The Reality of ABM Report

The promise

Every ABM program starts from the same promise: go after the right accounts, and everything else will follow. It's supposed to go a little something like this...

That idea built an entire category. It's why ABM gets its own budget line, why a generation of platforms got funded, and why a lot of us rebuilt our teams around a named account list. And the core of it is right. Spending your time and money on the accounts most likely to buy beats spreading it thin across the market and hoping some of it sticks.

That's how it's supposed to work. Whether traditional ABM platforms are actually equipped to pull it off is a different story. So we asked about every step: how you decide which accounts get your attention, whether you can name the real buyers inside them, whether you can explain why one looks ready, how fast you can act on it, and what happens when your CRO asks you to prove it drove growth.

TL;DR: Not one step came back clean. The strategy is sound, but the way it gets run today breaks down at every stage. Read what follows with your own program in mind.

The state of ABM programs today

First, some context on who answered. Nobody in this survey is playing around.

These are hard deals on long timelines. Nearly a third are working 6- to 12-month cycles, and 11.7% are past a year. The contract values match: Nearly half won't put an account into an ABM program below $50K, and 21.8% draw the line at $100K or more.

So this is patient, expensive work. Teams are putting substantial budgets behind a restricted list and then waiting a couple of quarters to find out if they got it right. Most teams keep that list tight.

Nearly a third run 50 to 200 accounts. Another quarter run 200 to 1,000, and 24.7% run more than that. Per rep the numbers are more contained: 22.2% assign 10 to 25 accounts each, and 15.9% assign fewer than 10.

Most run a blended motion rather than a purist one. 36.4% describe their approach as a mix, 21.8% as 1:many, 21.3% as 1:few, and only 7.1% as strictly 1:1.

Are your priorities mixed up?

Every ABM program runs on a list. Marketing and sales agree on which companies are worth pursuing, usually built from firmographics and fit like industry, size, revenue band, tech stack, or whether they look like the customers you already have.

When we asked marketers what gets in the way of ABM succeeding, knowing which of these accounts to prioritize came out on top, ahead of sales alignment, personalizing at scale, and proving impact.

Building the list is straightforward. Knowing which of those accounts deserves your attention right now is the harder part.

Firmographics might get you a list, but they don't tell you which accounts on it deserve the budget this quarter, and that's the harder call. Most teams are choosing between 200 accounts that all technically qualify.

When we asked what frustrates marketers most, and what would make the biggest difference in their day-to-day work, prioritization came up again and again.

What separates the account you should pursue this week from everything else that can wait is what the people in those accounts are doing now: who is researching, who is reading, who just changed jobs into a role that needs what you sell. Prioritization is hard because that layer is missing, and without it all you have is a list of companies that look right on paper.

Finding the people who matter

Companies don't evaluate software. They don't forward decks to their boss or push back on pricing. People do. Finding those people is the work of ABM, but it's notoriously difficult.

Titles might get you close, but they don't tell you the full story. The person who owns the problem or has final say isn't always the person you thought it would be, and org charts go stale fast. Manual research gets you names but says nothing about intent.

Known engagement helps here. Seeing that a specific director from a target account read three pages on your site last Tuesday is valuable information you can use to identify your buying committee and reach them.

Turns out that's a capability most teams don't have. We asked what they primarily rely on to identify the buying group inside a target account:

In other words, the majority of teams are inferring who the buying group is, whether from titles, from research, or from a conversation with sales, instead of confirming it through verified engagement.

When asked what frustrates them about their ABM program, one respondent said:

That blind spot means marketers are often running their ads in the dark, wasting budget on people who will never convert into buyers.

The largest group, 59.0%, say they're "somewhat confident" their ads are reaching the right people inside an account, which is a fair position to be in. They're targeting relevant roles and functions, and many of those impressions will land on people who matter—but they can't verify it person by person.

Only 8.8% can. Below that, nearly a quarter say targeting happens mostly at the account level, which in practice means paying to reach anyone who happens to work there, and another 6.3% have essentially no visibility into who gets reached at all.

In a strategy that requires precision, roughly nine in 10 teams are still moving money largely based on assumptions.

One cut of the data suggests a solution.

Among teams that treat website de-anonymization as central to how they score and prioritize accounts, 88.9% say they're somewhat or very confident their ads reach the right person. For teams not using it at all, that drops to 48.5%.

