edge analogy

• The “two cost lines” founder history and FunnelFuel comparison

• The founder-vs-employee mindset argument and the “next generation of founders” speculation

• Why “data intelligence” - a phrase getting a lot of air time at MadFest this week - actually means something specific, and why most of B2B programmatic doesn’t have it

• Why pre-canned, single-vendor segments deployed buy-side are the ceiling most B2B programmatic is quietly stuck under

• What a multi-signal, machine-learned approach to B2B audience construction actually looks like in practice

• Why “agentic” isn’t a buzzword here either - it’s arriving now, and it needs a fundamentally different data foundation to work

• How sell-side containerisation and auction pre-looks unlock B2B-relevant impressions your DSP currently can’t even see, opening up bidstream modelling to maximise what programmatic can deliver for you

• Why precision, not scale, is about to become the metric that separates winners from everyone still buying by volume

MadFest London ran this week under the banner “The Human Touch” — built explicitly as a reaction against generic, AI-slop marketing, and a push for something with more genuine intelligence behind it. According to my team whoa attended, data intelligence came up constantly on the stages and in the side conversations. Everyone’s using the phrase. My sense is, almost nobody defines it.

That’s a problem, because “data intelligence” is not a synonym for “more data,” and it isn’t a synonym for “AI.”

It’s a specific, technical claim: that a system understands the confidence level behind what it’s telling you, because it has reconciled multiple independent sources against each other rather than trusting any one of them at face value.

By that definition, most of what gets sold as B2B data intelligence today isn’t intelligence at all. It’s volume with a dashboard on top.

Here’s the belief I want to pick a fight with: that the frontier of B2B programmatic is a better deal ID.

It isn’t. A deal ID is a container. What’s inside it is what matters, and right now, what’s inside most of them is exactly what’s been inside them for five years — a single vendor’s pre-built segment, packaged up, given a shinier wrapper, and deployed sell-side as if that constitutes innovation. It doesn’t. It’s the same bland, off-the-shelf intent taxonomy everyone else is buying, just curated into a nicer box. Call it AI-enriched if you like. It’s still one signal, alone, dressed up. The absolute biggest and best potential win doing this is a bit more scale and a more hygienic starting point for the signal, both of which are great nudges forward, but they are not the quantum leap that we’re discussing here today

The industry has spent so long fighting over access to signal that it’s barely started fighting over quality of signal — over actual data intelligence, in the strict sense. That’s the fight that actually matters now, and it’s the one most vendors aren’t equipped for.

Its THE fight because the agentic promise of tomorrow is fast arriving today, and this changes the programmatic game from valuing scale towards lining up behind a currency of outcomes - and outcomes requires quality whereas the former rewarded volume [behind a data segment. This is a night to day shift

The single-vendor ceiling

Every B2B data vendor sells you their view of the world. Intent data from one provider. Firmographic scale from another. A technographic layer from a third. Bought individually, or bundled by a platform that’s really just reselling the same handful of underlying sources with a different UI on top.

The problem isn’t that this data is bad. Some of it is genuinely good, and pockets of these segments are usually highly accurate. The problem is that any single vendor’s signal, taken in isolation, is a proxy — a best guess at in-market behaviour, built on whatever data that one vendor happens to have visibility into. Deploy it on its own and you’re not finding the accounts most likely to buy. You’re finding the accounts that look most like the vendor’s training set.

That’s the bland segment. It’s not wrong, exactly. It’s just low-resolution, and low-resolution is no longer good enough.

There is also a mis-marriage of intentions. Any vendor selling their data on a cost per thousand (CPM) will of course be rewarded for delivering scale. The more scale they can give you, the more you can spend against it and the more revenue they generate. When the buyer (the advertiser or agency representing them) wants to deliver outcomes, they need precision.

Part of the data intelligence game is identifying the pockets of the very most accurate and fresh signal within each data vendor, and leveraging that, filling in the gaps with the very best of the other data partners in a giant venn diagram. This delivers a rare premise - scale AND accuracy. It also involves getting your hands dirty, signing lots of contracts, taking on lots of fixed costs and doing a lot of quite challenging work. The reward though is a much more accurate data graph

What data intelligence actually requires

Therefore the alternative isn’t “more data.” It’s a different relationship with the data you already have access to and it’s considerably harder to build than anything a single-vendor segment requires.

Take multiple signal sources — intent, firmographic, technographic, first-party behavioural, account-level exposure history — and instead of deploying any one of them at face value, machine learn across all of them simultaneously. This means building a weighted, probabilistic confidence layer that sits on top of the account graph: each signal is treated as an independent, imperfect estimator, cross-validated against every other signal for that account, with the model continuously re-weighting each source based on how well it has historically correlated with actual pipeline outcomes for that specific vertical, deal size, and buying-stage cohort.

An account showing Bombora-style topic surges and first-party high-value-action behaviour and a firmographic profile that matches your best closed-won cohort produces a compounding confidence score entirely unlike an account showing just one of those three in isolation, and that compounding, not any single input, is what data intelligence is actually referring to.

This is the actual gold-mining exercise. Not “which vendor has the most segments” but “which combination of signals, weighted and validated against each other via a continuously re-trained model, produces the highest-confidence account list, expressed as a probability rather than a binary in-segment/out-of-segment flag.” The output isn’t a segment. It’s a live model — one that gets sharper the more outcome data you feed back into it, because you’re measuring account-level pipeline progression against high-value actions, not proxying success from a click, and feeding that delta back upstream to re-calibrate the signal weightings themselves.

This is the part most “programmatic for B2B” content skips entirely, because building it requires the kind of infrastructure most vendors simply don’t have: a proprietary account graph to sit signals on top of, a machine learning layer to reconcile them against each other rather than deploying them one at a time, and enough closed-loop outcome data flowing back from CRM to actually validate the model rather than just assert it. Most vendors have one of those three. Very few have all three.

This also comes all the way back to the overarching premise that I have believed having spent 15+ years in this industry - programmatic and ‘adtech’ focussing on the bigger and easier budgets behind B2C and not the smaller and complex world of B2B. Therefore the appetite to get into this sort of stuff, even by some of the slightly more B2B leaning generalists in the space is often lacking.

Why this has to be ready for agentic, because agentic is arriving now

There’s a reason the multi-signal model matters more this year than it did two years ago. The agentic layer of B2B buying isn’t a 2027 prediction anymore. Buying committees are already using AI copilots to shape and narrow their options before a human ever fills out a form or clicks a display ad. That’s compressing the window in which traditional demand generation can influence a deal, and it’s making single-signal targeting even less reliable, because the behavioural exhaust of a buying committee doing research through a conversational interface (like reading websites) doesn’t look like the behavioural exhaust of 2022’s buyer journey.

An agentic future doesn’t reward scale. It rewards outcomes. It rewards the operator who can say, with genuine statistical confidence, which accounts are in-market right now — not which accounts merely resemble a segment definition written six months ago.

Volume-based targeting (big segments, spray and pray tactics) was always a hedge against uncertainty: buy wide because you can’t be precise. Multi-signal, machine-learned modelling is the alternative to that hedge. It’s precision replacing volume as the primary lever, because the underlying confidence in who’s actually in-market is finally high enough to justify it. This justifies data investment (CPMs) way above norms but rewards the buyer with a level of precision which matches the premise of programmatic - predicated on trading individual impressions across surfaces like TV, web, audio, one at a a time.

Where sell-side containerisation actually unlocks value

This is the part that doesn’t get discussed enough, because it sits on the supply side rather than the (traditional programmatic) sexier demand-side story.

Most B2B-relevant impressions never get bid on as B2B impressions. They pass through the exchange looking like general-audience inventory, because the signal that would identify the visitor as a business decision-maker either never reaches the DSP, or reaches it too late — after the auction the DSP could have won cheaply has already cleared.

Sell-side containerisation changes that. When account and audience signal is resolved at the auction, sell-side, before the bid request even reaches the DSP, you’re not relying on the DSP’s own identity resolution to catch a B2B-relevant impression on the way past. You’re getting an auction pre-look that says: this impression, in this moment, sits against an account on your target list — bid accordingly. That’s impression volume that would otherwise be functionally invisible to a B2B buyer, opened up specifically because the resolution work happened earlier in the chain, not later.

The practical effect is bidding accuracy that a pure demand-side approach can’t replicate, because by the time signal resolution happens demand-side, a meaningful share of the addressable opportunity has already been priced, won by someone else, or simply missed. Containerisation sell-side, combined with the multi-signal confidence model on the account graph, is what actually overcomes the structural breaking points B2B programmatic has lived with for years — signal loss, buying-committee complexity, and DSPs that were never built to resolve B2B identity in the first place.

The position

Scale was always a substitute for confidence. If you can’t be sure which accounts are in-market, you buy wide and hope volume compensates. That’s the model most of the industry is still running, dressed up in curated deal IDs, badged with “AI,” and marketed as data intelligence when it’s really just data volume with better branding.

The actual shift — the one that’s ready for what’s arriving, not what already happened — is high-confidence, multi-signal account models built and validated through machine learning, deployed against impressions unlocked earlier in the chain than most B2B buyers currently reach. That’s not an incremental improvement on the bland segment. It’s a different category of infrastructure entirely, and it’s the only thing that deserves the “data intelligence” label MadFest spent three days using loosely.

The vendors still selling single-source segments aren’t going to disappear. But they’re going to look increasingly like the browser toolbar of B2B adtech — familiar, comfortable, and quietly irrelevant to where the value actually sits.

What’s your honest read; is your current stack built on data intelligence, or is it still just buying scale and calling it strategy?

Who am I?

I’m the co-founder of Funnelfuel.io, we have invested millions of dollars into building what I have been talking about today. If you work for an advertiser or agency which would value our approach to solving complex B2B pain points like data accuracy, data scale, reporting, insights and signal based autonomous ABM - simple reply to this newsletter, it gos straight to my inbox or email me at mike@funnelfuel.io. You can follow me on Substack and LinkedIn here

What you’ll learn in this newsletter:

• How Cannes Lions has shifted from a loosely structured European networking event to a far more corporate, US-dominated affair

• Why the infrastructure of the event has changed, restaurants, yachts, apartments, and what that means practically

• Why B2B is no longer just background noise at Cannes, and what that shift signals

• The practical tips that will make or break your 2027 experience if you’re planning to go

• Why being present at Cannes as a B2B vendor or agency is worth the investment — and how to maximise it

I last attended Cannes Lions in 2019. I’d been going consistently for seven or eight years before that. In June this year I went back, making a last minute decision having had one or two very large meeting requests. On the back of a week in NYC, it was a stretch, but I am very glad I did bite the bullet.

Seven years is a long time in adtech. It turns out it’s also a long time in the south of France.

The Cannes I returned to in 2026 is a fundamentally different event to the one I left. Not in location, not in weather (still 35 degrees, still punishing), and not in the volume of rosé consumed (equally if not more so punishing). But in character, in structure, and increasingly, in who is setting the agenda, it’s changed substantially. For those of us who operate in B2B, that shift is actually quite good news.

From linen and flip-flops to long sleeves and structured agendas

The Cannes of the mid-2010s had a particular energy. It was informal by design. Relationships were built in restaurants along the Croisette, in bars that you’d migrate between, in the kind of loosely arranged lunches where the meeting itself was secondary to the ambient networking. La Californie — a restaurant I spent a lot of time in across those years — was the kind of place you’d walk past and end up staying for three hours.

That La Californie no longer exists in the same form. This year it was the iHeart Media café: fully rebranded, blocked to the public, operating as an appointment-only meeting space. That’s not a one-off story. Similar things happened to a number of the traditional British adtech haunts — including Roma, the go-to for years among the UK ad tech crowd — which were absorbed or transformed by brand takeovers, in their case by F1 which branded it as ‘the Paddock bar’

The event has, in short, been Americanised. I mean that in a specific and not unkind way. There was a visible and significant US corporate presence in 2026 that wasn’t there in anything like this form in 2019. C-suite and president-level executives from major American companies were present at scale, and they brought their culture with them. Multiple people mentioned brands that had instructed their travelling party to wear long-sleeve shirts and trousers even in 38-degree heat. Apartments off the Croisette became proper meeting rooms — air-conditioned, structured, scheduled.

What this practically means is that the old Cannes playbook — show up, hover around the usual spots, let conversations happen — doesn’t really work anymore. The restaurants you might have relied on as meeting points have been annexed. The ambient collisions are harder to engineer. The event now runs more like a very well-located conference with a lot of private sides to it.

The yachts have changed hands

When I was last there regularly, the yacht presence was dominated by UK publishing groups. The Daily Mail at its DMG Media peak. News Corp’s Sun. That era had a particular character — big media owner hospitality, trade press dinners, an Anglocentric flavour to the top table conversations.

That has shifted. In 2026 you had the Mercedes-Benz Formula One team. HBO operating under the White Lotus banner. Formula One as an entity was almost strangely prevalent given it’s not an advertising platform - from the paddock bar to the boats. The big media-only publishing groups have largely receded from the yacht and prime hospitality tier. What replaced them is a more diverse, more entertainment-weighted, more American set of presences that probably reflects changing media preferences and the changing fabric of the addressable internet - from banners on news sites to television and podcasts.

The adtech layer is still there — particularly the bigger network-facing platforms — but the heyday of UK publishing groups throwing lavish hospitality at the front of the festival feels like it’s passed.

B2B is finally leaning in

Here’s what I think is the more important story for this newsletter’s audience.

B2B’s presence at Cannes has grown meaningfully. LinkedIn in particular had a significantly larger and more visible presence than I’d seen in prior years. When I was attending regularly, LinkedIn was a quiet few boots on the ground stretching to maybe a small apartment presence. By 2019 they’d grown a little. In 2026 they were actively leading conversations, not just attending them.

The official Lions B2B programme at this year’s festival reflects a genuine industry shift — B2B marketing has moved beyond traditional lead generation, and brands are increasingly being challenged to connect creativity with measurable business outcomes while elevating marketing’s influence at boardroom level.

For a long time, B2B marketing was treated as more rational, more product-led, more technical, and somehow less creative than consumer marketing. That assumption has been breaking down, and the presence of dedicated B2B programming at Cannes is a meaningful signal that the industry now sees B2B creativity as a growth area rather than a niche exception.

From the conversations I had there — with B2B vendors, agencies, and platform teams — there’s a genuine appetite for the kind of B2B-specific discussions about supply path, signal infrastructure, and audience architecture that would have felt niche or premature at Cannes five years ago. That wasn’t happening on the main stage necessarily, but it was happening in the apartments. Below is the very plush Index Exchange apartment which proved a real asset for them and their meetings

This matters. Cannes is where deals get structured, partnerships get seeded, and commercial relationships shift from transactional to strategic. If B2B as a category is now actively represented in that conversation, that’s access to a room that was previously more difficult to get into.

The practical reality: what works and what doesn’t in 2026

I’m going to be direct about the operational side because I think it’s genuinely useful for anyone planning to go in 2027.

WhatsApp is the operating system. Everything runs through it. Dropping pins for locations. Rearranging meetings that moved because someone’s dinner ran long the night before. Getting a last-minute message from someone who’s just landed and has a gap in their day. If you’re not on WhatsApp and actively monitoring it, you will miss things. One thing I noticed: UK phone signal in Cannes can be patchy. Email was frequently unreliable for me. WhatsApp, being lighter on the protocol, was more resilient. Lean into it.

Best-laid plans don’t survive the first morning. The ideal Cannes schedule is one that has structure in the form of confirmed anchor meetings and genuine flexibility around everything else. Early morning meetings in particular are at risk — people who got into the Rosé a bit earlier than planned the night before don’t always materialise at 8am as intended. Build in buffer and don’t over-schedule the first half of each day.

