Intent signals are the observable actions that suggest someone is moving toward a purchase: a search, a comparison page read, a repeat visit, a competitor lookup. Each signal is one data point showing a person or company is actively investigating a problem you solve, right now, before they've raised a hand or filled out a form. String enough of them together and you can watch demand forming in real time.
Most teams treat intent signals like a horoscope. Vague, account-level, "this company seems interested." That's not a signal you can act on. A real signal points at a person, names the topic, and is fresh enough to matter. The rest of this page breaks down the types, the examples, and the line between intent data and a single signal.
So let's get specific.
What is an example of a buying signal?
A buying signal is one concrete action. Not a feeling, not a score. Something a person actually did.
Here's a real shape of it. Someone reads your "X vs Y" comparison page on Monday. They come back Wednesday and read it again. Thursday they search for pricing on the same category. That's three signals stacking into a pattern.
Other examples of buying signals:
- Searching a problem your product solves ("how to reduce wasted ad spend on out-of-market accounts")
- Reading multiple vendor comparison pages in a short window
- Researching a competitor by name
- Downloading a buyer's guide or attending a webinar on a specific topic
- Returning to a product or pricing page more than once
Single signals are noise. A search could be idle curiosity. But a cluster of signals around one topic, from one person, inside one week? That's intent you can take to the bank.
What is the difference between intent signals and intent data?
People use these terms like they're the same thing. They're not.
An intent signal is a single event. One search. One content read. One comparison. It's the smallest unit, a single observation that someone is interested in something.
Intent data is the collection. It's the structured dataset that gathers thousands of those signals, ties them to people or accounts, and makes them searchable. The signal is the brick. The data is the building.
Here's why the distinction matters. You don't "buy" a signal. You buy access to a feed of intent data, then you act on the signals inside it. Your job is to pull the right ones out and route them to the right channel before they go cold.
So when someone says "we have intent signals," ask the follow-up: signals on whom, about what, how fresh? That's where most vendors get quiet.
What are the types of buying signals?
Buying signals split into three buckets. Each one tells you something different, and each one demands a different play.
Research signals. Active investigation: searches, content reads, competitor comparisons, topic deep-dives. These are the most valuable because they find net-new demand. The person hasn't talked to you yet. They might not even know you exist. Research signals are how you get in front of a buyer before your competitor does.
Fit signals. These tell you a person or company matches your ideal customer profile. Industry, role, company size. Fit alone isn't intent. A perfect-fit account that isn't researching anything is just a name on a list. But fit plus research is a priority account.
Engagement signals. Direct interaction with you. A demo request, a pricing-page visit, a reply to a sequence. These confirm interest that already exists. They're the warmest signals, but also the rarest, because the buyer already found you.
Most teams only chase engagement signals. They wait for the hand-raise. That's fishing in the small slice of the market that's ready to buy today, the same pond as every competitor. The LinkedIn B2B Institute puts it at about 5% in market at any given moment.
Research signals are where the other 95% lives.
Buying signals, buyer intent signals, and why the wording matters.
Buying signals, buyer intent signals, intent signals. These all point at the same thing. Someone doing something that suggests they're moving toward a purchase.
The vocabulary sprawls because different tools name it differently. Sales tools call them buying signals. Marketing platforms call them buyer intent signals. Data vendors call them intent data. Same idea underneath: an observable action that predicts a purchase.
What changes is the resolution. A "buying signal" inside your CRM is usually first-party, something that happened on your own site. A buyer intent signal from a data provider is usually third-party, something that happened out on the open web, across sites you don't own.
Open-web intent is the bigger pool by far. Your own site sees the people who already found you. The open web sees everyone researching the problem, including people who've never heard of you.
That's the gap most teams never close.
Account-level vs person-level intent signals.
This is the difference that decides whether a signal is usable.
An account-level intent signal says "Acme Corp is researching data vendors." Useful, sort of. But Acme has 500 employees. Who do you call? Who do you target? You're guessing across a building.
A person-level intent signal says "this specific person is researching data vendors." Now you have a human, not a logo. And if the signal comes with a hashed email, a name, a location, and a LinkedIn URL when available, you have everything you need to reach them.
That's how Buyerfeeds is built. Every signal in the feed is person-level. Each row carries a hashed email you can match as a custom audience in LinkedIn, Meta, Google, or TikTok, plus a stable identity token so you can join it to your own data.
Account-level intent is a weather report. Person-level intent is the address.
Free-text topics beat predefined categories.
Most intent data forces you into a fixed list of topics. You pick from a menu of "cloud security" and "marketing automation," and you get whatever the vendor decided to track.
The problem: your buyers don't research in the vendor's categories. They research your exact niche, your competitor's product name, the specific pain you solve. None of which fits a predefined bucket.
Buyerfeeds lets you search any free-text topic at the person level. Type the thing your buyers actually research, a feature, a competitor, a problem stated in their words, and get back the people showing intent on it. No category gymnastics.
So your signal matches your market instead of someone else's taxonomy.
How to turn intent signals into pipeline.
A signal is worthless until you route it somewhere. Here's the simple version.
First, freshness. A signal from 60 days ago is a need that's already met or lost. Buyerfeeds runs on 24-hour data freshness, so the people in your feed are in market now. Act inside the day.
Second, the channel. You've got a hashed email per row, so you have options:
- Push it to ad platforms as a custom audience and run intent-matched advertising
- Enrich your CRM (HubSpot, Salesforce) so reps see who's hot
- Sync to sales tools like Outreach, Salesloft, Apollo, Clay
- Pull it via API and route it however your stack works
Third, match the message to the signal. Someone researching a problem isn't ready for a pricing page. They need problem-aware content. Someone comparing vendors is product-aware and wants proof. The signal tells you the awareness stage. The content has to match it.
That's the whole game. Catch the signal fresh, reach the actual person, say the thing that fits where they are.
Most teams nail one of the three and wonder why intent data "doesn't work."
Go deeper.
→ What is intent data?. The full definition, how it's collected, and where signals fit.
→ Person-level intent data. Why naming the human beats naming the account, and what a feed row contains.
→ Intent data for advertising. How to push person-level signals to LinkedIn, Meta, Google, and TikTok as custom audiences.