Which buying signals show a B2B account is in market?

Published lists of buying signals keep getting longer. The length is the problem. Few of your target accounts are buying at any moment, so a long list mostly flags people who are only reading. This post gives a smaller model: 4 signal types, one rule for combining them and a way to test it against your own deals.

How many of your target accounts can be in market right now?

Fewer than your dashboard suggests. Across all potential buyers, the 95-5 rule puts it at 1 in 20; for a target-account list, 6sense’s estimate implies at most about 2 in 5. The LinkedIn B2B Institute’s 95-5 rule says 95% of potential buyers are out of market today.^↗ It rests on slow purchase cycles: 80% of companies change banking services only once every 5 years.^↗ 6sense’s Kerry Cunningham asserts that “roughly 60% of target accounts are not in-market” at any given time; he gives no study for that figure.^↗ 6sense sells intent and ABM software, so read its figures as one vendor’s research.

The 2 numbers measure different populations. The 95-5 rule covers every potential buyer in a category. 6sense talks about target accounts, which are usually a list already screened for fit.

Take a list of 500 target accounts, and say 60 of them show a topic surge this month (illustrative numbers). If the 95-5 rate held for your list, only 25 accounts would be buying, so at least 35 of the 60 surges would be false alarms, even if every real buyer surged. On 6sense’s estimate, up to 200 accounts are buying, so the surge missed at least 140 of them. Either way, a surge on its own is wrong about a lot of accounts: it flags researchers, or it misses buyers. 6sense gives the reason: “most visitors to vendor websites are curious professionals, not active buyers”.^↗

Timing raises the stakes. Buyers first contact a seller at 61% of the journey, down from 69% (6sense, 2025).^↗ And 95% buy from their Day One shortlist (6sense, 2025).^↗ Triggers are the only type that can fire before the shortlist forms, so act on them early and without pitching. By the time intent and multi-person engagement appear, the shortlist often exists already: treat them as evidence that you are on it, and help that buying group make its case. For the 95% who aren’t buying yet, the job is to be known before the next trigger. No signal model replaces that.

What are the main types of buying signals?

There are 4 types: fit, triggers, third-party intent and engagement, and each answers a different question. Most signal lists pour all 4 into one score, so a funding round and a whitepaper download count the same.

Fit is a gate. An account outside your ICP isn’t in market for you, however much it researches. Score fit as pass or fail before you count anything else.

Triggers explain why now. Gartner says 99% of B2B purchases are driven by organisational change.^↗ A new CFO, a merger, a funding round, a new market or a failed audit creates the need. 6sense puts it plainly: those moments “can be observed but rarely caused”.^↗ The reverse doesn’t hold. Most organisational changes don’t lead to a purchase in your category, so a trigger alone never makes an account a priority. Count only the triggers that create your specific problem: a new CFO for a finance tool, a new CISO or a failed audit for security. Company-level events are the safe place to start. If you track named people’s job moves, use a provider that documents its lawful basis and tell those people where you got their data, as Art. 14 GDPR requires.

Third-party intent shows research on your topics across the web. It is a relative measure. Bombora, for example, marks a topic as surging at a score of 60 or more, meaning consumption rose significantly above that account’s own baseline.^↗ A surge says an account is reading more than usual. It doesn’t say who is reading, or why.

Engagement is the buying group showing up in your own channels: known people on your site, event attendance, replies, trials. It is the strongest confirmation when it comes from several people. On average 13 people take part in a B2B buying decision, and 89% of purchases involve 2 or more departments (Forrester, 2024).^↗ As Kerry Cunningham of 6sense writes: “One person is noise. Two is a pattern.”^↗

Count a person as known only when they have identified themselves and agreed to tracking: they logged in, registered for an event or trial, replied or clicked through from an email they opted into. In the EU, person-level website tracking needs cookie consent (§ 25 TDDDG in Germany), and tools that put names to anonymous visitors are a GDPR risk. Use account-level data for everyone else.

Why is one intent surge or one form fill weak evidence?