Marketing cut-through is very tough. From postering big boats to 20m euro apartments, to big events, to villa parties, to half day boat cruises - getting seen and heard is tough out there. Kargo’s drone show was quite spectacular and caught my attention, and Snapchat were smart to sponsor the bus-train which gets plenty of use. There’s definitely room for some guerrilla like tactics if your Cannes marketing budget doesn’t stretch into the 7 figures

Accommodation matters more than you think. I booked late this year — reactive decision to attend, no regrets, but I ended up two miles down the coast. That’s manageable if you’re willing to walk in the morning (I was, in the heat) but getting back became a constant logistical consideration. Uber was unreliable in the mornings heading in, somewhat better in the evenings heading out. Colleagues who had booked hotels and apartments two or three miles out were reporting real friction — too hot to walk, too unreliable to Uber, no gap between end of meetings and evening events to freshen up.

The closer to the centre you are, the more you can operate at the pace the event actually demands. If you’re planning for 2027: book early, be wary of Airbnbs that can be cancelled at late notice, and try to establish a direct relationship with the property.

An apartment beats a hotel room. If you can get a well-located apartment rather than a standard hotel room, do it. The ability to have meetings in an air-conditioned space you control — rather than fighting for a corner of a packed restaurant bar — is a genuine competitive advantage. If you’re negotiating something significant, doing it in a space that feels like your turf rather than a neutral or even adverse venue changes the dynamic in ways that are hard to quantify but easy to feel.

The window between end of day and evening events is gold. Cannes runs long — dinners, parties, yacht events go late. If you’re in a good location, a 45-minute reset between the afternoon’s meetings and the evening’s commitments makes a meaningful difference to how you show up across the week. Lose that window to a complicated commute and you’ll feel it by day three. By Thursday I was anti-social and ready to go home, as I had no breaks all week.

Taking an hour out of the action to stretch my legs up the coast - taking in the wider benefits of a trip to the French Riviera in summer

What the broader themes mean for B2B marketers specifically

AI dominated the conversation at Cannes Lions 2026, but the discussion had matured. The debate has shifted from whether AI threatens creativity to how to work with technology moving forward. For B2B, that’s actually quite a useful inflection point. The question of whether AI belongs in B2B pipeline and intent infrastructure isn’t really being debated anymore — the more interesting conversations were about which AI-native capabilities are actually differentiated versus what’s table stakes.

The other theme worth noting for B2B audiences: the growing role of signal infrastructure in understanding and reaching professional audiences. The conversations I was having — around intent data, account scoring, supply path construction, contextual B2B targeting — felt more mainstream than they would have done even three years ago. The adtech community is increasingly literate on B2B-specific challenges.

If you’re a B2B marketer or an agency that operates in B2B, Cannes is worth your time in a way it probably wasn’t five or six years ago. The infrastructure has shifted, the US corporate weight is significant, and the B2B-specific programming is now real. The question isn’t whether to go — it’s how to go effectively.

Five things to do differently in 2027

1. Book accommodation NOW for 2027, or in March at the latest. Central, professional rental (not a consumer Airbnb you can’t rely on), within a mile of the Palais. The difference this makes to your ability to operate is disproportionate to the inconvenience of booking early.

2. Plan for an apartment to host in. If your budget allows, a space you can offer as a meeting room for others is one of the highest-leverage moves at the event. Being the place people come to, rather than the person navigating to someone else’s territory, shifts the meeting dynamic. It is perfectly acceptable to share out there - even the biggest yachts from the best of the best often sub-lease their top decks. Apartments get turned into co-working meeting suites. You do not need to eat the whole cost

3. Anchor your schedule, don’t fill it. Three or four confirmed, high-value meetings per day is better than eight that you’re scrambling to hold together. Build around the anchors. Cannes is a quality over quantity play for the partners that could go strategic

4. Get on WhatsApp groups early. The informal networks that form ahead of Cannes — people sharing who’s going, setting up group threads — are where the ambient intelligence lives. Being in those conversations before you land is worth more than any scheduled meeting.

5. Use it for supply chain relationship building, not just awareness. The people you need to have strategic conversations with about programmatic infrastructure, data partnerships, and platform access are all in a small area for five days. A relationship you build in that context often moves faster than twelve months of Zoom calls.

B2B at Cannes in 2026 felt like a category finally arriving. It’s noisier than it was, more competitive for space and attention, and the logistics are harder than the folklore suggests. But the upside — access to senior decision-makers, the ability to have strategic conversations in a compressed timeframe, the signal you send by being there — is v real.

If you’re building a B2B brand, an agency offering, or a platform business in adtech, I’d make a plan to be there in 2027. Just book the accommodation early.

More from The B2B Stack coming your way over the next few weeks — there’s a lot to cover from H1. Thanks for reading.

This weekend, My business FunnelFuel.io landed at number 33 on the Sunday Times 100, Britain’s fastest-growing (and profitable) private companies. Fifth year of the list, topped this year by Goalhanger, the podcast outfit behind The Rest Is History, on the back of 321 per cent average annual growth. Scan the rest of the table and it is mostly what you would expect: celebrity-backed drinks, challenger beauty, food brands you have seen on a tube poster. Consumer names now make up close to half the list

So a B2B programmatic managed service turning up at 33 is, frankly, a bit of an oddity - an one we are very proud of.

Part of the reason is that the Sunday Times runs pure-technology companies on a separate table, the Sunday Times 100 Tech, which means the main list rewards trading businesses growing real, audited revenue rather than software multiples and a funding round. We got there on sustained sales growth, built with very little outside capital, and at a time where tech businesses are burning credits and cash 24/7, we got there profitably. That last part is the bit I am quietly proud of. Plenty of adtech grows fast by setting fire to someone else’s money. Anyway. That is enough of that.

Onto the last week, which was a good one for anyone who actually cares where their media decisions get made.

What you’ll learn in this article

• Why PubMatic launching a containerisation layer on 1 June is bigger than it looks, and what “decisioning in the supply path” actually means for your campaigns

• What Omnicom telling its buyers to go direct to publishers tells you about supply-path optimisation in 2026

• Why the discovery surface for B2B buyers is shifting under your feet, and what Google’s Universal Cart and OpenAI’s new ad hires signal

• The one question to take into your next media review off the back of all three

Decisioning is moving into the pipes

The story of the week is dull-sounding and yet very important. On 1 June, PubMatic launched a containerisation layer it calls Decision Fabric, built on the AgenticOS standard, with partners including MiQ, Chalice, inPowered and SWYM able to run their own decisioning logic inside it (Digiday).

If “containerisation” hasn’t reached your team yet, here is the plain version. For most of programmatic’s life, the clever part of a campaign, who to bid on, at what price, against which signal, happened inside the demand-side platform. The DSP was the brain. Containerisation flips that. It lets a buyer, a curator or a data company drop its own decisioning logic into a container that runs further down the pipe, on the supply side, closer to the inventory. The brain moves out of the DSP and into the plumbing. The unlocks that follow include cost savings and efficiency upgrades which have been estimated by Index Exchange - the pioneers of this - at 75-100x, thrashing their own initial estimates of one order of magnitude and gaining nearer ten orders. Efficincy, carbon, cloud costs, niche audience unlocks, genuine supply path optimisation and cleaner/stronger signals all play their roles. This is the future of programmatic 2.0

PubMatic is not first here. AgenticOS has been gathering partners for months, and this is the supply side formalising a shift that the buy side has been circling since the back end of last year. What changed this week is that it stopped being a concept deck and became a product you can point partners at.

Why this matters if you run B2B paid media: the value in B2B has never been the bidding - which is vanilla programmatic 101 - it has instead been the targeting logic. Knowing that this set of accounts, showing this pattern of intent, sitting at this stage of a buying committee’s journey, is worth paying up for. If that logic can now live in the supply path rather than being flattened into a generic DSP audience, the people who own genuinely differentiated decisioning get to apply it much closer to the impression. The flip side is the risk: if you don’t own your decisioning, you are renting someone else’s, and you are now renting it in more places. The question to ask any partner pitching you “AI-optimised” anything in 2026 is a boring one. Whose logic is in the container, and can you see it?

Omnicom would like fewer hands in the pipe

In the same beat, two supply-side sources told Digiday that buyers inside Omnicom are being pushed hard to route more spend straight to publishers rather than through the usual stack of intermediaries (Digiday). The pendulum beings to swing again, from supply platform dominance over demand side power pays, to agencies who were in danger 18 months ago of becoming relics.

This is supply-path optimisation, SPO, with the gloves off. The holding companies have spent two years trimming the number of hops between a buyer’s budget and a publisher’s page, partly for cost, partly because every hop is a place fraud and made-for-advertising inventory hides. And realistically because every third party hop can become a first party - for the agency - revenue grab, to make up for their eroding media margins. Jounce Media still has roughly 15 per cent of open-exchange impressions landing on MFA sites. A shorter path is a cleaner path, and should work harder for B2B vendors.

The B2B angle is uncomfortable and worth sitting with. B2B campaigns run on smaller, more precise audiences than consumer, which means we are more exposed to the tax of a long supply path, not less. A consumer brand buying broad reach can absorb a few wasted hops. A B2B advertiser paying a premium to reach 4,000 named accounts cannot, because every dollar lost to an intermediary or an MFA arbitrage is a dollar that was meant to reach a finance director at a target account and didn’t. If you have never asked your agency or your managed service to show you the path your money takes from your card to the publisher, this is the week to put it on the agenda. The honest ones will have the answer ready.

The buyer found a new front door, and it isn’t your website

Now the lighter one, though it stops being light if you think about it for more than a minute.

Three things happened in the last fortnight that point the same way. Google unveiled Universal Cart, a shopping cart that follows a person across Search, Gemini, YouTube and Gmail and lets Gemini hunt down deals before checkout. Less useful for B2B, but this is generally super interesting from Google.

OpenAI posted for a head of enterprise marketing for its ads business, which is not the job listing of a company dabbling. And supplement brand Olly started rewriting its product pages, not for Google’s crawler, but so that AI chatbots describe it accurately when a shopper asks for a recommendation (Digiday, Digiday).

Consumer stories, on the face of it. But the behaviour underneath is exactly the behaviour we have been documenting on the B2B side for two years. Buyers research independently and arrive late. The repeated finding is that B2B buyers are something like 70 per cent of the way through a purchase before they speak to a vendor, and spend a sliver of their total buying time actually with suppliers. The new wrinkle is that a growing share of that independent research now happens inside an LLM rather than on a search results page or your carefully optimised website. This is a behaviour change that spans personal and professional lives, so shouldn’t be ringfenced into B2B or B2C, its changing behaviours behind vast cohorts of people.

That should change where some of your effort goes. If a procurement lead asks ChatGPT or Gemini to shortlist vendors in your category, the deciding factor is whether the model has been fed an accurate, well-structured picture of what you do, not whether your homepage hero animation is on brand. Olly worked this out for collagen gummies. The B2B version is making sure your category, your differentiators and your proof points are legible to a machine that has never seen your pitch deck. Call it GEO, call it AIO, call it housekeeping. It is becoming table stakes.

The thread tying all three together

Notice what these have in common. Decisioning sliding out of the DSP and into the supply path. Spend being pushed past the intermediaries to the source. Discovery moving from the open web into AI assistants. In every case, value is migrating closer to the edges and away from the middle layers that used to capture it by default.

For a B2B advertiser the practical takeaway is the same in all three. Own the part that is actually yours. Your account intelligence, your decisioning logic, your understanding of the buying committee. Be ruthless about the parts that are just toll booths. That is the line we have organised FunnelFuel around for years, which is presumably some of why this week went the way it did, but the principle holds whoever you work with.

One question to take into your next media review: of every dollar you spend on B2B paid media, how much is working media reaching a real target account, and how much is paying for the privilege of getting there? If nobody in the room can answer, that is the answer. Those dollars work hardest when they unlock the strongest vectorised targeting AND the strongest insight and reporting payloads back - when you get both, your marketing investment pays for insights that justify its place in the CFO’s budgets, when either slips, the investment case weakens and budgets follow

That is the week. If you want the longer version of any of this, or you are wrestling with the supply-path question on a live budget, just hit reply. It comes straight to me and I read them all.

Until Friday.

Mike

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Here is a fact most marketers get backwards. When a visitor clicks “Reject all” on a cookie banner today, the ads do not stop. The publisher can still fill the slot. What stops is the signal. The programmatic machinery that decides what the impression is worth goes dark, the bid request arrives stripped of almost everything that gives it value, and a piece of premium inventory that should clear at a healthy price gets sold for pennies or goes unsold entirely.

Everybody loses in this scenario;

1. Publishers and media creators go unpaid - with independent content getting defunded and let down by the operating model of the internet being rug pulled. This leads to publishers setting more ad placements, which are more detrimental to the user experience, which in turn puts more people off going to their site/s and creates a vortex like vicious circle of decline

2. Advertisers miss the chance to buy high value account based impressions in premium content environments, where intent is forged and fortified. These are the exact environments which command the upper end of B2B CPMs, and they go un-auctioned

3. End users, with our B2B lens on, often buying committee members in global accounts see garbage bottom-filler ads which detract from the user experience and are a distracting irrelevance, pushing them away from premium sites and spending less time in the open internet.

That distinction, between full-throttle data enabled monetisation and blocked signal, matters because it explains the slow bleed that premium publishers have lived through since regulators forced “Reject all” to sit level with “Accept all”. The audience did not disappear. The ability to monetise it did.

Privacy and AI have been the bedfellows of doom for the publishing world, so any walk-back on the incredibly stringent rules is potentially the most significant development since privacy rules were introduced with GDPR - because it is the first signs of a reversal in its direction of travel.

On 18 May 2026 the Information Commissioner’s Office published formal advice to government proposing that a defined set of low-risk advertising functions could run without consent under PECR. Of all the privacy laws, PECR is the most arduous, and a strict interpretation of it would essentially render programmatic advertising as an industry as illegal.

This new formal advice went out, unglamorously, in a letter to a digital minister and a Treasury secretary, and most of the trade press filed it under compliance housekeeping. I think that is a mistake. Strip away the legalese and this is the first time in roughly a decade that a major Western privacy regulator has moved deliberately to widen, rather than narrow, what addressable advertising is allowed to do. The ratchet that only ever turned one way just slipped a tooth.

This piece is about why that slip is a bigger deal for B2B than for anyone else, and what it signals about the direction of travel for privacy itself.

What you’ll learn in this article

• Why premium publishers, the lifeblood of the open web, have been squeezed from two directions at once, and the numbers behind the squeeze

• Exactly what the ICO has proposed, the seven functions it would exempt, and the narrow set of signals that could flow without consent

• The category error at the heart of PECR: why the rules taxed anonymous context and individual surveillance at the same rate

• Why B2B has the most to gain, given an audience that is overwhelmingly anonymous, out-of-market and company-level

• Which specific B2B-relevant signals could become addressable without consent, and where the hard limits sit

• What the move tells us about a privacy movement that, until now, has only known one direction

The two-front squeeze on the open web

Premium content publishers are the substrate the open web is built on. They are also the part of the ecosystem that has taken the heaviest fire over the last five years, from two directions that arrived more or less simultaneously.

The first front is privacy compliance. Once cookie banners were forced to offer a genuine, equally weighted rejection, people used it. The exact rejection rate is contested and varies wildly by banner design and sector, which is itself telling, but the direction is not in doubt.

UK publisher data collected by Press Gazette puts consent rates somewhere around 70 to 80 percent when accept and reject are presented evenly, so a fifth to a third of visitors opting out.

