Intent data is the most useful tool in my ABM toolkit. It’s also the one most likely to fool me.
I run Overdrive’s account-based campaigns on intent signals every week. When we first leaned into it, the top of our funnel looked better than it ever had. More engaged accounts. More identified website visitors. More companies “surging” on the exact topics we sell. Then I’d check meetings booked, and that number didn’t move nearly as much.
That gap is the honest story of intent data in ABM. It’s very good at telling you who is reading. It’s not built to tell you who is buying. Treat those as the same thing and you end up with a busy dashboard and a quiet calendar.
What Is Intent Data in ABM, Really?
Intent data is a signal that people at a specific company are consuming content on a specific topic more than they usually do. Providers track content consumption across publisher networks and ad exchanges, tie it back to a company, and compare it against that company’s normal baseline. When reading on a topic jumps, the account “surges.”
So every intent signal comes down to three things: an account, a topic, and a point in time.
Notice what’s missing from that list. There’s no person, no reason, and no budget. Almost every mistake I’ve made with intent data came from filling in those blanks myself.
What Intent Data Tells You That’s Genuinely Useful
Used for what it is, intent data does three jobs well.
It tells you where to look first. If you have 500 accounts that fit your profile and the capacity to work 50 of them properly, intent is a solid way to rank them. It doesn’t replace your list. It sorts it.
It tells you what’s on their mind. The topic matters as much as the surge. An account reading heavily about attribution needs a different first message than one reading about AI search. That’s free direction for your creative and content.
It tells you roughly when to show up. Surges fade. An account researching something this month may have moved on by next quarter. Intent gives you a window, and the window is usually shorter than you’d like.
What Intent Data Doesn’t Tell You (and Where ABM Programs Go Wrong)
This is the part most vendor decks skip.
It doesn’t tell you who. Intent is account-level. Gartner finds that complex B2B purchases involve six to ten decision-makers, each independently gathering four or five pieces of information. A surge could be the CMO building a business case. It could also be one analyst reading for a single afternoon. inblog
It doesn’t tell you why. Researching a topic is not the same as having the problem. People read about things to learn them, to write a presentation, to size up a competitor, or because their boss forwarded an article. We saw this firsthand. Our AEO-focused ABM campaign earned a lot of impressions and a 0.20% click-through rate, because a lot of marketers are reading about AEO right now and most of them are trying to understand it, not hire for it. Our finance and banking campaign, which paired topic intent with a tight industry filter, hit 1.63% on display. Same kind of signal. The difference was fit.
It doesn’t tell you whether they can buy. A company with a small marketing budget can surge just as hard as one with a large one. Intent says nothing about budget, authority, or whether you’re even a realistic option for them.
It doesn’t tell you where they are in the process. Early curiosity and a final shortlist can look identical in an intent report.
Why Intent Data Alone Fills the Top of the Funnel but Not the Pipeline
When a surge gets treated like a lead, a few predictable things happen. Sales reaches out too early. The message opens with some version of “I noticed you’re looking into…” which reads as a little creepy. The account wasn’t ready, so it goes cold, and now you’ve spent a touch you’ll want back later.
Over time, the funnel fills up with accounts that were researching, not buying. Engagement looks great. Conversion doesn’t. I’ve lived this one. Intent made us feel targeted when we were really just busy.
The fix isn’t to drop intent data. It’s to give it a smaller, clearer job.
How to Use Intent Data in ABM Without Fooling Yourself
Start with fit, then layer intent. Build your account list from fit first: company size, marketing budget, business model, and the triggers that make your services relevant. Intent should rank accounts inside that list. It should never be the reason an account gets on it.
Stack signals before you act. One signal is a hunch. Three is a pattern. Look for third-party intent lining up with first-party behavior, like repeat website visits, multiple people from the same company engaging with your ads, or someone returning to a pricing or services page. When those agree, you have something worth acting on.
Use the topic to pick the message, not to announce that you’re watching. If an account is surging on measurement, run measurement content in front of them. Let your ads and content do the early work. Nobody wants to hear that you’ve been tracking their reading habits.
Reach the group, not the contact. Once an account warms up, broaden your targeting to the other roles likely involved in the decision. Finance, sales leadership, and the people who will actually work with you each need a slightly different reason to care.
Give sales context, not a name. A good handoff explains why this account, which topics they’re showing interest in, who has engaged, and what angle to lead with. That’s a conversation starter. A name and a “surging” label is just a cold call with extra steps.
Measure at the account level, and measure downstream. Compare meeting and pipeline rates for intent-prioritized accounts against the rest of your list. If there’s no meaningful difference, the signal isn’t helping you, no matter how good the engagement numbers look.
The Flashlight Problem
Intent data is a flashlight, not a map. It shows you where to look. It can’t tell you where to go.
That part is still a human call, and it’s the one that decides whether your ABM program fills a calendar or just a dashboard.
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