LinkedIn buying signals: a taxonomy ranked by strength and decay

Every guide to LinkedIn buying signals gives you the same list: post engagement, job changes, funding, hiring, profile views. None of them tells you which of those actually moves the odds, whether the signal is about a person or about their employer, or how long you have before it is worthless. The generic B2B guides do publish decay tables, but half their rows are events you cannot observe on LinkedIn at all, like pricing page visits and abandoned demo forms. This piece is the missing version: only LinkedIn-observable events, ranked, split by subject, and each decay window labelled with where it came from, including the ones that are nobody's measurement.

What a LinkedIn buying signal actually is

A LinkedIn buying signal is a publicly observable event on the platform that raises the odds a person or their company will evaluate something in your category soon. It is a probability shift, not proof. The useful ones are dated, attributable to a named person or account, and decay fast enough that timing changes the outcome.

That last clause is the filter most lists skip. A signal that stays true for a year is not a signal, it is a firmographic attribute. "Works at a Series B fintech" is targeting. "Commented yesterday on a post about outbound tooling breaking at scale" is a signal, because it has a clock on it.

LinkedIn itself runs on this logic. Its Buyer Intent product aggregates what it calls "180+ distinct insight signals" into a single account score, and the lead search filter surfaces accounts "where someone has expressed high or moderate buyer intent in the past 30 days", with a separate 7-day filter for the most recent activity (LinkedIn Sales Navigator Help, Buyer Intent FAQ, checked August 2026). Thirty days is LinkedIn's own outer edge for intent being worth scoring. That is a first-party anchor, and it is more than any of the top-ranking guides on this query offer.

The taxonomy: fourteen signals, ranked, with decay windows

Ranked by how much the signal moves the odds, strongest first. The "about" column is the one most lists omit and it is load-bearing: person signals tell you someone is paying attention, company signals tell you money is moving. They are not substitutes, and they decay at completely different speeds.

The LinkedIn alert definitions in the right-hand column are quoted from LinkedIn Sales Navigator Help, Sales Navigator Alerts overview, checked August 2026. They matter because they are the only windows in the table that come from the platform rather than from someone selling signal software.

Person signals and company signals answer different questions

A person signal tells you a named human is thinking about your problem right now. A company signal tells you the organisation has money moving toward the function you sell into. Person signals are fast, specific and shallow. Company signals are slow, vague and deep. Confusing them is why most signal lists produce activity instead of pipeline.

The practical difference shows up in what you write. A person signal gives you the sentence. Someone commented that their team's outbound tooling falls over past a certain volume, so you open by referencing that constraint, not the company. A company signal gives you the reason to be in the account at all, but not the sentence, because "I saw you raised a Series B" is a fact three hundred other people also saw.

It also shows up in who you write to. Company signals arrive without a name attached. A funding round or a hiring spike tells you the account is live; it does not tell you which of the eleven people in that function owns the decision. That mapping step is the work, and it is where hiring signals turn into buying intent rather than into a list of company names.

Most published decay windows are guesses, and they contradict each other

Almost no published LinkedIn signal decay window is a measurement. They are practitioner estimates, repeated between vendor blogs until they read like data. The easiest way to see this is to put two 2026 guides side by side on the same signal and watch the numbers disagree by more than an order of magnitude.

Neither guide cites a study for those windows, and both were published in 2026. On funding, one says you have two days and the other says you have three months. Both cannot be describing the same phenomenon.

The same problem infects the headline statistics in this space. The figure you will meet most often is that new executives spend 70 percent of their budget in their first 100 days. Follow it back: UserGems states it on its new-hire trigger post and links the claim to another UserGems blog post rather than to any external study (usergems.com, checked August 2026). That is a citation loop, not a source. The related claim that a new C-level hire spends a million dollars on new solutions within nineteen days does not resolve to any published research at all.

So treat every window in the big table above as a planning default, not a fact, and treat the ones marked "not measured" as exactly that. What you can verify is the direction: person-level attention decays in hours to days, company-level money decays in weeks to months, and LinkedIn's own product only bothers scoring intent inside 30 days.

Stacking: one signal is a reason to look, two are a reason to write

A single weak signal should never trigger a message. Stack instead: require either one strong signal from the top of the table, or two independent weaker ones pointing at the same account inside the same window. A like plus a follow is not a stack, because both are the same behaviour.