Rhe above is for the UK only, and the data I have seen, aggregated across billions of B2B vendor and publisher page loads captured by the FunnelFuel analytics, makes for much bleaker reading. My data is more in-line with broader academic and industry reviews find that with a properly presented “Reject all” button, something closer to half to two-thirds of users now decline, and that a compliant banner can mean 40 to 70 percent fewer tracking data points. German analytics firm etracker reports that a legally compliant design sees consent refused on around 60 percent of visits. Whatever the precise figure on a given site, a large and growing slice of premium audience became, in programmatic terms, invisible.

The industry’s workaround, consent or pay, tells you how acute the pain became. Sixteen of the fifty biggest UK news sites now ask users to either accept tracking or pay a subscription, and once that choice is introduced almost everyone accepts. It creates 2 problems though; one - according to Content Ignite, this creates a negative sentiment amongst users, and more concerningly, the ICO has confirmed those models can be lawful there’s clearly an interpretation that it is in-fact unlawful to do this. It is a revealing fix. Publishers were effectively charging users for the privilege of declining to be tracked, because the alternative was watching half their inventory lose its value. I am all for it, and 76% of users agree with me that publishers deserve to get paid for their work (see Content Ignite link above)

The second front that publishers are fighting is the collapse of the referral economy itself. While privacy rules were thinning the value of each impression, AI-driven search was thinning the number of impressions. Zero-click search, where a user gets the answer and never leaves the results page, now accounts for roughly 60 percent of Google queries, and on news-related searches Similarweb data puts it at 69 percent in the year after AI Overviews launched. The same analysis found Google search traffic to publishers fell about a third globally in the year to November 2025. Pew Research found that when an AI Overview appears, only 8 percent of users click a traditional result, against 15 percent when it does not, and barely 1 percent click the sources cited inside the answer. Ahrefs measured a 58 percent fall in click-through for top-ranking pages on queries that trigger an Overview. Publishers have been unusually candid about losing 20, 30, in some cases 90 percent of their traffic, and some have already closed.

Now hold those two fronts next to a third structural fact: the money was already concentrating elsewhere. Walled gardens capture 70 to 80 percent of programmatic spend and around 78 percent of global digital ad revenue, a share Statista expects to reach 83 percent by 2027. Yet people spend more than half their online time on the open web - and I have long sinced argued that advertising investments over-value walled gardens and under-value the cheaper and more plentiful attention that still exists in the open web environments, all the more so when you factor new facets of it, from podcasts to TV. Advertisers put only 20 to 30 percent of budget where audiences spend 55 to 60 percent of their attention. The squeeze on premium publishers, in other words, is not a market correcting. It is a market being pushed further out of balance by rules that made the open web harder to monetise at exactly the moment AI made it harder to reach.

That is the backdrop against which the ICO’s letter should be read.

What the ICO actually proposed

The advice concerns regulation 6 of PECR, the rule that requires consent before anything is stored on, or read from, a user’s device for advertising. It is the legal basis for the cookie banner. The ICO’s central argument, set out across its report and cost-benefit analysis, is that regulation 6 applies a single, blunt standard across a vast spectrum of activity, from behavioural profiling that follows an individual across dozens of sites, to a contextual ad that shows a cycling product to someone reading about cycling. Treating those as equivalent, the regulator now says, may be suppressing investment in the lower-risk approaches everyone claims to want.

So the ICO proposes what it calls a first-party framework. Under it, as the legal analysis from Lewis Silkin sets out, a publisher could store and access limited information on a device, without consent, for seven defined purposes:

1. Ad delivery

2. Targeting, in a limited form

3. Measurement and billing

4. Attribution

5. Frequency capping

6. Brand safety

7. Ad fraud prevention and detection

The word doing the heavy lifting is “limited” and probably really ‘too limiting’, and the targeting boundary is where the real thinking lives. Without consent, targeting could draw only on high-level device and platform information such as device type, operating system and browser but not browser version; geolocation abstracted to city or region level; the date and time of day; and contextual information, meaning the content the user is looking at right now mapped to a broad taxonomy like “sports” or “cycling”. Everything that constitutes following a person stays behind the consent wall. ID-based targeting that reaches the same user across sites or devices, demographic and interest-based segmentation, and behavioural profiling of any kind would all still require opt-in. For B2B we’re probably talking context and location together to form some signal

Three things are worth being honest about. First, nothing has changed yet. This is the ICO’s independent advice to support possible secondary legislation under the Data (Use and Access) Act 2025. The existing rules still apply, and will until Parliament acts. Second, the ICO itself is modest about the scale, conceding the approach would not revolutionise the ecosystem and that the benefit would be strongest for the mid- and base-tier publishers most constrained by consent rejection, and close to neutral for the large platforms already swimming in consented data. Third, the review was not a back-room favour to adtech. It ran from January 2025, took in a public call for views, citizen juries and user research, and the user research found that contextual approaches actually align with what people expect and are comfortable with. That last point is the centre of gravity. The regulator is not loosening rules against the public mood. It is arguing that the rules drifted out of step with it.

The category error at the heart of the rules

Here is the thing the trade-press housekeeping framing misses. Regulation 6 was never really a rule about privacy outcomes. It is a rule about a mechanism, the act of writing to or reading from a device, and it is almost completely agnostic about what that act is in service of. Drop a cookie to follow someone across the web and build a behavioural dossier, and you need consent. Read a signal to cap how many times an ad shows so you do not annoy the same reader six times, and you need the same consent. The law taxed surveillance and good housekeeping at an identical rate to the heaviest data enabled targeting possible

That is a category error, and the ICO has effectively just admitted it. Privacy risk is not a property of touching the device. It is a property of what you do with what you learn. Reaching a person you have profiled across the open web is high risk. Showing a relevant ad based on the article in front of them, capping its frequency and checking it is not fraudulent, is not. For most of the last decade we have regulated those as if they were the same act, and then expressed surprise that the privacy-preserving alternatives never got built. They never got built because the rules gave them no advantage. Why invest in contextual when contextual needs consent too, and consent is the scarce resource? if you have consent then you will unleash the full gambit of targeting, and contextual footprint and past behaviour is already priced in (assuming you know what you’re doing with B2B programmatic, and doing it optimally).

This is the part that should make B2B marketers sit up, because B2B has been collateral damage in a war that was largely aimed at consumer surveillance. The privacy regime was designed around the individual data subject, the named person whose behaviour is tracked, profiled and sold. That is a coherent target in a consumer context. It maps very poorly onto how B2B advertising actually works, where the unit that matters is not a person at all. It is an account.

Why B2B has the most to gain

Step back and look at what the B2B audience really is. It is, structurally, anonymous, out-of-market and collective.

Anonymous, because the modern B2B buyer does almost everything before they will identify themselves. Across the research the pattern is remarkably stable: buyers complete somewhere between 60 and 70 percent of their journey before they ever contact a vendor, McKinsey puts two-thirds of the process in digital self-service, and Gartner finds most buyers now prefer a rep-free experience and spend only around 17 percent of their buying time with any potential supplier at all. The dark funnel is not a marketing metaphor. It is the literal majority of the buying journey, conducted anonymously, on exactly the premium open-web content that consent rejection turned dark.

Out-of-market, because of the 95:5 rule drawn from the Ehrenberg-Bass Institute’s work for the LinkedIn B2B Institute: at any given moment up to 95 percent of your potential buyers are not in the market, with perhaps 5 percent active in a given quarter given multi-year purchase cycles. Effective B2B advertising therefore spends most of its life reaching people who will not buy for months or years, building the memory structures that pay off later. That is a reach-and-context game, not a chase-the-individual game.

And collective, because B2B decisions are made by buying groups, not buyers. Estimates of group size have crept up to ten, fifteen or more stakeholders on a serious purchase, with security, finance and operations all running in parallel. Forrester’s work suggests most buyers have effectively chosen a winner before talking to sales, and that the great majority of purchases stall in the process. You cannot win a fifteen-person committee by retargeting one cookie. You win it by being present, in relevant context, across the whole account, for a long time.

Now read that back against the signals the ICO would free up without consent: the content someone is reading, the broad category it sits in, the region they are in, the device they are on, the time of day, plus the ability to cap frequency, measure and attribute. For a consumer brand chasing a named individual, that is a thin gruel, which is exactly why the ICO says it will not revolutionise things for the big platforms. For B2B, it is close to the whole meal. We often are not trying to identify the person. We were trying to reach the right kind of organisation, in the right context, often enough to be remembered, and prove it moved pipeline. The permitted signals map almost perfectly onto the way good B2B media already thinks.

This is the inversion worth sitting with. The exemption looks minor through a consumer lens and transformational through a B2B one, precisely because B2B addressability was already built on context and the account rather than the surveilled individual. The rules squeezed us anyway, because they could not tell the difference.

Which B2B signals could become addressable without consent

Let me get concrete, because this is where strategy lives. Translate the ICO’s permitted signals into B2B terms and you get a usable layer for reaching the anonymous, non-consented majority.

Contextual taxonomy as a proxy for category intent. The content a reader is consuming, mapped to a broad taxonomy, is the single most useful permitted signal for B2B. Someone reading a deep piece on data warehousing, supply chain risk or martech consolidation is signalling category relevance regardless of who they are. Contextual was always the most under-rated B2B tactic. A framework that lets it run on the half of premium audiences who reject consent changes its reach maths entirely.

Region as a proxy for market and footprint. Geolocation abstracted to city or region will not name an account, but it lets you weight toward the markets, territories and economic clusters that matter, the financial districts, the tech corridors, the industrial regions, without touching anyone’s identity.

Device, platform and time as workday context. Device type, operating system and time of day are mundane on their own. In aggregate they describe behaviour that is recognisably professional: desktop and managed-device traffic, in working hours, in business contexts. It is a coarse filter, but coarse filters applied to the right inventory are how B2B reach has always been built efficiently.

Frequency capping across the committee. This one is quietly important. If you are trying to build presence across a ten-person buying group over months, the ability to manage exposure without consent means you can be consistently present without being a nuisance, on the very audience that previously fell out of the addressable pool the moment they opted out.

Measurement and attribution on the dark funnel. Perhaps the most valuable permission of all is the boring-sounding pair, measurement and attribution. The hardest problem in B2B is proving that anonymous, top-of-funnel, out-of-market activity actually built the pipeline that closed two quarters later. Being able to measure and attribute exposure on non-consented inventory, without consent, starts to close the gap between what we know works and what we can show works.

Now the limits, stated plainly so nobody overclaims. None of this reaches a named individual, and it should not pretend to. It does not bring back cross-site identity, person-level retargeting or behavioural segments without consent. It does not directly bless every B2B identity technique. Note in particular that company-level identification by resolving an IP address to an organisation, the engine behind a lot of account-based display and visitor identification, sits in its own legal lane. It typically runs server-side on network metadata under a legitimate-interest basis rather than through device storage, and operates at the organisation level rather than the individual. The ICO’s proposal is not primarily about that, and conflating the two would be sloppy. What the proposal does is validate the underlying principle that account-level and contextual approaches deserve to be treated differently from individual surveillance. That principle is the foundation the whole B2B addressability case rests on.

It is worth saying that activating non-consented, context-and-account inventory through curated deal IDs rather than user-level tracking is not a future hypothetical. It is the daily work for those of us who build B2B media on a signal layer rather than a cookie layer, which is why a re-balancing like this reads less as a workaround and more as the rules finally catching up with where good practice already was.

What this signals about the direction of privacy

The specific exemption matters. What it signals matters more.

For about a decade, privacy regulation has behaved like a ratchet: it only ever tightened. GDPR set the template, ePrivacy enforcement hardened the cookie banner, “Reject all” was forced level with “Accept all”, and the screws kept turning. The United States, long the laggard, has been catching up fast. As of early 2026 around twenty US states have comprehensive privacy laws covering more than half the population, and a dozen now require websites to honour the Global Privacy Control, a browser-level universal opt-out that sits upstream of any consent banner. Enforcement has teeth: reported US privacy penalties reached an estimated 1.4 billion dollars in 2025, and France’s regulator hit Google with a 325 million euro fine the same year. For anyone running addressable advertising at scale, the lived experience of the last decade has been a one-way tightening, the open web getting harder to use, and the programmatic toolkit getting steadily confiscated.

Against that, the ICO’s move is genuinely notable, not because it is large but because it points the other way. It is the first time a serious regulator has framed online advertising privacy as a spectrum of risk to be priced proportionately, rather than a single bright line to be defended ever more aggressively. The framing is the story. The ICO is effectively saying that risk-blind maximalism has costs of its own: it starves the privacy-preserving alternatives, it pushes spend into the very walled gardens that hold the most data, and it can drift away from what the public actually wants, which the citizen-jury work suggests is closer to relevant context than to either surveillance or a barren web.

I would not over-read it. This is one regulator, in one small jurisdiction, acting under an explicit growth mandate, on advice that is not yet law. There are real tensions even within it. As Lewis Silkin notes, the coherence question is awkward: having only just blessed consent-or-pay as a route to gather consent for the most intrusive advertising, the regulator is now carving out a consent-free lane for the least intrusive, and the interaction between the two is not fully mapped. The proposal also leans heavily on first-party structures and quietly raises the stakes on how publishers govern fingerprinting, which is harder to police than cookies precisely because it is harder to see. None of this is settled.

But direction of travel is set by inflection points, not by straight lines, and this looks like one. If the principle holds, that privacy should be regulated by the risk of the outcome rather than the mechanism of the touch, it travels well beyond one regulation. It is the argument that could, in time, distinguish anonymous context and account-level reach from individual surveillance across the whole regime, in the EU’s eventual ePrivacy settlement and in the maturing US frameworks alike. That is the version of privacy reform that B2B should be lobbying for in the open, because it is the version under which addressable B2B marketing is not collateral damage in a consumer fight.

What to do about it now

Three moves, none of which require waiting for legislation.

Build the contextual and account-level layer now, on the assumption that it is about to get materially more reach. The advertisers who already think in terms of context, taxonomy and the account rather than the cookie are the ones positioned to absorb a sudden expansion of addressable non-consented inventory the moment any exemption lands. The ones still wiring everything to user-level identifiers will be re-platforming under time pressure.

Get serious about measuring the anonymous majority. If the most valuable permission in the proposal is measurement and attribution on non-consented inventory, then the teams that already have a credible way to connect anonymous, out-of-market exposure to downstream pipeline will extract far more from it than the teams still counting last-click form fills. The infrastructure to prove dark-funnel impact is the infrastructure that turns this from a compliance footnote into a budget argument.

And engage with the direction, not just the detail. This is a rare moment where the regulatory wind is, briefly, at the industry’s back rather than in its face. The case for proportionate, risk-tiered privacy, privacy that protects people from being surveilled while letting the open web monetise anonymous context, is one B2B is unusually well placed to make, because our entire model already lives on the right side of that line. The worst outcome would be to treat a genuine opening as paperwork.

The audience that rejected your cookie never left. They are still out there, mostly out-of-market, mostly anonymous, doing the long quiet research that decides who makes the shortlist, on the premium open web that has been bleeding out for five years. For the first time in a long time, the rules might be about to let you reach them again, on terms that never required knowing their name in the first place. That is worth more than a compliance memo. It might be the moment the open web stops being something B2B marketers apologise for and starts being the thing they build on.

The B2B Stack is written by Mike Harty, a fifteen-year veteran of B2B programmatic advertising and founder of FunnelFuel.io. FunnelFuel runs B2B media on a raw signal layer rather than a cookie layer, activating context and account-level intent across the open web. If you are rebuilding addressability for the anonymous majority, that is the conversation we have every day.

