---
title: "Buying Signals: 15 Types, How to Spot Them, What to Send"
description: "What buying signals are, the 15 types worth tracking in B2B, how to detect each one, and how to turn a signal into outreach that does not feel creepy."
url: https://getaob.com/buying-signals
---

# Buying signals: what they are, how to detect them, and how to act

A **buying signal** is an observable event that makes an account more likely to buy now than it was last month: a new leader in your buyer seat, a job posting that describes the problem you solve, a funding round, a visit to your pricing page. Signals answer the question a fit score cannot: why reach out this week.

This guide covers the types worth tracking, how to detect them, and how to turn one into a message. For the vendors that sell signal data, see the [best buying signal tools](https://getaob.com/buying-signal-tools).

By [Garrett Wolfe](https://garrettawolfe.com), founder of OneGTM. Updated November 1, 2026.

Key takeaways

- Fit is durable and a signal fades. Keep them in separate fields, and reach out only when an account has both.
- The strongest signals are specific to your market. A generic intent score rarely beats one niche event that predicts your deals.
- A company-level signal is only useful once you can name and email the person it applies to.
- Reference the context the signal gives you, not the surveillance. "Saw you are hiring a RevOps lead" is fine. "Saw you were on our pricing page" is not.
- Measure positive replies and meetings by signal, and drop the ones that do not move.

## Fit vs. signal: two different questions

A fit score says whether an account could ever buy: right industry, right size, right tech, right region. It changes slowly. A signal says whether now is a good time. It decays in days or weeks. Teams that add them into one number end up emailing great-fit accounts with no reason to talk, or chasing hot signals at companies that will never buy.

Keep fit and signal in separate fields. Work accounts that are high on both first, nurture high fit with no signal, and mostly ignore signals at poor-fit accounts. This is the core of [GTM engineering](https://getaob.com/gtm-engineering), and it is how every signal build I run is wired.

## 15 types of buying signals

| Signal | What it suggests | Where to get it | Stays fresh for |
| --- | --- | --- | --- |
| New executive in your buyer seat | A new leader reviews the stack and the budget in their first 90 days | [Job change tracking](https://getaob.com/job-change-tracking-tools) | About 3 months |
| Champion changes jobs | Someone who bought from you before is now somewhere new | [Job change tracking](https://getaob.com/job-change-tracking-tools) | About 3 months |
| Job posting that names your problem | They are about to spend money on the problem, in salary if not with you | [Job posting data](https://getaob.com/job-posting-data) | Until the role is filled |
| Hiring surge in one team | A team is growing faster than its tools and process | [Job posting data](https://getaob.com/job-posting-data) | 1 to 2 months |
| Funding round | New budget and a board expecting growth | [Funding data](https://getaob.com/funding-data) | 1 to 3 months |
| Website visits, especially pricing | Someone is researching you right now | [Visitor identification](https://getaob.com/website-visitor-identification) | Days |
| Product usage | A free or trial account is hitting limits or adding seats | [Product usage signals](https://getaob.com/product-usage-signals) | Days |
| Tech install or removal | A tool you replace or complement just changed | [Tech stack tracking](https://getaob.com/tech-stack-tracking-tools) | 1 to 3 months |
| Competitor engagement | People following or liking posts from a competitor | [Engagement scrapers](https://getaob.com/linkedin-engagement-scrapers) | Weeks |
| Engagement with your own content | They liked, commented on, or shared your posts | [Engagement scrapers](https://getaob.com/linkedin-engagement-scrapers) | Weeks |
| Company news | Expansion, new locations, acquisitions, leadership changes | [News monitoring](https://getaob.com/company-news-monitoring) | 1 to 2 months |
| Public records | Permits, licenses, contracts, court filings, new provider registrations | [Public records data](https://getaob.com/public-records-data) | Varies |
| Third-party intent | Accounts researching your category on review sites and publisher networks | [Intent data providers](https://getaob.com/intent-data-providers) | Weeks |
| Social and community posts | Someone asks for a recommendation in your category on Reddit, X, or Slack groups | [Social listening](https://getaob.com/social-listening-tools) | Days |
| Traffic or ad spend change | Fast growth, a new campaign, or a new market | [Website traffic estimates](https://getaob.com/web-traffic-data), [ad libraries](https://getaob.com/ad-intelligence-tools) | Weeks |

Decay windows are rules of thumb. Measure your own: the window closes when replies to that signal drop back to your baseline.

