“Intent data” has become one of those terms that means everything and nothing. Every data vendor offers some version of it. The pitch is usually some variation of: “We can tell you which companies are actively looking to buy what you sell.”

The reality is more complicated. Understanding what intent data actually tells you, and what it doesn’t, matters for how you build your outbound process.

What intent data actually is

Most B2B intent data comes from one of a few sources:

Topic-based intent (e.g., Bombora, G2). These providers track anonymous web browsing activity across a network of publisher sites and B2B review platforms. When a company’s employees are consuming content about, say, “sales automation” at a rate above baseline, that company gets flagged as showing intent for that topic.

First-party intent. This is activity on your own properties: website visits, content downloads, webinar attendance, pricing page views. It’s more reliable than third-party data because you know the context, but it only captures people who’ve already found you.

Engagement intent. Email opens, ad clicks, social interactions. These are behavioral signals from your own campaigns.

Technographic/change signals. A company adopts a new tool, posts a relevant job, or announces a relevant initiative. These aren’t “intent” in the strict sense. They don’t mean someone is actively shopping. But they signal a context where buying becomes more likely.

The problem with probabilistic intent

Topic-based intent data is probabilistic. It tells you that employees at Company X were reading content about Topic Y. It doesn’t tell you:

• Who at the company was reading (often it’s IP-based, so you get the company but not the person)
• Why they were reading (research? curiosity? a competitor’s marketing team?)
• Whether they’re actually in a buying cycle (reading about a topic is not the same as evaluating vendors)

So intent data works best as a prioritization signal, not a qualification criterion. It can help you decide which companies to look at first, but it shouldn’t be the reason you decide to reach out.

The danger is treating intent as qualification. “They showed intent for our category, so they must be a good prospect” skips the most important question: is this company actually a fit?

Intent vs. qualification

Intent answers: “Is this company doing something that suggests they might be interested?”

Qualification answers: “Is this company the right kind of company for us to pursue?”

Different questions. A company can show intent without being qualified (they’re reading about sales automation, but they’re a two-person startup with no budget). And a company can be qualified without showing intent (they’re a perfect fit, they just haven’t started looking yet).

The best outbound combines both, but the order matters. Qualification first, intent second. Start with companies that are actually a good fit. Then, among those, prioritize the ones showing relevant signals. The reverse, chasing intent signals into companies that were never going to buy, is how you fill a pipeline with noise.

Where intent signals actually help

Intent data is most useful when you’ve already done the qualification work and need to decide timing and priority:

Prioritizing outreach. You have 200 qualified leads. Which 20 should you contact this week? The ones showing intent signals get bumped up.

Personalizing messaging. If you know a company is researching a specific topic, you can reference that in your outreach. “I noticed your team has been evaluating compliance solutions” is more relevant than a generic pitch (assuming you can say this without being creepy about how you know).

Triggering re-engagement. A qualified lead that went cold is now showing intent again. Time to follow up.

Validating qualification. If a company passes your qualification criteria and also shows intent, that’s a stronger signal than either one alone.

The explicit approach

Instead of relying on probabilistic intent signals to drive targeting, consider making your qualification logic explicit:

Define the questions that determine fit. Not “do they look interested?” but “do they have the problem we solve?” and “do they have the team that would use our product?”

Evaluate these questions against data: company websites, job postings, public filings, data providers. Produce a structured result with evidence for each one.

Use intent signals to prioritize within the qualified set, not to define the set.

This approach has a few advantages.

First, you can explain exactly why you targeted a company. With intent data alone, the best you can say is “our data provider told us they were interested.”

Second, explicit qualifiers produce the same evaluation framework every time. Intent signals are noisy and fluctuate week to week.

Third, there’s the compliance angle. In GDPR jurisdictions, “a third-party provider flagged them as showing intent” is not a strong basis for a legitimate interest claim. “We evaluated them against specific qualification criteria and documented the evidence” is much better.

Finally, when your qualification logic is explicit, you can track which qualifiers predict closed deals and which don’t. Intent data is harder to learn from because it’s a black box.

Combining the two

Intent data isn’t useless. It just serves a different purpose than most people use it for.

Use explicit qualification to define your target. Use intent signals to prioritize and time your outreach. Keep the two separate conceptually, even if they inform the same workflow.

If you’re using BDR², the qualifier system handles the qualification layer: explicit questions, structured evidence, auditable logic. Intent data from your providers can layer on top as a prioritization signal, but it doesn’t replace the core question: is this company actually a good fit?

Chasing intent without qualification fills your pipeline with noise. Qualifying without intent gives you good leads but bad timing. You need both if you want conversations that actually go somewhere.