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Sources

• ICO, Our advice to government on potential changes to online advertising rules, 18 May 2026

• Lewis Silkin, ICO advises government on consent-free route for low-risk online advertising, 21 May 2026

• Hunton, UK ICO Recommends Targeted Changes to PECR Rules for Online Advertising, May 2026

• PPC Land, UK’s ICO tells government to cut consent rules for low-risk ads, May 2026

• Press Gazette, More UK news publishers are adopting ‘consent or pay’, August 2025

• ignite.video, 26 Studies on Cookie Banners, Consent Rates, Compliance

• etracker, Cookie consent benchmarks

• Similarweb / BigGo Finance, Google AI search and zero-click, May 2026

• Pew Research via seo-kreativ, Google AI Overviews study, 2025 to 2026

• Ahrefs via ALM Corp, AI Overviews and publisher traffic, February 2026

• AdExchanger, The AI Search Reckoning Is Dismantling Open Web Traffic, January 2026

• Redseer, Walled Gardens vs Open Internet: The $1T Digital Ad Mismatch, 2026

• Statista, Walled gardens vs open internet share of digital ad revenue

• 6sense, When Do B2B Buyers Reach Out to Sales?

• Gartner, 61% of B2B Buyers Prefer a Rep-Free Buying Experience, June 2025

• Summit Partners / Ehrenberg-Bass, The 95:5 Rule, 2026

• Intentsify, How B2B Buying Groups Are Evolving, December 2025

• Leadfeeder, Why IP Targeting Is The Future of B2B Advertising

• Leadinfo, IP tracking and GDPR, December 2025

• O’Melveny, 2026 Data Security and Privacy Compliance Checklist, April 2026

• Secure Privacy, US State Privacy Law Tracker 2026

Unlike recent weeks we were starved of our usual agency-versus-DSP punch-up this week. No earnings shocker, exactly. The story that matters happened on a quieter register, and most of the trade coverage filed it under M&A maths and agentic buzzwords. That framing misses the point.

For about a decade, the identity and onboarding layer underneath digital advertising has behaved like neutral infrastructure.

LiveRamp sat in the middle of the pipes. Everyone routed through it. The holdcos used it, the independents used it, in-house teams used it, and nobody had to ask whose side it was on, because the answer was nobody’s. That was the whole value of it, because any tech tasked with handling first party data is always going to carry special sensitivity…

This week one holdco bought it. Publicis is acquiring LiveRamp for 2.2 billion dollars, and a neutral piece of plumbing just picked a side. If you buy B2B media and you are not a Publicis client, your identity resolution layer now has a landlord who also competes for your budget.

Omnicom worked that out within days and started heading for the exit, which tells you the conflict is not hypothetical.

Underneath the marquee deal, the same theme kept surfacing all week. The foundations B2B paid media stands on are quietly being bought, stretched, and questioned.

LinkedIn admitted the launch-day model is dead. The open web’s discovery layer kept collapsing under AI search. And the ratings currency that underpins CTV buying turned out to be measuring pirated streams.

Four stories that change the ground under your media plan.

We’ve got an obvious starter though

1. Publicis buys LiveRamp, and neutrality leaves the building

The story. Publicis announced on 17 May it is acquiring LiveRamp in an all-cash deal worth 2.2 billion dollars in enterprise value, around 2.5 billion in equity, at 38.50 dollars a share. That works out at roughly 12.3 times forward adjusted EBITDA on a business doing about 126 million dollars of EBITDA this year.

LiveRamp connects more than 25,000 publisher domains and 500-plus data and technology partners across 14 markets, with RampID as its durable identifier.

Publicis dressed the rationale in agentic language, calling it “data co-creation for smarter agents.” Then, on cue, Digiday reported Omnicom accelerating its exit from LiveRamp, citing the obvious problem of leaning on critical infrastructure owned by a direct rival.

My take. Strip out the agentic wrapper and this is a control play, not an AI play. The interesting bit is not what Publicis gains. It is what everyone else loses. And that’s around $600m of non Publicis revenue leaving the building and without a truly obvious home to head to

LiveRamp’s entire commercial logic was that it sat above the fight. It was the Switzerland of identity. RampID was useful precisely because it was vendor-agnostic plumbing that resolved a person or an account across walled gardens and the open web without an agenda. The moment a holdco owns that plumbing, the agnostic part is gone, whatever the press release promises about continued independence.

Omnicom did not wait around to test those promises, and neither should anyone running serious B2B budgets through clean rooms and onboarding flows.

For B2B specifically this lands harder than it does for consumer brands. Our entire targeting model leans on matching, onboarding, and account resolution, because we are chasing 200 named accounts and 11 people inside each of them, not a lookalike pool of two million.

When that resolution layer is owned by an agency that wants your media spend, “neutral match rates” becomes a phrase you have to take on trust. The independents in this market have spent years arguing that data ownership and execution should not sit in the same hand that bills your media. This deal just made that argument for us. The value of working with a partner whose data access does not route through a competitor’s balance sheet went up this week, quietly, for everyone outside the Publicis tent.

It is one reason we have always preferred raw signal access we control directly over processed scores rented from a layer somebody else owns. This was fundamental to how I built FunnelFuel’s data strategy, and I’m glad I did

Watch for the second-order move. If LiveRamp is now a Publicis asset, the other holdcos and the big independents need their own resolution layer or a credible neutral alternative, fast. That is the consolidation wave to track over the next two quarters, not the headline multiple.

2. LinkedIn quietly buried the launch-day model

The story. On 26 May, LinkedIn published a three-phase B2B launch framework arguing that product launches built around a single announcement moment leave most of the buying committee untouched. The numbers it leaned on are the real story. LinkedIn cited the average B2B buying journey at 211 days from Dreamdata’s 2025 benchmark, then the more recent March 2026 data stretching that to 272 days and 10 stakeholders, with up to 76 touchpoints across channels. Same month, LinkedIn launched an agency certification programme, citing lengthening buying cycles as the reason to raise standards among practitioners running its campaigns.

My take. Yes, LinkedIn has an obvious incentive here. “Launch day is dead, you need sustained presence” is a remarkably convenient conclusion for a platform that sells always-on budgets. Treat the framing with the scepticism it deserves.

The way their new algo is performing for organic posts could lead me to think you’d need to plan dozens of posts to land the message, a point which is a bit snide but the volatility in reach is real. That’s an aside to the bigger story

But the underlying data is not LinkedIn marketing. It is Dreamdata’s, drawn from millions of real customer journeys, and the direction of travel is the part worth sitting with.

The cycle did not just stay long. It got longer, year over year, from 211 days to 272. That is the opposite of what everyone predicted AI tooling and self-serve research would do to B2B buying. More information, more available, faster, and the committee is taking longer, not less.

This has been validated across scores of other research I’ve seen so I’m inclined to believe it

The practitioner read is straightforward. If the average deal now spans the best part of nine months and involves ten-plus people, then any paid media plan that spikes around a moment and goes quiet is buying impressions against a committee that has not started caring yet, and is dark by the time they do.

The money is not in the launch. It is in being consistently present and measurable across the dark stretch in the middle, where the committee is forming a view without ever clicking anything you can attribute.

This is the same point we keep making about high-value actions and the dark funnel. The signal that matters is rarely the conversion, as important as that obviously is, but the wider point is you need to build memory a long way upstream of hand-raising.

It is the quiet, repeated engagement from accounts that are months from a decision and will never fill in your form until they have already decided. Plan for the 272 days, not the launch week.

And as we’ve said repeatedly - when it comes to hitting the buying group, LinkedIn will over index in commercial roles and under represent back office personas. We need orchestrated coordination around the buying group triggered by cross pollinated data - engagement and intent signal coordinated between LinkedIn and other channels that represent the rest, which is where omnichannel stands heads and shoulders ahead

3. The open web’s discovery layer is collapsing in real time

The story. Google’s May core update began rolling out on 21 May and was being felt across the publishing world by the bank holiday weekend. Just what the premium publishing market needs, more volatility.

On 27 May, Google brought Preferred Sources into AI Overviews and AI Mode, added “Highly Cited” labels, and rolled out topic carousels, deepening the shift from blue links to synthesised answers.

Digiday ran a piece the same week on small publishers watching their traffic drain as AI reshapes discovery. And Comscore data showed Claude growing 130 per cent month over month in March, with ChatGPT still the dominant assistant, as the assistant layer keeps eating the top of the research funnel.

My take. This is not a B2B story on the surface, which is exactly why most B2B marketers are sleepwalking through it. Two things are happening at once and both matter to anyone running content-led demand gen.

First, the supply side of the open web is thinning. B2B programmatic runs on the open internet, on the trade press and the niche publishers and the long-tail business sites where buying committees actually read. These are the richest source of fresh third party intent

If AI Overviews keep absorbing the clicks and the small publishers keep folding, the quality inventory we target contextually gets scarcer and the survivors consolidate pricing power.

The “open web” half of the open web is quietly shrinking, and that flows straight into your CPMs and your reach against the accounts you care about.

Second, the discovery behaviour of your buyers is moving inside the assistants. When a procurement lead researches CRM platforms or a CMO scopes a martech category, an increasing share of that now happens in a conversation with ChatGPT or Claude, not a Google results page or a vendor site.

That research is invisible to your analytics and unservable by most of your paid media.

Google adding “Highly Cited” labels is a tell about where authority is being arbitrated now. The practical implication is uncomfortable but clear. Being the answer the model cites is becoming as important as being the result the buyer clicks, and almost nobody in B2B has a plan for it. Start treating model citation as a distribution channel, because your competitors who publish primary, citable expertise are already being read by the machine your buyer is asking.

The key is recognising that citations won’t drive clicks like the blue links of old, but the marketers gamble is they do build trust, brand memory and authority at crucial junctures in the research journey

4. The ratings currency is measuring pirates

The story. Adtech auditor Adalytics published a report on 26 May finding that illicit livestream services such as WatchSports and StreamSports99 are redistributing high-profile sport to hundreds of thousands of devices. In one example, a single pirated stream of the Super Bowl off NBCU’s Peacock reached more than 100,000 devices, one of over 100 sites redistributing that game. Adalytics estimates this year’s Super Bowl ratings were likely off by one to two million viewers. The NFL had already put Nielsen on notice last September over systematic undercounting.

My take. B2B money is flooding into CTV on the promise of premium, brand-safe, measurable reach. This report is a useful cold shower on the “measurable” part.

CTV is woefully Immature and onwards of 70% of the ad requests are essentially empty of the metadata which makes programmatic so valuable. Big players like Teads are working on solutions that remind me of my first days in the space around 2010 - hacking solutions to fix fundamental premises.

The whole CTV value proposition rests on a ratings and measurement currency that buyers treat as ground truth. Adalytics just demonstrated that the currency is counting streams nobody licensed and missing viewers nobody can find. If the measurement of the single most-watched, most-scrutinised broadcast of the year is off by a couple of million either way, the idea that your B2B CTV buy is being measured to a clean, trustworthy standard deserves a hard second look.

For our market the lesson is not “avoid CTV.” CTV works for B2B when it is bought against the right accounts and tied to real outcomes downstream. Our FunnelFuel team had a great week in advancing the scale behind account based targeting and reporting of connected tv. I’m bullish on it

The lesson is to stop outsourcing your confidence to a ratings number and insist on outcome-based measurement that connects the impression to account engagement and pipeline. Reach you cannot verify is not reach. It is a story you are paying for. Measure the thing that pays you back instead.

In case you missed it

ChatGPT ads got cheaper and B2B still has no way in. OpenAI removed its advertising spend minimum entirely on 5 May, having cut it from 250,000 dollars to 50,000 in April, and CPMs have slid from a 60-dollar launch price to as low as 25 through the Criteo pipeline. Impressive compression. But the platform’s allowed verticals still skew consumer (household goods, local services, travel, digital products), B2B is not really invited yet, and the deeper problem is unchanged. As TAU’s Robert Webster put it to Digiday, the real moat for Google and Meta is owning measurement, not the interface, and until someone independent can verify what a ChatGPT impression is worth, advertisers are taking OpenAI’s word for it. Much of the early spend is still test-and-innovation budget chasing FOMO.

Meta cut 350 Irish jobs, blaming AI. Around 20 per cent of its local workforce, with AI named as the driver. File under the slow reality that the efficiency story the platforms sell advertisers is the same story they are telling their own headcount.

The Trade Desk’s Q2 guide landed soft. A roughly 750 million dollar Q2 forecast was read as weak and the stock dropped after the 7 May call. We have covered TTD’s year to death, so just noting it. The pressure on the independent DSP from Amazon and the walled gardens is not letting up.

Dreamdata’s number worth pinning to your wall. 272 days. 10 stakeholders. If your reporting still judges a campaign on what converted this quarter, you are grading a nine-month process on a six-week sample.

Have a great weekend

Mike

Sources

• Publicis to acquire LiveRamp, SEC filings (Form 8-K / DEFA14A, LiveRamp Holdings), 17 May 2026

• “Ad Tech Briefing: Agencies reposition for the agentic era,” Digiday, 26 May 2026

• “LiveRamp announces Q4 and FY2026 results,” LiveRamp / SEC, 17 May 2026

• “LinkedIn’s three-phase B2B launch framework,” PPC Land, 26 May 2026; Dreamdata 2026 LinkedIn Ads B2B Benchmarks

• “Pirated Sports Streams Are Warping TV’s Most Important Ratings,” AdExchanger, 26 May 2026

• “AI ads, data deals, and a search shakeup define the week,” PPC Land, 28 May 2026

• “Advertisers still skeptical of ChatGPT ads,” Digiday, 26 May 2026; ChatGPT Ads guides citing Digiday CPM and minimum-spend reporting

• “By the numbers: Ad tech’s quarter of mixed fortunes,” Digiday, May 2026

A special edition of The B2B Stack. Not the Friday briefing.

Most adtech glossaries are written by vendors trying to make their product sound clever, or by SEO content farms paraphrasing the IAB. Neither group has bought a deal ID in anger, reconciled a curated marketplace against the publisher ad server it claimed to deliver, or sat in front of a CFO explaining why the take rate looks the way it does.

This one is written from that desk.

115 terms across nine clusters, plus a benchmarks coda with operating numbers from the work: typical CPMs by channel, realistic match rates by environment, the working media percentage you should actually be hitting, the supply-path hop counts that explain where your money has gone, the frequency caps that keep B2B presence on the right side of harassment. Roughly forty minutes if you read it through. Ten seconds if you jump to the A–Z and look up the one term you came for.

Where the industry uses a term loosely, it gets flagged. Where the B2B definition diverges from the generic one, the divergence is called out. Where a term has quietly changed meaning over the last twelve months, you’ll see that here before you see it anywhere else. Where there’s a specific number from inside the work that helps you calibrate what you’re seeing, the number is here, hedged where it deserves to be and unhedged where it doesn’t.

There is no shortage of definitions on the internet. There is a shortage of definitions that tell you what to actually do with them. That gap is what this is for.

It is designed to be bookmarked, returned to, and updated as the vocabulary shifts. The regular Friday briefing returns to its usual cadence this week. Reply to this email with corrections, gaps, or anything you’d argue with — the document gets better when operators help keep it honest.

What you’ll learn in this article

• The auction and bidding mechanics that govern every impression you buy, and the bid-shading savings most teams don’t see

• The supply-side terminology that defines what inventory actually is, including the curation, MFA, and verification layers that quietly determine campaign quality

• The buying-side architecture, fees, and economics that compose the programmatic tax — and the working media benchmarks worth measuring against

• The identity and audience plumbing that has been quietly rebuilt around the cookie’s collapse, with realistic match rates by environment

• The B2B-specific vocabulary that separates real operators from people who learned the trade in B2C, including the frequency and signal-decay metrics that don’t appear in any DSP report

• The measurement, attribution, and signal language that lets you tell working media from theatre

• A benchmark coda with the operating numbers (CPMs, match rates, working media, supply-chain hops, incrementality thresholds) you should be testing your own stack against

How to use this glossary

Read it through once if you’re newer to the space. Bookmark it if you’re not. The A–Z index below lets you jump straight to a term you came in for. Each entry stands alone, so dropping in from a search engine or an AI answer gets you the full context without backtracking. Terms in italics inside an entry are defined elsewhere in the glossary.