Want this built for your team?

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## First-party, second-party, and third-party signals

### First-party

Events in your own systems: website visits, product usage, form fills, email replies, event check-ins. Highest accuracy, lowest volume, and you already own the data.

### Second-party

Public behavior on someone else's platform that you can see directly: LinkedIn engagement, community posts, job postings, review site activity. Good accuracy, and usually where the niche signals live.

### Third-party

Aggregated intent from data vendors, built from content consumption across publisher networks. Broad coverage, fuzzy at the account level, and best used as a tiebreaker rather than a trigger.

## The best signals are niche

Every outbound tool watches funding rounds and job changes, so every competitor emails the same accounts the same week. The signals that work best are usually ones nobody packages: a new license in a regulated industry, a project award, a permit, a court filing, a new location.

For Statewise, an EHR for Medicaid care agencies, the buyers are barely on LinkedIn, so I built the account list from public provider registries and the signals from company-level events like new provider numbers, grants, licenses, and EHR changes. For Antimetal, an infrastructure SaaS company, the signal engine watched hires, promotions, job posts, people engaging with competitors, website visitors, and outages. Different markets, completely different signals. More sources are in [public records data](https://getaob.com/public-records-data) and [developer signal tools](https://getaob.com/developer-signal-tools).

## How to detect buying signals: a working pipeline

1. **Pick three to five signals:** Start from your last 20 closed-won deals. What happened at those companies in the 90 days before the first call?
2. **Source each one:** An API, a scraper, a vendor feed, or your own product data. Note the cost per event and how fresh it is.
3. **Match to your account list:** Join every event to an account by domain or LinkedIn page. Events at accounts outside your scored list get dropped or reviewed, not emailed.
4. **Dedupe and expire:** One account can fire the same signal five times. Keep one active signal per type, with a date, and expire it when the window closes.
5. **Find the person:** Most signals are company-level and name nobody. Map each signal to the persona it matters to, then find and verify that person. If you cannot email anyone at the account, the signal is not actionable yet.
6. **Route:** Into a sequence written for that signal, or to a rep in Slack with the context attached, depending on account value.
7. **Log it:** Write the signal to the CRM record and onto every message it triggers, so replies and meetings can be counted by signal later.

## How to act on a buying signal

The signal earns you a reason to write. The message still has to be about them. Name the public context plainly, connect it to a problem they are likely to have because of it, and ask a small question.

| Signal | Angle | Example first line |
| --- | --- | --- |
| New VP of Sales | New leaders reset the stack in their first quarter | congrats on the new role. most new sales leaders i talk to spend month one figuring out what the crm actually says. |
| Job posting for an SDR team | Hiring reps before the system exists | saw the three sdr reqs. curious who is building the lists they will work from on day one. |
| Funding round | A growth target and a short runway to hit it | congrats on the series a. the plan usually doubles pipeline faster than headcount can. |
| Engaged with a competitor post | Evaluating the category | saw you are following the conversation on outbound tooling. what is the team using today? |
| New license or permit | Opening something that needs your product | saw the new location permit in austin. who handles setting up the systems for a new site? |

Website visits are the exception. Use them to decide who to contact and when, but never say you saw the visit. Write as if the timing were a coincidence.

Speed matters most for fast-decaying signals. A pricing page visit is worth a same-day touch. A funding round can wait a week while you find the right person.