Give marketers the ability to see the people already showing up on their site, and confidence that their ad budget is reaching actual buyers nearly doubles.

The score moved. Do you know why?

If your team gets past the first two steps—the list is prioritized, the buyers are identified—now the system has to tell you when to act, and that's the job of a score or a stage.

Most tools are a black box when it comes to scoring. Behaviors get assigned point values, thresholds get set, and when an account crosses one it moves to the next stage.

You see the output, but you don't see what went into it: which behaviors moved the number, when they happened, whether it was one big signal or six small ones, whether the person doing the engaging is someone who could buy. The score arrives without any visibility into the reasoning that produced it, which puts you in the position of being asked to act on something you can't fact-check or verify in any meaningful way.

Only 18.4% of teams can almost always explain why an account's stage or score changed. Almost half can explain it only sometimes. Roughly one in six can rarely explain it, and another 18.8% aren't tracking closely enough to know either way.

For more than four out of five teams, the number moves and the reason behind it is either partially or entirely opaque. The cost of this black box is inertia and the risk of acting too late, when the opportunity has passed.

Just 2.9% of teams would move on a score change with no further validation. The most common answer, at 45.6%, is that the change is directional and needs investigating first. Another 13.4% rarely act on it or don't rely on it at all, and 8.4% have stopped using scoring altogether.

Respondents describe the issue this way:

Speaking of signals, another respondent put it bluntly: "Buying signals are overrated."

That skepticism makes sense. Signals that feed a black box with no evidence trail don't exactly inspire confidence, as the data above shows.

But the data does suggest a fix. Trust in scoring climbs steadily as the number of distinct buyer signals a team tracks increases. Among teams tracking only one to three signals, 17.5% trust a score change enough to act on it. At four to six signals, that rises to 32.5%. At seven to 10, 48.6%. Among teams tracking 10 or more, 60%.

The inverse holds, too. Nearly one in five teams tracking only a handful of signals have abandoned scoring altogether. Among teams tracking 10 or more, not a single respondent had.

A score determined from three inputs is still questionable, but a score built from 12 inputs, where a team can point at hiring activity, a pricing page visit, and competitor research that moved it, is more solid proof.

Isolated buying signals might be "overrated," but with more depth and coverage, those signals turn into a story that's a lot harder to ignore.

Knowing doesn't translate to action

Even if a team has figured out which accounts matter, knows who's inside them, and trusts the signals, survey data shows that this intelligence isn't being reliably activated.

Fewer than one in 13 teams have a system where meaningful buyer activity automatically changes something. Nearly one in five say the activity usually doesn't turn into any action whatsoever—the signal fires, someone maybe sees it, and nothing happens. Another 18.4% take three or more business days.

When many teams are working six-month cycles, a three-day delay might not sound like a big deal, but it is. A six-month cycle doesn't mean six months of steady interest. It comes in bursts, and an account deep in research on Monday can be back to ignoring you by Thursday.

One respondent put it this way:

That fragmentation is backed by the data: 55.2% of teams actively use three to five tools in their ABM program, and 6.7% use six or more. But more tools hasn't meant less work. Teams are running full stacks and still doing core parts of the motion by hand.

More than a third of teams build ad audiences by hand. A third handle outbound handoff manually. Almost a quarter monitor signals manually, meaning someone is watching for the thing that was supposed to trigger automatically.

These are the exact steps that stand between a signal firing and anything happening as a result. Every time one of them is handled by hand, it means the motion stalls until someone has the time to spend on it.

The promise of ABM implied a steady machine, where buyer movement is turned into a refreshed audience, an active campaign, a targeted nurture sequence, or a warm handoff to sales. But that process is still far too manual.

Automation sits at the top of the list when respondents are asked what would make the biggest difference, named in roughly 17% of responses.

One respondent had this request:

No proof? Big problem

Even when everything goes perfectly, none of it matters if you don't have evidence that it worked. No one wants that pit-in-your-stomach moment of defending a number they can't fully prove—especially when the numbers look like this:

These should be numbers worth celebrating. But without evidence to back them up, they could have big consequences. These are numbers that are read aloud on board calls and used to justify next year's budget and headcount. Yet when someone senior pushes back on them, most teams don't have a strong defense.

Just 15.9% can immediately show evidence everyone in the room agrees on. Nearly a third say the number holds up but only after digging. A quarter say marketing and sales look at the same evidence and reach different conclusions, which is the hardest version to resolve. And 22.2% have to reconstruct the story manually, probably under pressure.