If you find something missing, wrong, or fashionable nonsense, reply to this email. Every correction gets read and considered.

A–Z index

If you know the term, jump straight to it.

A · Account-Based Marketing (ABM) · Account graph · Account-level frequency · ads.txt and sellers.json · Ad exchange · Ad server · Agentic ad tech · Answer Engine Optimisation (AEO/GEO) · Audience containerisation · Audience extension · Authenticated audience · Authenticated traffic

B · B2B Marketing Operating System · Bid duplication · Bid shading · Bid stream · Bombora · Brand safety · Brand suitability · Buying committee

C · Closed-loop measurement · Connected TV (CTV) · Contextual · Conversion API (CAPI) · Cost per Mille (CPM) · CPC, CPA, CPL, CPV · Curation · Customer Data Platform (CDP)

D · Dark funnel · Data clean room · Data co-op · Data CPM · Data Management Platform (DMP) · Data onboarding · Deal ID · Demand generation · Demand-Side Platform (DSP) · Digital Out-of-Home (DOOH) · Display

E · Engagement scoring

F · Fingerprinting · First-party data · First-price auction · Firmographic · Floor price · Frequency cap

G · Generative Engine Optimisation (GEO)

H · Hashed email (HEM) · Header bidding

I · ID5 · Ideal Customer Profile (ICP) · Identity graph · In-app · Incrementality · Insertion Order (IO) · Intent data · Invalid Traffic (IVT)

L · Lead generation · Lift study · Lookalike modelling

M · Made-for-Advertising (MFA) · Marketing Mix Modelling (MMM) · Match rate · Mobile Advertising ID (MAID) · MQL · Multi-Touch Attribution (MTA)

N · Native

O · Open exchange · Open web · OpenRTB · OTT

P · Pacing · Pipeline attribution · Pixel · Preferred Deal · Private Marketplace (PMP) · Privacy Sandbox · Programmatic audio · Programmatic Guaranteed (PG) · Programmatic tax · PPID

R · RampID · Reach · Real-Time Bidding (RTB) · REDS · Remarketing · Retargeting · Return on Ad Spend (ROAS)

S · Second-party data · Second-price auction · Self-service vs managed service · Signal · Signal decay · Signal envelope · 6sense and Demandbase · Supply Path Optimisation (SPO) · Supply path reconciliation · Supply-Side Platform (SSP) · Surge

T · Take rate · Target Account List (TAL) · Technographic · Tech fee · Third-party cookie · Third-party data · Topic taxonomy · Total Addressable Market (TAM)

U · Unified ID 2.0 (UID2)

V · Verification · Video · Viewability · Viewable CPM (vCPM) · View-through

W · Walled garden · Win rate · Working media

1. Auction and bidding

The mechanical layer beneath every programmatic impression. Most marketers never look here. The ones who do tend to spend their budgets better.

Real-Time Bidding (RTB)

The auction mechanism that runs underneath open programmatic. Every time an ad slot becomes available, an auction request goes out to bidders, who respond with a price within milliseconds. The highest bidder wins, the creative is served, the page loads. All of this happens in roughly the time it takes you to blink.

In practice. RTB is the default assumption when someone says “programmatic,” but increasingly it isn’t where the interesting buying happens. Curated marketplaces, programmatic guaranteed, and preferred deals all sit alongside open RTB, and for B2B specifically, the share of spend going through pure open auction has been falling for years.

OpenRTB

The technical specification that defines how RTB requests and responses are formatted. Maintained by the IAB Tech Lab. The reason a DSP built by one vendor can talk to an SSP built by another.

In practice. You don’t need to read the spec. You do need to know it exists, because every conversation about new signals, new identifiers, or new auction mechanics ultimately comes down to whether the OpenRTB schema supports them. If a vendor tells you they’re sending a new audience signal in the bid stream, the right question is which OpenRTB field.

First-price auction

An auction format where the winning bidder pays exactly what they bid. The default across most of programmatic since around 2019. Replaced second-price auction as header bidding broke the assumptions that made second-price coherent.

In practice. First-price changes how you bid. In a true second-price world you could bid your maximum and trust the auction to clear at the next price down. In first-price, every penny you bid is a penny you pay, which is why bid shading exists.

Second-price auction

An auction format where the winner pays one cent more than the second-highest bid. Theoretically encourages truthful bidding because there’s no penalty for bidding your maximum. Largely extinct in open exchange, though it persists inside some private marketplaces and walled gardens.

In practice. Worth knowing about because the assumptions it created (bid your true maximum, don’t worry about the clearing price) still leak into how some buyers and DSPs reason about bidding, years after the auction format itself was replaced. If you’re being told to “just bid higher” by a vendor, check what auction the inventory is actually clearing in.

Bid shading

An algorithmic adjustment, usually applied by a DSP, that lowers a buyer’s effective bid in a first-price auction based on what the model expects the winning bid to be. The idea is to keep some of the surplus that first-price would otherwise transfer to the publisher. An Adtech take rate booster

In practice. Every major DSP has its own bid-shading logic, and none of them will tell you exactly how it works. Typical shading savings in open-exchange first-price auctions sit between 5-12% of the gross bid, with the highest savings on inventory with the most price volatility. PMPs and direct deals compress that spread to low single digits. Worth knowing the mechanism exists, worth being sceptical when your DSP claims efficiency wins that look suspiciously round.

Floor price

The minimum bid a publisher or SSP will accept for an impression. Set per-deal, per-format, per-buyer, or globally. Can be hard (no bid below this clears) or soft (bids below this enter a lower-priority pool).

In practice. Floors are the publisher’s main lever against bid suppression. They’re also the thing that makes “the CPMs in this PMP are too high” a slightly more nuanced complaint than it sounds.

Bid stream

The flow of bid requests sent from publishers and SSPs to buyers, containing the metadata that lets a DSP decide whether and how much to bid. Includes the URL, user signals, device, geo, and increasingly first-party audience data layered in via curation. Individual sell side platforms like Index Exchange handle upwards of 700 billion bid requests per day

In practice. Almost every interesting innovation in programmatic in the last five years has come from changes to what’s in the bid stream. Bombora surge data in the bid stream is a meaningfully different product to Bombora data sitting in a separate audience platform.

Win rate

The percentage of bid requests a buyer wins out of the requests they bid on. Reported by every DSP, interpreted by almost no buyer.

In practice. Low win rates can mean you’re underbidding, or that you’re bidding into supply you’ll never win because someone with better data is always above you. The diagnostic only works if you compare like-for-like supply paths, which most teams don’t.

In B2B, we deal with nail in a haystack audiences, so a low win rate can mean we are missing out on highly valuable audience opportunities. Its common that buyers bid too low to reach account based audiences in premium environments, and end up clearing with lower CPMs but in environments like online gaming, where CPMs are cheap but intent is non-existent. There’s a complex dynamic here to be worked through at a buyer level

Header bidding

A publisher-side technology that runs an auction across multiple SSPs and exchanges simultaneously, in the user’s browser or on the publisher’s server, before calling the ad server. Replaced the old waterfall logic where SSPs were ranked sequentially.

In practice. A modern publisher header bidding setup commonly auctions a single impression to 8-15 SSPs simultaneously, sometimes more once resellers are layered in. Each path takes a percentage. This is the structural reason open-exchange working media looks the way it does, and the reason supply path optimisation became a discipline rather than an option.

When I first entered the ad tech industry 15 years ago, individual publishers would work with individual sell-side platforms, meaning that sell-side platforms had distinction and unique value in the supply that they represented. Header bidding, in reality, completely blew open the doors on that value proposition and commoditised the amount of inventory available.

That means that today, in modern programmatic, you’ve got upwards of 50 sell-side platforms who are pretty much all representing the exact same inventory, creating many layers of duplication across their respective bid requests and massively inflating the volumes of impressions that DSPs like the Trade Desk and DV 360 have to listen to. This has created a world of supply path optimisation, curation, audience containerisation and sell-side predicated rebate structures in order to try and drive distinction in what is, in reality, a highly commoditised part of the ecosystem

Bid duplication

The phenomenon where the same impression opportunity reaches the same DSP through multiple supply paths simultaneously, producing several competing bids on a single auction.

In practice. Header bidding plus reseller relationships routinely create 3-7 duplicate paths to the same impression. The buyer effectively bids against themselves, the SSPs take their fees on each path, and the winning path is often not the most direct one. This is the specific waste pattern that supply path optimisation exists to fix, and the reason a duplicated path can look efficient on win rate while quietly inflating the buyer’s effective cost.

Supply Path Optimisation (SPO)

The buyer-side practice of choosing which SSPs and exchanges to bid through for any given piece of inventory, usually with the goals of reducing duplicate auctions, lowering fees, and preferring more direct paths to the publisher.

In practice. SPO is one of the most underused levers in B2B. Most B2B buyers have no view of how many hops sit between their DSP bid and the publisher’s ad server. The honest answer is usually three to five. Each hop takes a percentage, each hop introduces latency, and at least one of them is usually arbitrage. Even worse, signal gets lost or broken across these hops, creating less accuracy. Cleaning up the top ten supply paths typically recovers 8-15% of working media in the first quarter of focused effort.

2. Supply and inventory

What you’re actually buying when you buy programmatic. The vocabulary here has shifted more than any other cluster in the last three years.

Supply-Side Platform (SSP)

The publisher’s counterpart to a DSP. Manages the publisher’s relationships with multiple exchanges and demand sources, runs auctions on the publisher’s behalf, and reports back on revenue. Examples: Magnite, Index Exchange, OpenX, PubMatic

In practice. For most B2B buyers, the SSP is invisible — the DSP handles which SSPs it bids into. That invisibility is the reason supply path optimisation exists and the reason fees compound in places you can’t see.

Over about my first twelve years in the adtech ecosystem, I would have said that the sell-side platform is in danger of becoming a redundant part of the ad tech stack, symbolic in many ways of the duplication and bloat that exists in a world that should have few intermediaries. However, over the past probably three or four years, five at a push, we’ve seen the emergence of genuine innovation coming from the elite, probably the top half a dozen or so, of the supply-side platforms, led by the likes of Index Exchange, who have recently launched their Index Cloud.

We’re now seeing a lot of genuine differentiation and unique value coming from the ability to construct audiences in a nearly latency-free environment, living in the sell-side infrastructure, which is allowing us to run DSPs in containerised environments, like Bedrock recently announced. We’re also increasingly seeing a real shortening of the time that it takes to run an open RTB auction, from 200 ms round trips down to a fraction of that, which is opening up genuine creative optimisation opportunities, which is using that time that’s being clawed back. This is real grass roots innovation in adtech and gives me cause to believe that the ecosystem will evolve beyond the models that personified it even 5 years ago.

As SSPs have converged towards DSPs, and with examples at the trade desk and their open path solution converging back towards the supply side, there is actually a lot less distinction today between an SSP and a DSP than there was 10 to 15 years ago. This is definitely a part of the ecosystem to keep a really close eye on, where genuine and true innovation is emerging

Ad exchange

A marketplace where publisher inventory and buyer demand meet. Often used interchangeably with SSP, though strictly an exchange is the auction layer and an SSP is the publisher-facing technology that plugs into it. I’d venture the ad exchange is redundant today because of header bidding and SPO efforts.

In practice. The distinction matters less in 2026 than it did a decade ago, because most major players run both. You will see them listed separately in reporting.

Demand-Side Platform (DSP)

The buying technology that places bids into auctions across multiple SSPs and exchanges. Holds the campaign setup, audience definitions, frequency caps, budget pacing, and creative. Examples: The Trade Desk, DV360, StackAdapt, Yahoo DSP, Adform.

In practice. The choice of DSP is the most consequential decision in a B2B programmatic stack and the one most often made for the wrong reasons. The right DSP for B2B is the one with the best access to the data signals you actually need to activate, the cleanest supply, the most flexible deal architecture, and a fee structure you can read without a lawyer.

The DSP has been the king of the ad tech world for the last 15 years, and that’s only really started to change since the emergence of innovation coming from the supply-side layers over the past half a decade or so. Since then, we’ve actually seen some real pressure coming onto the top DSPs for the first time. Trade desk, for example, has been the darling of the NASDAQ, representing ad tech with ferocious lower-left-to-upward-right growth over the past five years. That’s coming under some pressure now for the first time, and it’s symbolic really of changes that are coming between agentic buying, which is disintermediating a lot of hands on the keyboards with DSPs and with it some of their value, perceived or otherwise. The emergence of better curation and increasingly containerised audiences is now meaning that the reality of running a DSP is that you’re actually using it for a lot less than you used to use it for. If in the past we were leaning on it for setting up campaigns, building audiences, defining those, and optimising, we’re increasingly now doing a lot of that audience building and construction and refinement in the supply-side layers. What we need a DSP for is less and less, which is putting downward pressure on the fee structures, and we’ve seen that kind of leading to fewer players in the DSP market really over the past few years

Open exchange

The default inventory pool available to any buyer who can connect to an SSP. No private negotiation, no audience curation, no minimum spend commitment. Sits at the bottom of most modern supply hierarchies.

In practice. Open exchange in B2B is mostly a poor use of money. The supply is variable quality, the audience signal is degraded, and the MFA exposure is real. Most disciplined B2B buyers route ninety percent or more of their spend through curated or private inventory.

Open web

The collection of publisher websites and digital environments accessible through standard programmatic infrastructure, as distinct from authenticated, closed platforms. Where most independent publishing lives.

In practice. “Open web” is a term that has been doing more rhetorical work in the last few years as walled gardens have grown. For B2B buyers, the open web is where most of the high-trust trade and business press lives, and it remains the only environment where you can apply your own audience and curation logic at scale.

Walled garden

A closed platform that controls its own inventory, audience data, identity, and (usually) measurement, with limited ability for external tools to operate inside. The largest are Google, Meta, Amazon, and LinkedIn, with TikTok and Reddit increasingly in the same category.

In practice. B2B campaigns spend a meaningful share of total budget inside walled gardens, especially LinkedIn. The trade-off is structural: better authenticated targeting and inside-the-platform measurement, against limited cross-platform reporting, no log-level transparency, and audience data that doesn’t leave the garden. The right B2B stack treats walled gardens as one tool among several, not as the whole tool.

Private Marketplace (PMP)

An auction-based deal between a publisher (or curator) and a defined set of buyers, accessed via a deal ID. More restricted than open exchange but still auctioned. Usually has an agreed floor price and audience or content scope.

In practice. PMPs have largely become the default in B2B because they’re the easiest way to enforce content quality and apply curated audience layers without committing to fixed media volume.

Deal ID

A unique alphanumeric identifier issued by a publisher, SSP, or curator that gives a buyer access to a specific package of inventory, audience, pricing, or deal mechanics. The plumbing that makes everything other than open exchange work.

In practice. Most B2B deal IDs in 2026 aren’t really about premium publisher access. They’re audience containers. The publisher list inside the deal is often a thousand sites long; what makes the deal valuable is the data layer applied to it. Judging a deal ID by its publisher roster misses the point.

Programmatic Guaranteed (PG)

A non-auctioned deal where a buyer commits to a fixed volume of impressions at a fixed price, executed programmatically through a deal ID. The closest programmatic gets to a traditional insertion order.

