Buying signals
Updated November 10, 2026
Buying signals are behaviors, statements or events that suggest a prospect or account may be moving toward a purchase. They range from a pricing question on a call to a visit to a pricing page to a company event such as a funding round or a new executive, and sellers use them to decide whom to contact and when.
In short: Buying signals are behaviors, statements or company events, such as pricing questions, pricing page visits or a new funding round, that suggest an account may be moving toward a purchase and help sellers time outreach.
How buying signals work
A buying signal is evidence that something has changed for a prospect in a way that makes a purchase more likely. HubSpot defines buying signals as behaviors or statements that indicate a prospect is considering a purchase 1. Sellers use them to decide when to reach out and what to say.
HubSpot sorts buying signals into four types 1.
- Verbal signals are what a prospect says or writes, such as questions about pricing, contract length or rollout.
- Non-verbal signals are tone, pace and engagement on a call or in a meeting.
- Digital signals are activity on the seller's website, content and emails, such as pricing page visits, case study reads or a free trial sign-up.
- Situational signals are events at the prospect's company or in its market, such as a funding round, a merger, an expansion or a new executive hire. Sales teams often call these trigger events.
The first three come from the buyer's contact with the seller. The fourth comes from the outside world, which makes it the main source for outbound teams that have not spoken to an account yet.
Digital signals collected at scale are usually sold as intent data. Salesforce splits intent data into first-party data from a seller's own website and properties and third-party data from external publishers, review sites and industry websites 2. Salesforce also warns that not all intent signals are predictive and suggests a pilot to test which ones work 2.
Public company events are a third route. A job post, a running ad or a company post has a date, and anyone can check it. RichAPI's signal playbook treats these as observed public activity, not intent data, and takes a stricter line than most sales guides 3. It calls a company hiring implementation engineers evidence about that company, not a buying signal, and leaves the inference to the seller 3.
Teams then act on the signal. Signals feed lead scoring, decide which rep gets an account and shape the first line of a message. Fit data such as firmographics decides whether an account deserves a signal check at all.
What makes a buying signal worth acting on
Five variables separate a useful signal from noise.
**Strength.** A prospect asking about contract terms says more than a prospect opening an email. HubSpot treats a click on an emailed link as a much stronger signal than an open 1. Rank signals by how close they sit to a decision.
**Fit.** A strong signal from a company outside the target profile is still a poor lead. Check fit first with firmographics and technographics, then look for signals inside the accounts that pass.
**Freshness.** Signals age like any other record, the same pattern described under data decay. RichAPI's signal playbook suggests expiring a signal after two weeks for posts, a month for jobs and a quarter for ads 3. It also warns that a re-contact off a stale trigger reads worse than no contact, because the prospect knows how old the event is 3.
**Stacking.** One signal is a hint. Two or three pointing the same way, such as a new executive plus a related job post, make a stronger case. HubSpot says softer digital signals paint a fuller picture when combined with other signals 1.
**Checkability.** A signal the rep can cite, like a dated job post, supports an honest first line. An unexplained score does not. The buyer can verify a public fact, and the rep never has to explain a number the buyer has never seen.
Example of reading buying signals on one account
A seller of customer onboarding software reviews one target account before writing. The account already fits the target profile on industry, size and region. The account record holds the following.
Account example logistics software company
Fit check passes on industry, headcount band and region
Signal A situational job post for two customer onboarding specialists, posted this week
Signal B situational new VP of Customer Success announced last month
Signal C digital none, no site visits or email replies on record
Read customer team growing, new owner for the problem, no contact yet
Action write to the new VP, cite the job post, ask one questionBoth signals are situational in HubSpot's terms, the kind of company change HubSpot lists as a possible buying signal 1. Neither proves a purchase is coming. Together they make a reasonable case that the customer team is growing and that a new leader may be reviewing tools.
The rep opens with the job post because the buyer can check it. RichAPI's playbook makes the same point, arguing that a specific hiring fact makes a defensible first line and a vague phrase such as recently active does not 3.
Finding accounts like this at scale is where public activity data helps. RichAPI's signal recipe searches job posts with linkedin_job_search at 0.1 credits per result, dedupes the results to companies and then pulls people at those companies 3. In the playbook's worked run, 200 job posts bill 20 credits and dedupe to about 60 companies 3. Pulling 3 people at each company with linkedin_company_employees_search returns 180 results at 0.1 credits each, or 18 credits 3. The email step uses email_finder at 5 credits per call, a waterfall endpoint where a call that finds nothing costs zero credits 3.
The playbook also warns about a slow failure. A job post that went up in March can still be up in July, so a team that dedupes on the company keeps getting the same accounts every week 3. The fix is to dedupe on the signal and keep its timestamp.
Buying signals vs intent data
The terms overlap and are often used as synonyms. Buying signals cover any clue that a purchase may be near, while intent data is one packaged source of those clues.
| Buying signals | Intent data | |
|---|---|---|
| What it answers | Is this prospect or account showing signs of a coming purchase? | Which companies seem to be researching this category? |
| Input | Conversations, site activity and public company events | Web visits, content reads and searches matched to companies |
| Output | A dated observation a rep can act on | An intent score or topic surge per company |
| Cost driver | Rep time and the data used to spot events | Vendor subscriptions, often by topic or volume |
| Main risk | Reading a purchase into an unrelated event | Wrong company matches and unexplained scores |
| Use it when | Deciding whom to contact and what to say first | Ranking a large fit list by apparent research |
Frequently asked questions
What are examples of buying signals?
Common examples include questions about pricing or contract terms, visits to a pricing page, a free trial sign-up, a new executive, a funding round and job posts for roles that would use the product 1. The strongest ones sit close to a decision and come from accounts that already fit.
What is the difference between buying signals and trigger events?
Trigger events are one type of buying signal. They are changes at the prospect's company or in its market, such as a merger, an expansion or a new leader 1. Other buying signals come from the prospect's own words or from activity on the seller's website.
Is a job post a buying signal?
Many sales guides treat it as one. It is safer to treat a job post as a public, dated fact that suggests a team is growing or a gap is open. Whether the company will buy anything is the seller's inference, and messages should cite the post rather than the inference.
How long does a buying signal stay useful?
It depends on the signal. RichAPI's signal playbook suggests expiring post signals after two weeks, job signals after a month and ad signals after a quarter 3. Pick a window per signal type, store the date with every signal and stop acting on it once the window closes.
The bottom line
Buying signals are behaviors, statements or company events that suggest a purchase may be coming. They help sellers decide whom to contact and when, but none of them proves a purchase. Check fit before chasing signals, rank them by strength, store every signal with a date and an expiry, and open messages with the fact the buyer can verify rather than the inference behind it.