Buyer intent signals: types, examples, and limits

What is a buyer intent signal?

A buyer intent signal is a recorded action or change that could indicate interest, need, timing, or access. Examples include a person discussing a relevant problem, engaging with a competitor's ideas, changing roles, or working at a company that is hiring for a related capability.

The phrase can be misleading. A public action rarely reveals a private purchasing plan. Someone can read a post without evaluating software. A company can hire for a function without opening a budget for your category. A new executive can create change without becoming your buyer.

The accurate question is not, “Does this signal prove intent?” It is:

Does this observable evidence make this person or company worth a closer look under our team's criteria?

That distinction turns a signal from a promise into a research input.

The main types of buyer signals

Different signals answer different questions. Treating them as interchangeable is one reason intent programs become noisy.

Fit is not intent. It is a gate. A perfectly timed event at the wrong company is still a poor opportunity, while a perfect-fit company with no current evidence may belong on a watchlist rather than this week's priority list.

Which buyer intent signals are strongest?

There is no universally strongest signal. A direct request is more explicit than a public like, but it can still come from a student, competitor, vendor, or person with no buying role. A company event can be important for one sales thesis and irrelevant to another.

A signal becomes more useful as six qualities improve:

1. Attribution: you can identify the person or company connected to the event.
2. Recency: the event is dated and current enough for the decision you need to make.
3. Specificity: the evidence concerns the problem, category, or change you actually care about.
4. Fit: the person and company meet your team's requirements.
5. Corroboration: independent observations point in a compatible direction.
6. Reviewability: a seller can inspect the source and explain why the inference follows.

Corroboration increases confidence; it does not convert an inference into a fact. Two weak observations can still produce a weak conclusion. The quality of the sources and the reasoning between them matter more than the raw signal count.

What makes a buyer signal actionable?

A useful signal and an actionable signal are not always the same thing. A precise observation can still stall if the person does not fit, the account is already owned, no one is responsible for the next step, or the evidence reaches a queue without enough context to review it.

Before a signal moves to a rep, check five things:

• Qualification: the person, role, and company match the team's current requirements and exclusions.
• Materiality: the observation is specific enough to change a priority, question, or next move.
• Context: related public evidence and known CRM state do not contradict the proposed action.
• Ownership: the team has decided whether an SDR, AE, founder, marketer, or another operator should review it.
• Brief: the reviewer receives the company tier, person and role, observed evidence, related activity at the same company, why it may matter now, what remains uncertain, and a proposed next step.

This brief is more useful than a raw stream of likes, job changes, and company news. It lets the reviewer accept, reject, reroute, or hold the opportunity without having to reconstruct the reasoning.

A worked buyer-signal example

The following is a hypothetical example, not a customer result.

Team thesis: security operations leaders at mid-market software companies become relevant when alert volume is creating a visible hiring or workflow problem.

Observed: on August 20, a security leader publicly discussed alert fatigue. On August 22, the leader's company posted an opening for a security-operations role. The person's role and company size match the team's stated criteria.

Inferred: the two observations suggest the problem is current and may be receiving organizational attention. The person is worth researching before other fit-matched names with no recent evidence.

Still unconfirmed: the company may be solving the problem through hiring, an internal project, an existing vendor, or no funded initiative at all. The public evidence does not confirm a purchase.

Next move: read the original discussion and job description, check whether the existing stack or stated approach changes the thesis, then ask a question tied to the observed problem. Do not write, “I saw you are in market.”

When person signals change the company reading

Person-level observations can add up to a more useful company-level reading. For example, related activity from several relevant people, paired with a company change, may make an account worth reviewing sooner. Competitor-related activity can also become more informative when it appears across relevant people at the same company instead of as one isolated interaction.

The correct conclusion is not “the account is surging.” The conclusion is narrower: several attributable observations now support a different account hypothesis under the team's criteria. A reviewer should still check whether the observations are independent, whether the people have relevant roles, whether the evidence is current, and whether contradictory company or CRM context changes the interpretation.

This is person-to-company aggregation for research, not a claim that Lumnis automatically detects an account surge or proves a coordinated buying process.

How sales teams should use buyer signals

Start with a team thesis

Define the people, companies, problems, exclusions, and evidence that matter before collecting signals. Without that frame, every event looks potentially important.

Preserve the source and date

A label such as “high intent” is not enough. Keep the source, the event date, the date it was checked, and the person or company it was attributed to.

Apply evidence as a reason, not a verdict

Use the signal to explain why an opportunity moved up, moved down, or stayed on a watchlist. Do not let a vendor score replace the explanation.

Look for disconfirming evidence

An existing strategic commitment, a mismatched geography, a recent role departure, or an explicit statement against your approach can matter as much as a positive observation.

Review before acting

The person taking the next step should be able to inspect the evidence and reject the reasoning. Human review is a quality control, especially when the action would be personal or consequential.

Add the current CRM context

When Attio or HubSpot is connected, Lumnis can show whether the person matches a CRM contact and, separately, whether the company matches an account. Available account context can include an active deal, an existing customer, a marketing record, no active deal, and deal or pipeline stage details when present.

Use that context to avoid duplicate work, interrupting an active deal, or sending an existing customer through a new-business motion. A missing or uncertain match is not proof that no record exists; verify it in the CRM before acting. This is current record context, not automated CRM intelligence or a closed-loop outcome model.

Record what happened next

Track whether the team accepted the opportunity, what it learned, and whether the thesis held. The objective is not to prove that every signal was right. It is to improve how the team reads evidence over time.

Today, that learning should be treated as a deliberate operating review. Compare accepted and rejected opportunities with later engagement, meetings, and pipeline movement, then decide whether the team's criteria should change. Do not assume those outcomes are automatically attributed to the original signal or fed back into Lumnis without a person designing and reviewing that process.

Common buyer-intent mistakes

• Calling fit an intent signal. Industry, headcount, and title can qualify a person or company, but they do not establish timing.
• Treating one public action as a declaration. A view, like, comment, or follow can have many explanations.
• Ignoring the date. An accurate observation can be too old for the decision at hand.
• Counting instead of reading. More signals do not help if they repeat the same weak source or concern an irrelevant topic.
• Hiding the evidence behind a score. A seller cannot responsibly use a conclusion they cannot explain.
• Acting with false certainty. Outreach that says “we know you are buying” turns an inference into an unsupported claim.
• Collecting only positive evidence. A good research process can lower a priority as well as raise it.

Where Lumnis fits

Lumnis is people and company intelligence. Teams describe the people and companies they care about, and Lumnis researches public evidence such as recent activity, hiring context, company events, and competitor-related attention. It returns a reviewable reason a person or account may matter, with linked sources where available and a sensible next move.

Lumnis does not claim to know an unpublished buying plan. The product is designed to help a person inspect the evidence and decide whether the inference fits the team's thesis.

Use People Intelligence when you need to find relevant people from a target description. Use Account Intelligence when the company is already known and you need a dated account view and relevant people.