Because you see only a small slice of what an in-market account does. 6sense reports that, across its 2024 and 2025 Buyer Experience studies, vendors saw only 10–15% of total research activity.^↗ Fewer than 30% of eventual buyers ever filled in a form on the winning vendor’s website.^↗ And 67% of B2B buyers say they prefer a rep-free experience (Gartner, 2026).^↗

Put the base rate and the visibility together. A single signal fires often for accounts that aren’t buying, and stays silent for many that are. That is why analysts warn against trusting any one source. Forrester says intent signals “on their own should not replace the qualification process”.^↗ Gartner says demand generation leaders “must triangulate on multiple intent data signals”.^↗

These 3 signals look like buying and usually aren’t:

• A single content download by one person, with nobody else from the account active
• A lone topic surge with no trigger and no first-party engagement behind it
• A job change older than 90 days with no follow-up engagement from the new hire’s team

Before you count engagement, strip out email opens, which privacy features inflate; clicks within seconds of delivery from several addresses at one domain, usually a security scanner; careers-page traffic; competitors, partners and current customers on support pages; and accounts with an open opportunity or active contract, which belong in expansion.

How do you combine signals into a score sales will trust?

Gate on fit, then pursue only when several known people engage and a second signal type backs them up. The rule is short enough to explain to a rep in a minute, and that matters more than precision.

1. Fit gate. Pass or fail against your ICP: industry, size, region and the systems you integrate with. A fail means no score, whatever the activity.
2. Trigger. A relevant organisational change in the last 90 days.
3. Intent. A surge on at least 2 of your core topics, or on 1 topic 2 weeks running, in the last 30 days.
4. Engagement. 2 or more known people from the account in the last 30 days. Rank the account higher when they come from different departments.
5. Tier. Pursue this week, with a brief to the account owner: engagement plus a trigger or an intent surge. Nurture and watch: any other signal. Monitor: no signals.

Check the volume. If more than about 1 account in 10 on your list reaches pursue in a month, your thresholds are too loose for the base rates above. Tighten them until each rep can work the pursue tier that week.

The windows are our own first settings, so tune them against your own data. Signals decay, and a stale one is worse than none because it sends a rep to an account that has already chosen. As a rule of thumb, set the engagement window to about a sixth of your median sales cycle, with a floor of 30 days: on a 9–12 month cycle, that means 45–60 days, and 90–180 days for triggers. Keep intent at 30 days: it measures weekly change against a baseline.

Pursue doesn’t mean “call and pitch”. The first move is to put proof in front of the right roles: account-targeted ads to their functions, a relevant case study to contacts who have opted in or a warm introduction through a customer or partner. In Germany, cold email needs prior consent even between businesses, so don’t turn a signal into an unsolicited sequence.

The multi-person rule also sets up the sale. In Gong’s analysis of 1.8M deals, deals over $50k with several engaged contacts had win rates 130% higher on average.^↗ That is a correlation and doesn’t prove cause, but it points the same way.

Even a strong score is not a forecast. Forrester finds that 86% of B2B purchases stall during the buying process.^↗ A signal earns a conversation. Qualification still decides the deal.

How do you prove your signals predict pipeline?

Back-test the score on accounts you already know before you trust it with rep time. You need 3 things: your target account list, closed-won and closed-lost opportunities from the last 4 quarters and the signal history for those accounts over the same period. If your signal history is thin, add a quarter of forward testing.

Score every target account as it looked on the first day of each quarter, including accounts that never opened an opportunity. Then count, for each tier, how many accounts opened an opportunity in the next 90 days and how many of those were won. Pursue-tier accounts should convert at a clearly higher rate than nurture and monitor. If they don’t, the score is adding noise. Tune on 3 quarters, check on the 4th and change one weight at a time.

Test intent vendors the same way. Forrester advises reviewing trial signals “in areas where your existing knowledge is highest”, so you can check them against what you already know.^↗ Give the vendor a list of accounts whose buying history you know, and see what it says about them.

You can start this week:

1. Write your fit gate as 4–5 pass or fail rules and apply it to your target list.
2. Pull your last 20 won deals and note the organisational change that preceded each one. That is your trigger list.
3. Add a rule that counts distinct consented contacts per account over your engagement window, excluding scanner clicks and opens.
4. Save today’s scores, so next quarter’s forward test has a starting point.

Then review the score every 90 days. Signal sources change, your ICP shifts and topic lists drift. A score nobody checks becomes the next MQL: a number everyone reports and nobody acts on.