In practice. PG is useful when you need delivery certainty: a launch window, a heavyweight execution, a specific high-impact placement. It’s expensive per impression and inflexible on optimisation, so most B2B campaigns use it for ten to twenty percent of budget at most.

Preferred Deal

A non-guaranteed, non-auctioned deal between a buyer and a publisher at a fixed CPM, where the buyer has first refusal on the inventory before it goes to PMP or open exchange. Has largely fallen out of fashion.

In practice. Preferred deals are rare in modern B2B stacks because they offer the worst of both worlds: no auction efficiency, no delivery guarantee.

Curation / Curated Marketplace

A practice where a third party assembles a package of inventory across multiple publishers, applies an audience or content layer, and exposes it to buyers as a deal ID or PMP. Examples of curators: Audigent, Chalice AI, Multilocal, and a growing roster of agency- and operator-owned curation businesses.

In practice. Curation is the single most important shift in B2B programmatic of the last three years. The mechanics are simple: take inventory the buyer could already access, layer signal the buyer couldn’t easily apply themselves, expose it as a single deal ID with predictable economics. Most curated marketplaces in B2B carry an additional 10-25% on the media cost in exchange for the audience and content layer. Whether that’s value depends entirely on what the curator is actually applying. The difference between a thoughtful B2B curation, built on raw signal access and properly maintained allow-lists, and a generic audience overlay sold under the same name is a factor of three in performance and often the reason a campaign clears or doesn’t.

Made-for-Advertising (MFA)

Sites that exist primarily to generate ad impressions rather than to host genuine editorial. Characterised by heavy ad-to-content ratios, slow load times, clickbait headlines, low session quality, and outsized share of programmatic inventory volume.

In practice. Research from Adalytics, the ANA, and Jounce Media across 2023 to 2025 estimated MFA absorbing 15-21% of open-exchange spend industry-wide. The pattern inside a single B2B campaign is usually worse than the average: MFA exposure concentrates in a small number of high-volume domains that look respectable in a list and disappear under a competitive set check. I’ve reviewed B2B open-exchange buys where a single unfamiliar domain accounted for nearly 20% of impressions, and the buyer had never heard of it. The defences are unglamorous: a clean exclusion list refreshed monthly, a preference for curated supply, and at least quarterly log-file audits.

Brand safety

The discipline of preventing ads from appearing alongside content that’s harmful to the advertiser’s reputation. Includes hard exclusions (illegal content, hate speech) and soft preferences (avoiding war coverage for a leisure brand, for example).

In practice. B2B brand safety is mostly a B2C-defined toolset applied to a different problem. The harder question for B2B is brand suitability: whether the publication is one your buyer respects. That’s a different filter to whether the article mentions a sensitive topic.

Brand suitability

The discipline of selecting environments that are positively appropriate for an advertiser’s brand and audience, rather than simply not unsafe. Distinct from brand safety, which is the floor; suitability is the ceiling.

In practice. Most modern B2B buyers should care more about suitability than safety. The risk in B2B advertising isn’t usually that your ad runs next to upsetting content; it’s that your ad runs across a long tail of low-attention, low-credibility sites that quietly erode the perception your media is trying to build. Suitability filtering is what curated marketplaces and quality-led private deals are really selling.

Invalid Traffic (IVT) / ad fraud

Traffic on advertising inventory that isn’t a real human seeing the ad. Includes bot traffic, scripted impressions, hidden ads, click farms, domain spoofing, and various flavours of inventory fraud. IAB Tech Lab distinguishes General Invalid Traffic (GIVT, detectable by basic filters) from Sophisticated Invalid Traffic (SIVT, requiring more advanced detection).

In practice. Industry-reported IVT in open programmatic has ranged from low single digits to double digits depending on methodology and year, with Pixalate, DV, and IAS reporting somewhat different numbers from somewhat different vantage points. The honest read is that B2B campaigns running through curated supply and verified inventory see meaningfully lower IVT exposure than equivalent open-exchange buys, often by a factor of three or more. Most B2B teams have never asked their DSP to report IVT separately by supply path, which is the only view that tells you which paths are actually problems versus which are clean.

Verification (DV, IAS, MOAT)

Third-party services that independently measure viewability, IVT exposure, brand-safety adjacency, and other supply quality metrics for advertisers. The three major players are DoubleVerify, Integral Ad Science, and Oracle MOAT.

In practice. Verification has been a category of structural friction in programmatic for over a decade. The fees are real, the protection is real, and the over-blocking is real too. Most enterprise B2B campaigns run with at least pre-bid verification on; whether you also run post-bid measurement is usually a budget question.

Publisher-Provided Identifier (PPID)

A persistent identifier set by a publisher for its own users, passed into the bid stream to enable identity continuity without third-party cookies. Implementation patterns vary by publisher.

In practice. PPIDs have quietly become one of the more important post-cookie identity signals in premium publisher environments. For B2B, the value sits in how well the publisher’s authenticated user base maps to your target audience. A trade publication with ninety percent registered users and a strong B2B reader base is a different identity proposition to a general-interest site with low log-in rates.

Viewability

The measure of whether an ad was actually rendered in a viewable position on the page. The IAB standard is fifty percent of pixels in view for one continuous second for display, and two continuous seconds for video.

In practice. Viewability is a hygiene metric, not a performance one. High viewability is necessary but not sufficient. Most B2B buyers should be targeting seventy percent or higher on display and ninety-plus on video, and treating sub-fifty percent placements as something to diagnose, not optimise around.

ads.txt and sellers.json

Publisher-side files that declare who is authorised to sell a publisher’s inventory programmatically. ads.txt is a plain-text file in the root of the publisher’s domain. sellers.json is the SSP-side counterpart that declares the seller identity behind each authorised path.

In practice. Together they let buyers verify that the path they’re bidding through is legitimate. Useful technically. Less interesting commercially than they sound, but absence of either is a hygiene red flag.

3. Buying-side architecture and economics

The tech and money flowing inside the buyer’s half of the stack. Where most of the fees live, and where most of the fees aren’t visible.

Ad server

The technology that holds the ad creative, decides which version to serve in any given impression, and tracks delivery. Sits between the DSP decision and the user’s screen. Examples: Google Campaign Manager (CM360), Flashtalking, Sizmek (now Amazon Ad Server).

In practice. Most B2B buyers use a single ad server across all DSPs, which is the right call because it makes cross-DSP reporting possible.

Insertion Order (IO)

The contractual document covering a media buy. Specifies the campaign, the budget, the flight dates, the targeting, and the creative. The bridge between sales and execution.

In practice. Programmatic was sold for years as the death of the IO. In B2B it never really happened. Most enterprise B2B campaigns still run on an IO, often a multi-channel one that wraps programmatic, search, social, and direct deals into a single document.

Take rate

The percentage of media spend taken by the DSP (or any other intermediary) as a fee. Disclosed in some commercial structures, opaque in others.

In practice. DSP take rates vary wildly. A self-service Trade Desk seat in a major holding company might run a take rate in the high single digits. A small advertiser on a managed-service contract can be paying twenty-plus percent before any other fees layer on.

Tech fee

A catch-all for any fee charged by a piece of technology in the buying stack. Includes DSP take rate, ad verification fees, brand safety tooling, audience platform fees, and identity resolution fees.

In practice. A meaningful share of B2B programmatic spend dissolves into tech fees that the marketer never sees on a single line item. Working through a programmatic invoice and rebuilding the fee waterfall is the single most useful exercise a B2B CMO can do this year.

Working media

The portion of total budget that actually reaches a publisher in exchange for an impression. The inverse of all fees, taxes, and intermediation costs above it.

In practice. Most B2B programmatic stacks operate at 55-70% working media when measured rigorously through to publisher payout. Genuine 80%+ working media exists only in heavily curated, direct, or short-supply-chain configurations. Anything claimed above 90% by a managed-service vendor without log-level evidence to back it deserves polite scepticism.

Data CPM

A fee charged per thousand impressions for applying a data segment, audience, or signal layer to a buy. Distinct from the media CPM.

In practice. Data CPMs stack. A typical B2B impression might carry the publisher’s CPM, plus a curation fee, plus a Bombora intent layer at a data CPM, plus a third-party brand safety verification fee, plus the DSP take rate. Each line is small. The compound is not.

Programmatic tax

Informal term for the cumulative load of fees, intermediation costs, and tech charges between the buyer’s budget and the publisher’s revenue. Sometimes called the “ad tech tax.”

In practice. The 2020 ISBA / PwC study put the programmatic supply chain tax at around fifty percent in some open-exchange paths, with around fifteen percent of spend unaccounted for entirely. More recent work from the ANA, Adalytics, and the IAB suggests the median has improved somewhat in B2C as direct deals have grown. Comparable B2B-specific data is thinner. Independent log-file analysis remains the only reliable way to know your own number.

Supply path reconciliation

The process of comparing what your DSP says it bought against what the publisher’s ad server records as delivered, then identifying the supply paths, fees, intermediaries, and audience layers that compose each impression.

In practice. Reconciliation is the only honest way to know what you actually bought, and it’s expensive, slow, and rare. The first reconciliation a B2B team runs almost always surprises everyone in the room: a path the agency swore was direct turns out to have two intermediaries, a CPM the buyer assumed was the publisher’s payout is actually the gross before fees, and the supply mix on a “premium” buy includes inventory the buyer would not have bought knowingly. The B2B teams that have done this exercise once tend to make different supply decisions afterwards.

Self-service vs managed service

Two commercial models for accessing a DSP. In self-service, the buyer holds the seat and runs the campaigns directly. In managed service, an agency or specialist operator holds the seat and runs the buying on the buyer’s behalf, usually for a percentage fee.

In practice. The right answer depends entirely on internal capability. Self-service looks cheaper on paper and is more expensive in reality if you don’t have a senior trader in-house. Managed service is more expensive on paper and is often the right call for B2B teams running campaigns at moderate scale who don’t want to staff a trading desk.

Pacing

The pattern in which a campaign’s budget is spent across its flight. Even pacing spreads spend uniformly; ASAP pacing spends as fast as supply allows; smart or accelerated pacing variants concentrate spend around predicted high-performance windows.

In practice. Pacing is one of the most under-managed dimensions of B2B campaign delivery. A B2B audience is rarely active uniformly across the working week, and almost never on weekends. Letting a DSP pace evenly across seven days means spending a meaningful share of your budget against a non-buying audience. Day-parted and weekday-weighted pacing remains a basic discipline most teams skip.

Pixel

A small piece of code, usually a transparent image or JavaScript tag, placed on a webpage to record events: page views, conversions, audience membership, time on page. The original mechanism for marketing measurement on the open web.

In practice. Pixels have been gradually displaced by Conversion API server-side equivalents as browser-level protections have eroded their reliability. They still work in many contexts and remain the default in plenty of campaigns. Treating pixel data as ground truth in 2026 is generous; treating it as one signal among several is sensible.

Conversion API (CAPI)

A server-to-server method for sending conversion events from an advertiser’s environment directly to an ad platform, bypassing the user’s browser. Implemented by Meta as the original CAPI, with equivalents across most major DSPs and walled gardens.

In practice. Conversion APIs have become essential as cookie deprecation and tracking-prevention features have made pixel-based event capture unreliable. For B2B, CAPI matters most for closed-loop signal back to walled gardens (especially LinkedIn) and for feeding cleaner data into optimisation algorithms that would otherwise be working from degraded inputs.

4. Identity and audience

The plumbing that lets you target a specific person, or company, or device. Quietly rebuilt over the last five years as the third-party cookie has eroded.

Third-party cookie

A small text file stored in a user’s browser by a domain other than the one being visited. The mechanism that underpinned most of cross-site tracking, retargeting, and audience matching in display advertising for two decades.

In practice. The third-party cookie is dead in Safari and Firefox and on its way out in Chrome, with Google’s deprecation timeline having shifted multiple times. Treating cookies as a primary identity strategy in 2026 is professional negligence. They still work in some contexts, but planning a programme around them is planning to be wrong in eighteen months.

Privacy Sandbox

Google’s collection of replacement technologies for third-party cookies in Chrome. Includes the Topics API for interest-based targeting, Protected Audience for remarketing, and Attribution Reporting for measurement.

In practice. Privacy Sandbox is real, it works at a basic level, and almost nobody in B2B is using it as their primary identity strategy because the targeting precision is far below what B2B campaigns require. It will matter more for B2C retail than for B2B.

Mobile Advertising ID (MAID)

Persistent identifiers issued by mobile operating systems for advertising purposes. GAID (Google Advertising ID) on Android, IDFA (Identifier for Advertisers) on iOS.

In practice. IDFA has been functionally dead since Apple’s App Tracking Transparency rollout in 2021, with opt-in rates running at twenty to thirty percent in most categories. GAID still works but Google has signalled changes. For B2B, mobile identifiers are rarely the primary signal anyway, because most of the relevant research and buying activity happens on desktop or in authenticated web environments where MAIDs aren’t the working identifier.

The practical value is mapping desktop signals to MAIDS, to extend targeting into mobile environments and to execute location based tactics

Hashed email (HEM)

An email address run through a one-way cryptographic hash function, producing a unique string that can be matched across systems without exposing the underlying email.

In practice. Hashed emails are the de facto persistent identifier in B2B because B2B is a logged-in, email-driven world. Most modern B2B identity resolution starts from a hashed email and builds outwards.

Unified ID 2.0 (UID2)

An open-source, deterministic identifier built from hashed and salted email addresses or phone numbers. Maintained by the IAB Tech Lab, originally developed by The Trade Desk. Designed as an industry-wide cookie replacement.

In practice. UID2 has real adoption in B2C, especially in CTV and authenticated environments. In B2B the picture is patchier because B2B sites have lower authentication rates than streaming services or retailers. Useful as one identifier in a stack, not as the only one.

RampID

LiveRamp’s persistent, people-based identifier. Built from authenticated and offline data sources, resolved into a single ID that can be activated across most major DSPs and SSPs.

In practice. RampID has the widest deployment of any commercial identity solution across the open programmatic ecosystem. For B2B specifically, the value is in how well RampID resolves your CRM list against the addressable identity universe inside major DSPs. Onboarding fees, ongoing activation CPMs, and minimum commitments layer on top of media costs in ways that aren’t always visible inside a managed-service IO. Worth a service-side resolution check against your CRM file before contract — the gap between “your data is supported” and “your data resolves usefully” is real, and a five-minute conversation can save a five-figure surprise.

As of May 2026 they have been purchased by Publicis, with unknowns over whether the technlogy will be gated for their customers or not.

ID5

A commercial identity solution that operates as a probabilistic and deterministic identifier across the open web, particularly in environments where authentication is low.

In practice. ID5 sits in a part of the identity stack that most B2B planners never directly interact with, but the resolution rate of the identifier in any given supply path can materially affect addressable reach. Worth asking your DSP or curator which alternative IDs are being recognised inside the bid request, not just which ones are notionally supported.

Identity graph

A database that links multiple identifiers (cookies, MAIDs, hashed emails, RampIDs, UID2s, account-level signals) to a single resolved person or account. The connective tissue beneath modern audience activation.

In practice. Every meaningful audience platform in B2B sits on top of an identity graph of some kind. The quality of the graph determines the quality of every downstream activation. The graph is also the most opaque part of most vendors’ stacks.

Match rate

The percentage of records in a target audience that can be matched to a usable advertising identifier inside the activation environment. Usually expressed as a percentage of the input list.

In practice. Match rate is the most-quoted and least-honest metric in B2B audience activation. Vendor-quoted match rates of 70-80% rarely survive an honest comparison to working ID-level activation rates at the impression level. Realistic ranges in our own book: 22-38% on open-web display, 35-55% on CTV in authenticated environments, 60-75% inside LinkedIn against a well-maintained CRM list, below 15% on mobile in-app. The honest test isn’t the vendor’s quoted match rate, it’s the impressions actually delivered against the target list. Most teams have never asked their DSP to report against that denominator. They should.