## Measuring which signals work

Tag every enrolled lead with the signal that put it there. After a few hundred sends per signal, compare positive reply rate and meetings booked against a no-signal control group from the same fit tier. Signals that do not beat the control go. Signals that do get more sources and more volume.

Expect most of your pipeline to come from two or three signals. That is normal, and it tells you where to spend.

## Tools for tracking buying signals

Signal platforms bundle sources and route alerts. Data feeds give you the raw events to build your own. Both are compared, with pricing, in [best buying signal tools](https://getaob.com/buying-signal-tools) and [intent data providers](https://getaob.com/intent-data-providers). If you would rather have the signals picked, sourced, and wired into outbound for you, that is what the [signal-based outbound](https://getaob.com/outbound-agency) build does.

## Buying signals FAQ

### What are buying signals?

Buying signals are observable events that make an account more likely to buy now, such as a new executive in your buyer seat, a job posting that describes the problem you solve, a funding round, a tech stack change, or repeat visits to your pricing page. They tell you when to reach out, while a fit score tells you who to reach out to.

### What are examples of buying signals in B2B sales?

Common examples are executive hires and job changes, job postings, hiring surges, funding rounds, website and pricing page visits, product usage spikes, tech installs or removals, engagement with competitor content, company news, public records like permits and licenses, and third-party intent from review sites.

### What is the difference between buying signals and intent data?

Intent data usually means third-party data that estimates which accounts are researching a topic across publisher networks. Buying signals is the broader term and includes first-party events like website visits and product usage, plus public events like hiring, funding, and job changes.

### How do you track buying signals?

Pick a few signals that showed up before your past deals, source each one from an API, scraper, vendor, or your own product data, match events to accounts in your scored list, expire them when they go stale, find the right person at the account, and route them to a sequence or a rep with the context attached.

### Which buying signals are strongest?

It depends on the market. First-party signals like pricing page visits and product usage are usually the most accurate. Beyond those, niche signals specific to your market, like a new license or project award, tend to beat generic signals that every competitor also watches.

### How fast should you act on a buying signal?

Match the speed to the decay. Website visits and product usage deserve a same-day response. Job changes and funding rounds stay useful for weeks, so it is worth taking a few days to find and verify the right person.

### Can AI detect buying signals?

AI is good at reading unstructured sources, such as job descriptions, news, filings, and websites, and deciding whether an event matches a signal you defined. It still needs a clear definition of the signal and a scored account list to match against.

Want the right signals picked, sourced, and wired into outbound?

## Related

- [Best buying signal tools](https://getaob.com/buying-signal-tools): Signal platforms and data feeds for job changes, hiring, funding, and intent, with what OneGTM uses.
- [Best signal-based outbound agencies](https://getaob.com/signal-based-outbound-agencies): Agencies that run outbound from real buying signals, compared, with published prices.
- [Best intent data providers](https://getaob.com/intent-data-providers): Bombora, G2, TrustRadius, 6sense and more for third-party intent, with how to test them.
- [Best job change tracking tools](https://getaob.com/job-change-tracking-tools): Champion tracking platforms and data APIs, with pricing and how to test one before you buy.
- [Job posting data for sales](https://getaob.com/job-posting-data): Job postings APIs, scrapers, and datasets, with pricing and how to turn a post into a meeting.
- [Best website visitor identification tools](https://getaob.com/website-visitor-identification): Person-level and company-level visitor ID tools, with pricing, accuracy, and privacy notes.
- [Signal-based outbound agency](https://getaob.com/outbound-agency): Cold email and LinkedIn systems triggered by real buying signals.
- [GTM engineering guide](https://getaob.com/gtm-engineering): What GTM engineering is, how it differs from RevOps, the stack, five core workflows, and how to start.
- [Green Dock Partners case study](https://getaob.com/green-dock-case-study): A tracker that watches public closure news and sends an industrial real estate investor a daily digest. No deal outcome claimed.

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Book a call at [getaob.com/#contact](https://getaob.com/#contact) or email [team@getaob.com](mailto:team@getaob.com).