The pipeline number is there. What's missing is a version of it that stands up to scrutiny. Take it from this respondent:

We asked respondents what outcome they'd most want to be true if their program worked exactly right.

These are all requests for a defensible cause-and-effect story.

Asked what would make the biggest difference in their day-to-day work, many respondents pointed to this challenge:

The case for a single system

There's a natural experiment hiding in the data.

What happens when a team has every step of the motion covered by a tool: scoring, account selection, research, nomination, routing, reporting, ad audience building, signal monitoring, and outbound handoff?

13.4% of respondents describe exactly that setup. At 32 teams it's a small group, so read what follows as directional. But they're the best-resourced cohort in the survey, which makes them a useful test of whether full coverage is the same thing as a working system.

When teams are fully tooled, they explain score changes at twice the rate of everyone else, and defend their pipeline number on the spot twice as often.

Clearly tool coverage helps—but even the best-equipped teams in the survey can't explain their own score changes two times out of three, and can't defend their own pipeline number roughly three times out of four.

To understand why, consider what happens in a fully tooled stack:

When asked what would help, respondents aren't requesting another point solution:

A single system gives teams continuity: the reason behind a score, the audience it triggers, and the pipeline it produces stay attached to each other. When someone asks what happened and what the impact was, the answer is already there.

Asking sales to take your word for it

Two questions in this survey were open-ended. We asked what frustrates marketers most about their program right now, and what one thing would make the biggest difference in their day-to-day work. More than 200 people answered both, in their own words.

These responses are directional rather than statistically precise, since grouping free text into themes involves judgment in a way that counting multiple-choice answers doesn't.

But one theme came up more than any other:

It shows up in the closed-ended data, too. When we asked marketers to name the single biggest bottleneck to ABM success, sales alignment and outbound activation came second at 15.5%, just under a point behind account prioritization.

Maybe these teams just don't communicate enough, you might say. That's not what the data shows.

Nearly half of teams meet with sales weekly, and another 4.2% meet daily. Only 3.8% are down to quarterly. These teams are in a room together plenty enough, yet alignment is still the most common difficulty marketing raises unprompted.

Set this finding against the rest of the report and it makes more sense.

A rep gets handed an account. Marketing can't say which people inside it are engaged, because 47.7% are working from assumed job titles. They also can't say why the account moved, because only 18.4% can consistently explain a score change. The signal that triggered the handoff may be three days old, because only 7.5% of teams operate in real time. And when the deal closes, nobody can agree on what marketing contributed, because only 15.9% can produce evidence both sides accept.

Marketers are handing sales a score and expecting them to take it on blind faith.

From the rep's side, ignoring the score is a rational choice. They could spend their limited time on an account that came in from marketing flagged as important with no clear reason behind it, or they could continue pursuing an account they sourced themselves and fully understand. What they need is irrefutable proof that they would jump at the chance to act upon.

A rep who can see the job change, the pricing page visit, and the competitor research that moved an account won't need to be convinced to make the call, because the evidence is all there.

How does your program compare?

Five questions, taken straight from the survey. Answer them the way the 213 respondents who answered all five did, and we'll score you the same way we scored them.

Finishing what the promise started

Get the account right, and everything else falls into place.

The data in this report suggests that idea was never finished, and legacy tech has failed to live up to the promise of ABM. Each place where it breaks down is a piece of connective tissue marketers are missing today:

The next chapter of ABM requires more clarity and confidence at every stage of the process.

That's a higher bar than it used to be. Budgets are tighter, buying committees are bigger, and there are more vendors competing for the same limited number of accounts. When every account matters more, it's too expensive to be wrong about your strategy.

The legacy tools teams are working with make that challenging. Buyer engagement is anonymous, so who knows if you're prioritizing the right accounts and targeting the right people. Scoring and staging operate from the same obscurity, which makes it hard to determine what to do next.

Workflows are disconnected, so one system isn't learning from the next and action lags. Maybe worst of all, there's no baked-in record of marketing's influence anywhere to be found.

You could say ABM has some trust issues... and they compound at every handoff. The good news is, you don't need years of therapy or a new theory of marketing to fix them.

Commit to removing the blinders and infusing every stage with more transparency, and watch the trust and confidence (and pipeline) grow.