Authenticated traffic

Inventory served to users who are logged in to the publisher or platform, providing a persistent first-party signal that doesn’t rely on cookies or MAIDs. Includes most CTV, streaming audio, walled-garden social, and authenticated publisher environments.

In practice. Authenticated traffic is the only segment of the open programmatic supply where identity is genuinely durable. B2B buying stacks should be tilting hard towards authenticated supply where the audience fits.

Fingerprinting / probabilistic identity

Identification methods that combine non-personal signals (browser, device, IP, behaviour) to identify a user with high probability without using cookies or MAIDs.

In practice. Fingerprinting has been pushed to the margins by browser-level countermeasures and by regulator scrutiny. It still operates, particularly in measurement and fraud-detection contexts. Most reputable B2B buying stacks no longer rely on it for primary targeting.

Data onboarding

The process of taking offline or first-party data (a CRM list, a CSV of accounts, a customer file) and matching it into the digital identity environment so it can be activated across DSPs and walled gardens.

In practice. Onboarding is where match rate becomes a daily reality. The shorter the path from your first-party data to the impression decision, the better the match. Multiple onboarding layers compound match-rate decay.

Data Management Platform (DMP)

A category of technology, dominant from roughly 2012 to 2020, that aggregated audience data from multiple sources, built segments, and pushed them out to DSPs and other activation tools. Examples: Salesforce DMP (Krux), Oracle BlueKai, Lotame, Adobe Audience Manager.

In practice. DMPs have been quietly dismantled as a category. The third-party cookie collapse undermined the cross-site audience-building that justified them, and modern CDPs have absorbed most of the use cases that survived. The term still appears in B2B procurement documents and platform overviews, mostly out of habit.

Customer Data Platform (CDP)

A category of technology that unifies first-party customer data from across an organisation (CRM, web behaviour, email engagement, product usage, support interactions) into persistent, ID-based profiles, then makes those profiles available to activation channels. Examples: Segment, mParticle, Tealium, Treasure Data, Salesforce CDP.

In practice. CDPs have become foundational infrastructure for any B2B marketing organisation serious about activating first-party data into advertising. The hard part isn’t usually the CDP itself; it’s the data hygiene work upstream and the integration work downstream that makes the CDP useful. Buying a CDP without committing to that work tends to produce a more expensive version of the data mess you already had.

Retargeting / remarketing

The practice of serving ads to users who have previously interacted with a brand: visited the website, opened an email, watched a video, attended a webinar. Sometimes used interchangeably; technically Google reserves “remarketing” for its own ecosystem.

In practice. Retargeting has been the most over-relied-on tactic in B2B advertising for years. Most B2B retargeting audiences are small (because the qualifying behaviour is rare), saturate quickly (because the same handful of users are reached over and over), and produce attributed conversions that are heavily inflated by non-incremental activity. A reasonable rule of thumb: if a controlled holdout test on your retargeting line shows incremental lift below 30%, the line is mostly attribution theatre. Useful as one layer in a wider stack. Not useful as the spine of a programme, and not useful as a budget defence in front of a finance team that asks the right questions.

Lookalike modelling

A method for finding new audiences that resemble an existing high-value audience, using statistical similarity across behavioural, firmographic, or other attributes. The original at-scale audience extension technique.

In practice. Lookalike modelling in B2B is more limited than in B2C because the seed audiences are smaller, the firmographic universe is narrower, and the platforms with the best lookalike models (Meta, Google) aren’t usually where the B2B buying decision-makers live. Better deployed inside B2B-native data environments where the modelling can use firmographic and technographic signal, not just generic behaviour.

Audience extension

The practice of extending a known audience (a target account list, a CRM segment) into adjacent users or accounts that share defining characteristics but weren’t on the original list. Distinct from lookalike modelling in that extension is usually rule-based (industry, size, technology footprint) rather than purely statistical.

In practice. Audience extension is one of the more useful B2B audience disciplines and one of the most under-used. A target list of 500 accounts can be extended via firmographic and technographic logic into a workable activation universe of 2,000 to 5,000 accounts without sacrificing definitional precision. The constraint isn’t usually the data; it’s the planning rigour to define the extension rules properly.

5. B2B-specific vocabulary

The terms that don’t translate cleanly from B2C. This is where the B2B operator’s job really starts.

Account-Based Marketing (ABM)

A marketing approach that targets specific named accounts as the unit of attention, rather than individuals or generic personas. Coordinates marketing and sales activity around a defined account list.

In practice. ABM as a category includes everything from a sales-led named-account programme with no media at all, through to fully-funded programmatic ABM running against thousands of accounts in parallel. The category label is doing too much work. When someone says “ABM” the first question is which version.

Ideal Customer Profile (ICP)

A definition of the kind of account most likely to become a high-value customer. Built from firmographic, technographic, and behavioural attributes.

In practice. Most B2B teams have an ICP. Few of them have an ICP that’s been updated more than once a year. Static ICPs miss the second-order shifts that show up in pipeline data months before they show up in strategy decks.

Target Account List (TAL)

The named accounts a campaign is designed to reach. Sits downstream of the ICP, sometimes refined further by sales prioritisation or current pipeline status.

In practice. A TAL of one hundred accounts is a different campaign to a TAL of ten thousand. The ratio of TAL size to Total Addressable Market drives almost every downstream activation decision: channel mix, frequency, creative variation, measurement model.

Total Addressable Market (TAM)

The total universe of accounts that could in principle become customers, before any targeting or prioritisation is applied. The denominator that ICP, TAL, and account-list strategy all sit inside.

In practice. Most B2B TAM estimates are built top-down from industry and employee-count filters and tend to be either confidently wrong or comfortably round. A more useful definition for media planning is the operational TAM: the subset of total TAM that you can actually reach with available data, identity, and activation infrastructure. That number is usually meaningfully smaller than the strategy-deck TAM, and the gap is where most B2B media plans quietly break.

Firmographic

Attributes that describe a company: industry, employee count, revenue, geography, ownership structure. The B2B equivalent of demographics.

Technographic

Attributes that describe a company’s technology stack: which CRM they use, which marketing automation platform, which cloud provider, which DSP. Sourced from a combination of detection technology and self-declared data.

In practice. Technographic targeting is one of the most commercially relevant signals in B2B and one of the most variable in quality. Detection methods range from genuinely robust (job-listing scraping cross-referenced with technology footprint scans) to wishful (one mention of a product in a single web page).

Intent data

Behavioural signals indicating that an account is researching or considering a particular topic, product, or category. Sourced from publishers, B2B media networks, search behaviour, and proprietary research environments.

In practice. Intent data has become table stakes in B2B, and the quality variance between providers is the largest unexamined variable in B2B targeting. Same nominal data category from three different providers, three almost entirely different account universes. The difference between a co-op of a few hundred publishers and a network of several thousand is structural. The difference between raw signal access and a pre-scored aggregate is larger again. Run the overlap analysis yourself before believing any vendor’s coverage claim; the results tend to embarrass everyone involved.

Surge

A spike in intent activity on a particular topic by a particular account, relative to that account’s own baseline. The mechanism by which intent providers separate “engaged” from “always engaged.”

In practice. Surge is the most-quoted metric in intent reporting and also the most coarse. A surge based on three articles read by one anonymous user at a 500-person company is treated identically to a surge based on twenty articles read across multiple users at the same company. The underlying volume matters.

Bombora

The largest B2B intent data co-op in the market. Operates a network of around 5,000 B2B publishers and provides intent topic data at the company level. Most major B2B platforms (6sense, Demandbase, ZoomInfo, and others) license Bombora data as a component of their offering.

In practice. Most B2B teams encounter Bombora data through a downstream platform (6sense, Demandbase, ZoomInfo, or similar), not directly. The taxonomy covers roughly 14,000 B2B topics; surge is calculated against a rolling account-level baseline of around 12 weeks. The downstream platforms see the surge score and the topic, then apply their own modelling. Operators with raw signal access see additional dimensions (signal weight, contributor diversity, topic clustering, source publisher) that downstream platforms quietly compress out. The shift to raw signal-level Bombora access has been the basis of meaningful B2B advertising differentiation for the small number of operators with that relationship.

Dark funnel

The portion of buyer behaviour that happens outside any channel you can directly measure: Slack communities, peer recommendations, podcast listening, AI-driven research, private analyst conversations. Sometimes called “dark social” or “self-directed research.”

In practice. Dark funnel activity is now the dominant mode of B2B buying for most categories, particularly software. Marketing teams that build their plans around what they can attribute, attribute themselves into smaller and smaller corners of the actual buyer journey.

Buying committee

The group of stakeholders involved in a B2B purchase decision. Typically six to twelve people for enterprise software, more for capital purchases, fewer for SMB or self-serve products.

In practice. Most B2B campaigns are built creatively for one persona — usually the perceived decision-maker — and then served to whoever is in the audience. Better practice is to plan for the committee from the start: different creative for different roles, different signals, different sequencing.

Account graph

A data structure linking individual identifiers (cookies, IDs, hashed emails, mobile IDs) to the company-level entity they belong to. The B2B-specific equivalent of an identity graph.

In practice. The account graph is the prerequisite for any serious B2B audience activation. Without it, you can target known persons (which is small) or anonymous traffic (which is unfocused). With it, you can target accounts.

Signal

Loose industry term covering any behavioural or attribute data point indicating a possible buyer state. Includes intent topics, surge data, technographic changes, news events, hiring signals, funding signals, web behaviour, and email engagement.

In practice. “Signal” has become the dominant vocabulary in B2B marketing because it captures the shift from MQL-style discrete events to continuous, multi-source evidence. The risk is that the term has been stretched so far that it now means everything and nothing. Useful signal is signal that’s specific, recent, and weighted against an account baseline.

Engagement scoring

A model that aggregates multiple signals into a single score representing the likelihood or readiness of an account to engage. Underpins most account prioritisation in modern B2B systems.

In practice. Most engagement scoring models are additive (more signal equals more score). The better models are multiplicative — they require multiple corroborating signal types to fire before scoring an account high. Multiplicative models produce smaller, more reliable target lists. Additive models produce big lists with a lot of noise.

Account-level frequency

The number of times any user inside a target account is exposed to a campaign, regardless of which specific person. The right unit of frequency measurement in B2B, and the one almost no DSP reports on natively.

In practice. A campaign that hits a target account 12 times across four different employees is a different experience to a campaign that hits the same employee 12 times. The first looks like presence; the second looks like harassment. Most DSPs cap at the cookie or device level, which is neither of these. Getting to true account-level frequency reporting usually requires either a B2B-native activation layer that resolves identifiers to the account before delivery, or a custom log-level analysis after the fact. Worth the effort: it’s the metric that turns “we’re running ABM” into “we know what our target accounts have actually been exposed to.”

Signal decay

The rate at which an intent or behavioural signal loses predictive value as time passes from when it fired.

In practice. Most B2B teams treat signals as durable for weeks or months because that’s the cadence at which platforms surface them. The honest decay curve is much steeper. Research-based intent signals typically lose half their predictive value inside 14-21 days; account-level surge signals can decay within a week if not corroborated by additional activity. Acting on a four-week-old signal is acting on a different account state than the one that generated it. The activation cadence should match the decay curve, not the platform’s refresh cycle.

6sense and Demandbase

The two largest B2B “ABM platforms,” each combining intent data, account identification, audience activation, and (in some configurations) advertising execution. Heavy enterprise footprint. Often treated as default infrastructure inside larger B2B marketing organisations.

In practice. Calling these platforms “ABM platforms” understates what they actually are: they’re data-and-orchestration layers that sit between a CRM, a marketing automation platform, and the activation channels. Whether you also use them as your advertising execution engine is a separate, often debatable, decision. The intent data refresh cadences differ from each other and from raw co-op feeds; for time-sensitive opportunities the latency between buying-committee research activity and signal appearing in the platform can be days to weeks. For an account already in active pipeline that’s fine. For competitive opportunities where you’re trying to surface a new account before a rival does, it usually isn’t.

Demand generation

Marketing activity aimed at building awareness, interest, and consideration across a target audience, typically over longer time horizons and broader audiences than direct response. The B2B equivalent of brand and mid-funnel activity combined.

In practice. “Demand gen” in B2B has come to mean almost anything that isn’t pure direct response or sales-led outbound. The risk is that the term swallows so much activity that none of it is held to a sensible measurement standard.

Lead generation

Marketing activity focused on capturing a specific contact’s details, usually via a gated asset, form fill, or sign-up. The historic engine of B2B marketing pipelines.

In practice. Lead generation as a standalone discipline has been quietly losing ground for years. Most B2B buyers will not fill in a form to access a piece of content that’s freely available elsewhere. Lead-gen as a tactic still works in narrow contexts (high-value gated research, webinars with genuine subject-matter authority). Lead-gen as a marketing organising principle is past its sell-by date.

MQL (Marketing Qualified Lead)

A lead that meets a defined set of behavioural or firmographic criteria, indicating to marketing that they should be passed to sales for follow-up. Historically the unit of handoff between marketing and sales.

In practice. MQLs were designed for a world where filling in a form was a strong signal of interest. That world isn’t this one. Many B2B teams have moved or are moving to account-level handoffs (Marketing Qualified Account, or MQA) or signal-based handoffs, where the unit isn’t a single contact but a pattern of activity at the account.

6. Data and signal sources

The categories of data that flow into a B2B activation, and the structures that hold them.

First-party data

Data collected directly by the company that owns the relationship: CRM records, website behaviour, email engagement, product usage, customer-service interactions. The most reliable data type, and the most under-utilised in most B2B stacks.

In practice. First-party data is the single most defensible audience asset a B2B marketing organisation has, and the one most often left sitting in a CRM rather than activated. The work to make it usable (clean records, persistent identifiers, identity resolution, consent management, activation pipes) is unglamorous and slow. The marketing teams that have done it have a structural advantage over the ones that haven’t.

Second-party data

Another company’s first-party data, made available to you under a direct commercial arrangement. Rare in B2B because most companies are wary of sharing customer-level data even with partners.

In practice. Second-party arrangements are growing in B2B as data clean rooms make them mechanically possible without raw data being exchanged. The most common B2B example is event partnerships, where a conference organiser shares attendee or session data with sponsors under a clean-room contract. Worth pursuing when the partner has audience overlap you can’t otherwise reach.

Third-party data

Data licensed from a vendor that has aggregated it from many sources. Includes intent providers (Bombora, G2, TechTarget), data brokers, firmographic providers (ZoomInfo, Dun and Bradstreet), and technographic providers (HG Insights, Slintel, BuiltWith).

In practice. Third-party data is the default in B2B activation because first-party data is usually too small and second-party data is structurally hard to share. The quality varies. Treating all third-party data as equivalent is one of the most common mistakes in B2B planning.

Data clean room

A secure environment where two or more parties can match and analyse data at the user or account level without either side seeing the raw records. Examples: Snowflake clean rooms, AWS Clean Rooms, LiveRamp Clean Rooms, Google Ads Data Hub.

In practice. Clean rooms have moved from “interesting concept” to “infrastructure you need to plan around” in the last two years. For B2B specifically, the use cases are mostly around closed-loop measurement and audience overlap analysis with key publisher partners. The maturity of the tooling is improving fast.

Data co-op

An arrangement where multiple companies contribute data to a shared pool in exchange for access to the aggregate. Bombora’s B2B publisher network is the canonical example: publishers contribute reading behaviour, Bombora resolves it to account-level intent signal, all contributors get access.

