Buying intent

Buying intent data is evidence, not a score

Buying intent data shows that a company is researching, discussing, or moving around a problem your product solves. It should give a rep recent evidence attached to the account, not just a number ranking one company above another.

A score can help sort a list. It can't explain why an account is on it.

Take a 180-person fintech that has just hired a head of compliance and posted three implementation roles in Germany. Its COO has also mentioned, in a public interview, that the current reporting process won't support the company's next stage of growth. That combination gives a rep something to investigate.

A score of 82 doesn't.

The score might be useful for prioritisation. But the useful buying intent data is the hiring activity, the expansion, and the executive's own comment. Those details tell the rep what changed and give them a reasonable starting point for research.

Most teams get this wrong. They treat a vendor's ranking as proof that an account is ready to buy. It isn't proof. It's a system's opinion about a set of observations, some of which may be old, weak, or unrelated to an active project.

What the rep actually needs to know

Before contacting an account, a rep needs answers to two questions:

1. What is the company doing now that relates to our product?
2. What can I say that shows why I picked this company?

A ranked account list usually answers neither one well. It tells the rep where to look, then leaves them to work out the reason.

That is how outreach gets filled with fake relevance. A message includes the company's name, industry, employee count, and a sentence about "scaling challenges." Technically personalised. Practically empty.

A useful record might say:

The company added four compliance roles in the past 60 days, opened a German office, and its COO said the existing reporting process will not support expansion.

Now the rep can check whether the company is evaluating software, hiring someone to solve the problem internally, or simply talking about a future concern. The signal doesn't remove the need for judgment. It gives the rep something real to judge.

Recency matters more than the label

Intent data has a short shelf life. A research visit from nine months ago shouldn't sit beside a new hiring push as if both carry the same weight.

The record should make timing obvious. When was the activity observed? What happened? Is it still relevant? If the data can't answer those questions, a rep has to guess. Most won't spend long guessing. They'll skip the account or send a generic message.

This is one reason a nightly batch isn't always enough. A company can announce a launch on Tuesday, hire a relevant executive on Wednesday, and be contacted the following Monday with none of that context available. By then, the opening may already feel late.

That doesn't mean every team needs minute-by-minute collection. It does mean the refresh schedule should match the buying cycle. A high-volume outbound team selling a low-cost product may work fine with daily updates. A team selling a six-figure platform to a small set of accounts probably needs fresher records and clearer source details.

The source is part of the signal

"High intent" is not an explanation. Neither is "account is active."

The record should show what produced the signal. That could be a company researching a category, an executive discussing a problem publicly, an employee posting about a difficult process, or a relevant leader leaving the business.

These sources don't mean the same thing.

A product launch may point to a new operating requirement. Hiring can indicate that the company is building the capability internally. An executive comment may reveal a concern the business is willing to discuss. An employee departure may matter because that person was likely carrying a project or acting as its internal sponsor.

None of these automatically means a purchase is underway. That's fine. Buying intent data doesn't need to predict a signed contract. It needs to help the team spot meaningful movement and decide what to check next.

For example, suppose a 70-person logistics software company loses its VP of Operations. At the same time, the company starts hiring a director of systems. That isn't a buying signal for every operations product. It is a reason to look at the company's systems, recent announcements, and likely priorities before deciding whether to contact them.

The distinction matters because intent systems often flatten different observations into one category. Once that happens, the rep sees a label instead of the evidence.

Keep the account history clean

Signal collection can create its own mess.

If an enrichment job runs every week and appends the same hiring event each time, one event starts to look like five. The account appears more active because the database copied the observation repeatedly.

That isn't a minor data quality issue. It changes prioritisation and can send reps after accounts for the wrong reason.

A clean setup replaces or updates prior observations from the same source. It keeps the original date, source, and relevant detail without creating duplicates. If a company has been discussing the same issue for months, that history should be visible as history, not represented by a pile of identical records.

The account record also needs to distinguish current activity from background context. Funding, hiring, news, product launches, market expansion, social activity, and research behaviour can all be useful. They shouldn't be mashed into one claim that says the company is "in market."

Collection is an operational problem too

Teams often talk about data quality as if the only question is whether the signal is accurate. The collection process matters just as much.

If a job runs in the background with no status, the team can't tell whether an account has been fully checked, partially checked, or missed because the job failed. A rep sees an empty record and assumes there is no activity. That may be wrong.

For larger account sets, collection should run as a visible process with clear states. Queued. Running. Complete. Failed. That can be simple. The point is to stop incomplete data from looking like negative data.

A separate signal store can help here. It keeps observations in one place while attaching them to the relevant company record. The goal isn't another dashboard. It's preserving the evidence behind the account's priority and making that evidence available when the rep starts work.

A working test for buying intent data

A team probably has useful buying intent data when a rep can open an account and answer these questions without asking an analyst to interpret a score:

• What happened recently?
• Where did the information come from?
• Why does it relate to the problem we solve?
• Is this a new event, an ongoing pattern, or a duplicate?
• What should I verify before contacting the account?

If the record only says "high intent," the team has a ranking. It may be a helpful ranking. It still isn't intent by itself.

The better test is less impressive and more practical: can a rep explain, in one sentence, why this account deserves attention today?