What Is Intent Data in B2B Outbound?

Intent data is evidence that a person or company is currently paying attention to a topic relevant to what you sell.

The word "intent" is overloaded. It gets used for everything from third-party modeled data products to a single LinkedIn comment. So it helps to break it into what it actually is and what it is not.

Two kinds of intent data

The first kind is modeled intent. Tools like Bombora and 6sense track what companies are reading across a network of B2B content sites. When a company's employees read significantly more content about a topic than baseline, the platform flags it as a surge. You get an account-level signal that company X is researching category Y this month.

The second kind is behavioral intent. A specific person did something specific and public. A comment on a LinkedIn post. A question asked in a community. A reaction to content in a specific category. You can see the exact action, the exact person, and the timestamp.

Both are useful. They are not interchangeable.

What modeled intent does well and where it falls short

Modeled intent is good for account-level prioritization. If you are running ABM against a target list, knowing which accounts are surging on a topic helps you sequence your outreach around the accounts most likely to be in an active evaluation.

The limitation is that it is averaged and modeled. The surge data tells you that people at Company X are reading about topic Y. It does not tell you which person. It does not give you a conversation opener. It gives you a reason to move the account up the priority list.

What behavioral signals add

A public behavioral signal is person-level and timestamped. You know who did something and when. That is a different kind of input.

It does not tell you that someone is definitely ready to buy. A person who comments on a post about LLM cost benchmarking might be curious, might be actively evaluating vendors, might be writing an article about the topic. The signal tells you they are paying attention. It does not tell you where they are in a buying process.

What it does give you is a real opener. "I saw your comment on [topic]" is not a fabricated personalization line. It is a reference to something the person actually did publicly. That changes the nature of the outreach.

When behavioral signals work in outbound

They work best when:

The prospect is ICP-matched before you act on the signal. A high-quality comment from someone outside your ICP is still outside your ICP.

The signal is recent. A comment from six weeks ago is much weaker than one from three days ago. Recency matters because attention shifts.

The outreach references the signal specifically, not generically. Saying "you seemed interested in this area" is weaker than referencing the specific topic the person engaged with.

Where intent data fails

Any intent signal is evidence, not certainty. A good comment does not mean the person has budget or authority. A surge on an account does not mean anyone there is in a buying cycle. Acting on signals as if they guarantee intent produces the same spray-and-pray results as acting on a static database, just with better rationalization.

The honest framing: signals improve your selection. They do not determine your outcome. Execution, ICP fit, and timing all still matter.

Richie Reach captures public behavioral signals from LinkedIn, classifies them by engagement type, and filters to ICP before delivery. The signal is the input. It makes the outreach rationale better, not the outcome guaranteed.

If you treat signals as evidence of attention and filter carefully, you get better conversations.
If you treat signals as a guarantee of intent, you get the same results as a cold list with extra steps.