In practice. The economics of co-ops only work if the data being contributed is roughly fungible. They tend to break down when one participant contributes substantially more or higher-quality data than they extract.

Topic taxonomy

A structured list of topics or interests against which intent or content data is classified. Bombora’s taxonomy covers several thousand B2B topics. The IAB Content Taxonomy is a broader-purpose framework.

In practice. The granularity of the taxonomy matters. Targeting “cloud infrastructure” is meaningfully different to targeting “Kubernetes managed services for financial services.” Some platforms allow custom-topic creation, most do not.

7. Channels and formats

The places programmatic shows up. Each channel has its own quirks for B2B specifically.

Display

Banner advertising on web pages. The original programmatic channel. Includes a range of standard IAB-defined ad sizes, with the medium rectangle (300x250) and the leaderboard (728x90) being the most common.

In practice. Display in B2B is rarely the channel that drives the headline number, but it’s almost always the channel that sustains presence at scale. The mistake is judging it on the same metrics as paid search.

Video (in-stream and out-stream)

Video advertising delivered programmatically. In-stream sits inside other video content (pre-roll, mid-roll, post-roll). Out-stream sits inside non-video environments, usually as a player embedded in an article.

In practice. In-stream video on premium B2B publishers is a strong format for awareness and brand-building. Out-stream is more variable, with quality dependent on the publisher’s player implementation and viewability discipline.

Connected TV (CTV)

Programmatic video delivered to a streaming environment on a television: smart TV apps, streaming sticks, gaming consoles. Authenticated, large-screen, full-attention.

In practice. CTV in B2B is mostly used for executive targeting and high-consideration awareness against named accounts. Reach is real but small relative to display. Typical B2B CTV CPMs in 2026 sit between £35-£55 for premium streaming environments, with sports and news inventory pushing higher. The economics make sense for high-ASP categories where individual account influence is worth £25-£50 per impression on a small TAL. They rarely make sense for high-volume mid-market campaigns where the same budget against the same outcome works harder in display and audio.

OTT (Over-The-Top)

A broader category covering any video content delivered over the open internet rather than through traditional cable or broadcast distribution. Includes CTV (television-screen viewing) as well as mobile and desktop streaming.

In practice. OTT and CTV are often used interchangeably in casual conversation; the distinction matters when you’re planning specifically against the big-screen environment versus the broader streaming universe. For B2B campaigns aimed at executive audiences, the CTV subset is usually the more relevant target.

Digital Out-of-Home (DOOH)

Outdoor digital screens (airports, taxis, billboards, office buildings, transit) bought programmatically. Often anonymised, but increasingly with location- and time-based audience signals layered in.

In practice. DOOH for B2B works in specific high-value contexts: executive commuting corridors, financial districts, conferences and event venues. The buying mechanics are still less mature than display, which is both an opportunity and a risk.

Programmatic audio

Audio advertising delivered programmatically: streaming music platforms, podcasts, broadcast radio simulcasts. Includes both pre-recorded host-read podcast ads (rarely truly programmatic) and dynamically inserted streaming audio (genuinely programmatic).

In practice. Audio for B2B is genuinely underused. Authenticated listener environments, long session times, and an attention regime that’s hard to find elsewhere. Suited to brand and senior executive reach.

Native

Ad formats designed to match the look and feel of the surrounding editorial content. Includes both feed-style native (in-feed content recommendation units) and brand-content placements styled as articles.

In practice. Native in B2B has a chequered reputation, partly because of the historical association with low-quality content recommendation networks. The format itself is fine. The supply quality is what matters. A native unit served inside a respected B2B trade publication is a different proposition to the same unit served inside a content-recommendation widget at the bottom of a tabloid article. Most modern B2B teams use native sparingly and only inside curated supply.

Contextual

Targeting based on the content of the page being viewed, rather than the identity of the user. Modern contextual is far more sophisticated than the keyword-matching of a decade ago, using natural-language understanding to classify pages by sentiment, topic, and entity.

In practice. Contextual is one of the few targeting methods that survives identity collapse intact. It’s also, despite years of vendor claims to the contrary, not a serious substitute for identity-based targeting at scale in B2B. The audiences are too broad, the targeting too coarse, the buying-committee resolution non-existent. Useful as a complement, not a replacement.

In-app

Advertising delivered inside mobile applications. Authenticated where the app requires sign-in, anonymous where it doesn’t.

In practice. In-app is a significant share of mobile programmatic supply but a relatively small share of B2B activation, because most B2B research and buying behaviour happens on desktop or in authenticated work environments. Worth including for completeness rather than as a B2B priority channel.

Reach / unique reach

The number of distinct users (or in B2B, distinct accounts) exposed to an advertising campaign at least once over a defined period. The complement to frequency in the reach/frequency planning model.

In practice. Most B2B campaigns under-report reach because they measure it at the cookie or device level rather than the account level. Account-level unique reach is the more useful metric for B2B planning, and the gap between the two can be substantial: a campaign that reached 50,000 unique devices might have reached only 12,000 unique accounts. The denominator matters.

8. Measurement and attribution

How you tell what worked. Where most B2B campaigns either over-claim or under-measure, often both at once.

Cost per Mille (CPM)

The cost of one thousand ad impressions. The default unit of programmatic media pricing.

Viewable CPM (vCPM)

The cost of one thousand viewable impressions, where viewability is defined by the IAB standard. Adjusts CPM for impressions that didn’t actually render in view.

In practice. Most B2B buying happens on standard CPM, but vCPM is the more honest unit for comparison across publishers. A 5 CPM at 50% viewability is more expensive in real terms than an 8 CPM at 90%.

Click-Through Rate (CTR)

The percentage of impressions that resulted in a click on the ad. Historically the headline performance metric, increasingly a vanity metric.

In practice. CTR in B2B display is structurally low (0.05 to 0.15 percent across most placements is normal) because the audiences aren’t there to click. Treating CTR as the primary indicator of programmatic success in B2B is a misalignment of the metric to the medium.

Cost per Click (CPC), Acquisition (CPA), Lead (CPL), View (CPV)

Pricing or reporting models indexed to the specific action. CPC for clicks, CPA for completed conversions, CPL for captured leads, CPV for completed video views.

In practice. B2B campaigns are almost always reported on a CPL or CPA basis internally, even when the underlying media is bought on CPM. The translation creates room for misunderstanding: a CPL of £200 against an average deal size of £80,000 looks expensive to a marketing operations team and looks like a rounding error to the CFO. Reporting on more than one cost basis is usually the right call.

Return on Ad Spend (ROAS)

Revenue attributed to advertising divided by ad spend. Most often used in B2C and retail. Less directly applicable in B2B where attribution windows are long and revenue attribution is multi-touch.

Multi-Touch Attribution (MTA)

A measurement approach that distributes credit for a conversion across multiple touchpoints in the buyer journey. Includes a range of weighting models: linear, time-decay, position-based, data-driven.

In practice. MTA in B2B is mostly broken. The cookie attrition that undermined B2C MTA undermines B2B MTA more severely because B2B journeys are longer. Most modern B2B measurement leans on a combination of incrementality testing and marketing mix modelling rather than MTA as the primary attribution engine.

Marketing Mix Modelling (MMM)

A top-down statistical approach to measuring the contribution of marketing channels to business outcomes. Uses aggregate spend, exposure, and outcome data, with no requirement for user-level tracking.

In practice. MMM has returned to prominence as identity has eroded user-level tracking. Modern MMM tooling (Meridian from Google, Robyn from Meta) is more accessible than the old enterprise-MMM consulting projects. For B2B, MMM is still under-deployed relative to its usefulness.

Incrementality

The measurement of whether an advertising exposure caused an outcome that wouldn’t have happened otherwise. Distinct from attribution, which assigns credit for outcomes that did happen.

In practice. Incrementality testing is the most honest form of advertising measurement available. It’s also under-used in B2B because the test design (split-cell, geo-holdout, conversion-lift study) requires either scale or patience that most teams don’t apply. The teams that do test rigorously are usually surprised by how much of their “attributed” volume turns out to be non-incremental.

Lift study

A specific kind of incrementality test, usually run inside a walled garden or DSP, that compares outcomes between an exposed group and a held-out control group.

REDS (Raw Event Data Stream)

The Trade Desk’s log-level data feed, providing per-impression, per-event data including bid, win price, supply path, audience match, and outcome. Available to advertisers and partners under specific agreements.

In practice. REDS data reveals two things every time you look at it for the first time. First, you bought more inventory from fewer supply paths than you thought, with the top ten paths typically accounting for 60-80% of impressions. Second, the paths that look statistically efficient on the surface (high win rate, low CPM) are often the same ones with elevated IVT and MFA exposure. Most B2B advertisers buying through The Trade Desk never request REDS access, which is one of the larger missed opportunities in modern B2B measurement. The access is contractual and the analysis is non-trivial, which is why most agencies don’t volunteer it.

Closed-loop measurement

A reporting structure that ties advertising exposure all the way through to a downstream business outcome (pipeline created, opportunity progressed, revenue closed). Requires connection between the ad-server data, the CRM, and ideally the financial system.

In practice. Genuine closed-loop measurement in B2B is rare because it requires data plumbing across functions that don’t normally share data. The teams that build it are usually able to make the case for their marketing budget on much firmer ground than the teams that don’t.

Pipeline attribution

The B2B-specific version of attribution that traces marketing activity through to qualified opportunities and revenue rather than stopping at leads or conversions.

View-through

A conversion or outcome credited to an ad that was viewed but not clicked. Distinct from a click-through, which requires a click.

In practice. View-through reporting in display is notoriously generous. Most reputable B2B measurement applies aggressive view-through windows (often single-digit days) and uses incrementality to validate that view-through claims are real rather than coincident.

Frequency cap

A limit on how many times a single user (or account, in B2B) is shown a particular ad over a defined period.

In practice. Frequency capping in B2B is harder than B2C because the underlying identity is more fragile and the unit (the account, comprising multiple identifiers across multiple people) is more complex. Most B2B campaigns are either under-capped at the user level (over-frequenting a small set of identified users while missing the rest of the buying committee) or over-capped at the account level (failing to build any presence with anyone). A defensible starting frame for B2B display sits around 8-12 impressions per user per week, with the campaign-level account-frequency target much higher than the per-user cap — see account-level frequency. The right answer is almost never the DSP default.

9. The terms reshaping the space in 2026

A short cluster of newer terms that didn’t exist or didn’t matter five years ago, and that are increasingly load-bearing now.

Agentic AI / agentic ad tech

Advertising technology where significant elements of campaign setup, optimisation, and decision-making are handled by AI agents acting semi-autonomously. Distinct from the broader use of ML in optimisation, which has been standard for a decade.

In practice. Agentic ad tech in 2026 is still mostly marketing language laid over capabilities that aren’t yet fully agentic. The trajectory is real, the current state is over-claimed. Worth tracking. Not yet worth restructuring a buying programme around.

Answer Engine Optimisation (AEO) / Generative Engine Optimisation (GEO)

The practice of structuring content to be discovered, cited, and surfaced by AI answer engines (ChatGPT, Perplexity, Claude, Google AI Overviews). The successor discipline to SEO in environments where the user gets an answer rather than a list of blue links.

In practice. GEO matters for B2B because the buying journey increasingly starts with a question typed into an AI, not a query typed into a search engine. The content patterns that get cited (structured, primary-source, definitive, recent) are different from the content patterns that ranked under classical SEO.

Authenticated audience

Audiences resolved through logged-in or otherwise authenticated identifiers, as distinct from inferred or probabilistic identifiers. Increasingly the only durable B2B audience category.

Audience containerisation

An emerging architectural pattern where audiences (defined by intent, firmographics, technographics, or behaviour) are packaged into deal IDs or other deliverable containers, so they can be activated across multiple buying channels with consistent definition and measurement.

In practice. Containerisation is the operational answer to the question “how do I activate the same audience across The Trade Desk, DV360, and StackAdapt without rebuilding it three times?” It also makes measurement comparison possible across DSPs in a way that traditional audience builds don’t.

Signal envelope

The cumulative pattern of signals attached to an account over time, treated as a structured object rather than as a feed of discrete events. The basis for the multiplicative engagement-scoring approach.

In practice. Signal envelopes are how modern B2B operators think about account state. Rather than reacting to each signal as it lands, you maintain a continuously updated picture of where each account sits and what would change that.

B2B Marketing Operating System

A term increasingly used to describe the integrated stack of audience, activation, measurement, and orchestration capabilities required to run modern B2B marketing. Distinct from individual point tools.

Benchmarks worth knowing

These are rough operating ranges from our own book and the wider public datasets, accurate to mid-2026. Treat them as the numbers you should be testing your own stack against, not as absolutes. If yours are materially worse, there’s probably a recoverable problem. If yours are materially better, double-check the methodology before you celebrate.

Working media

• Open-exchange display through a typical agency-managed stack: 55-68%

• Curated PMP via a competent operator: 68-82%

• Direct programmatic with named publishers: 78-90%

• LinkedIn (walled, all-in CPM): not directly comparable; treat as a separate line

Typical B2B CPMs

• Open exchange display: £4-£10

• Curated B2B PMP display: £6-£18

• Premium B2B publisher PMP (FT, WSJ, Bloomberg): £18-£40+

• Programmatic audio (streaming, B2B-relevant): £8-£18+

• CTV (B2B premium environments): £35-£55+

• LinkedIn (CPM-equivalent): £35-£90 depending on targeting depth

Match rates (against a well-maintained CRM list)

• Open-web display: 22-38%

• CTV in authenticated environments: 35-55%

• LinkedIn: 60-75%

• Mobile in-app: below 15%

Viewability

• Acceptable display target: 70%+

• Acceptable video target: 90%+

• Below 50% on either is a problem to diagnose, not a number to optimise around

Supply chain hops between DSP and publisher

• Best case (direct deal, named publisher): 1-2

• Typical open exchange: 3-5

• Worst case (reseller chains, MFA-adjacent paths): 6+

Frequency

• Per-user-per-week display (B2B): 8-12 impressions

• Per-account-per-month total (active campaign): 40-80 impressions across the buying committee

• Anything north of 20 impressions per user per week is starting to burn the brand it’s trying to build

Retargeting incrementality

• Anything below 30% measured incremental lift on a holdout test: mostly attribution theatre

• 30-50%: genuine but optimisable

• Above 50%: rare in B2B, worth understanding why before scaling

Intent signal decay

• Research-based intent half-life: 14-21 days

• Account-level surge half-life (without corroborating signal): 5-7 days

• Acting on a signal older than 30 days: usually acting on a different account state

These are the numbers we keep on a wall. If yours are dramatically different, the gap is the brief.

How this glossary will keep evolving

The vocabulary of B2B programmatic moves faster than the vocabulary of almost any other commercial discipline. Terms shift meaning every twelve to eighteen months. Some entries above will need rewriting by the end of the year. New entries will be added.

If you spotted something that’s wrong, missing, or outdated, reply to this email. Every correction gets read, considered, and (where warranted) folded in. The version above is 1.1; the next update will land in the same place. Bookmark the URL, not a screenshot.

Found this useful? Forward it to the colleague who keeps asking what a deal ID is, the one who insists CTR is a B2B performance metric, or the CFO who wants to understand where the programmatic budget actually goes. They’ll thank you. Possibly.

How to cite this. Harty, M. (2026). The B2B Programmatic Glossary. The B2B Stack. theb2bstack.com

Written by Mike Harty, fifteen-year veteran of B2B programmatic and founder of FunnelFuel.io. The B2B Stack is a practitioner-led publication for the people running modern B2B marketing stacks. FunnelFuel is the managed service that runs the stack described in this glossary, day in, day out, for B2B advertisers worldwide.

The regular Friday briefing returns this week.