A comment plus an open req for a role in your category is a real stack, because the two are independent of each other. One says a person is paying attention, the other says the budget already moved.

The stacking rule is what stops signal-based selling from degrading into volume selling with extra steps. Once you decide that any engagement justifies a message, you are back to sending everything to everyone, and the benchmarks say what that produces: across 48 BeReach workspaces and 42,236 connections in 2026, the median response rate was 4.5 percent against a 15.7 percent mean, and 68.3 percent of the responses that did arrive were explicit disinterest. The spread between the tenth percentile at 0.4 percent and the ninetieth at 49.5 percent is almost entirely a targeting spread, not a copywriting one.

The upside of getting the stack right is measurable. LeadRiver's April 2026 analysis of more than 50,000 connection requests puts trigger-referencing notes at 50 to 60 percent acceptance against 15 to 25 percent for generic ones, with 30 to 37 percent as the typical band overall. Notably, LeadRiver publishes no uplift figure for simply using the prospect's first name, and reports that it produces no meaningful improvement. Referencing the actual event is the variable that moves; personalisation tokens are not.

The same pattern holds one step later, at reply. Belkins' 2026 study of 15.1 million LinkedIn touchpoints found a 7.9 percent total reply rate for cold connect-then-message campaigns against 12.2 percent for warm messenger campaigns, and Expandi's own 2026 measurement puts the LinkedIn platform average at 10.3 percent, roughly double the 5.1 percent cold email figure that Expandi quotes from Belkins' research. Belkins has since restated its cold email benchmark at 0.45% on 7.5 million 2025 sends, measured against total sends rather than opens, so treat 5.1% as the 2026-vintage figure Expandi quoted.

Reading these signals without Sales Navigator

Most of the signals in the table are public. Post comments, reactions, job posts, headline changes and company page updates are all visible without a paid tier and without a connected account. What Sales Navigator adds is aggregation and alerting, plus the private signals nobody else can see, chiefly profile views and its Buyer Intent score.

That distinction is worth pricing out, because Buyer Intent is not on the entry tier. On LinkedIn's compare-plans page, checked August 2026, Sales Navigator Core is $119.99 per month billed monthly or $1,079.88 per year, about $89.99 per month, and Advanced is $159.99 per month billed monthly or $1,799.88 per year, about $149.99 per month. Both are priced per licence, meaning per seat. Buyer Intent is listed under Advanced and Advanced Plus only, so the intent score costs the Advanced tier per person on the team, every month.

If what you need is the public half of the table, you can assemble it directly. Post engagement gives you the strongest person signals, and pulling the named commenters off a post that framed your problem well is the highest-yield hour in prospecting; the comment reader and the reactions reader both work without connecting anything. Open reqs give you the company signals, through job search by role and company. For the fuller list of what is readable outside the paid tier, see LinkedIn intent signals without Sales Navigator.

This is where BeReach picks up. It builds the prospect list from that same public data, ranks who is worth your time and why against the signals that fired, and drafts a first message per person that references the specific event rather than a generic opener. A human reads and approves every draft before anything sends, so you can see whether the stack is real and whether the sentence holds up before committing time to the account.

What happens after the signal fires

Detection is the easy half. The signal only pays if the message goes out inside the window, references the specific event, and is worth reading on its own terms. Two things break this in practice: teams detect faster than they can write, and teams send so much that the platform limits catch up with them before the signals do.

On the writing side, the fix is not more templates. A signal-triggered message that could have been sent to anyone else with the same job title has thrown away the entire advantage of the signal. If you cannot write a first line that only makes sense for this person this week, the signal was too weak to act on. The engagement signals guide goes deeper into reading a comment closely enough to get that line, and the job change playbook covers the one signal where timing matters more than wording.

On the sending side, know the ceilings before you build a workflow around a fast-decaying signal. LinkedIn publishes no official invitation cap; the roughly 100 invitations per week figure everyone quotes is industry consensus, not a documented limit, and citing LinkedIn's help centre for it is a mistake. BeReach paces against its own ceilings: 50 invitations per day and 120 profile visits per hour are unconditional hard caps, while profile visits and messages start at 300 and 70 per day and scale with a workspace multiplier. The practical consequence is that your daily signal intake should be sized to your send capacity, not the other way round. Detecting 400 signals a week and being able to act on 60 of them means the other 340 decay in a queue.

Every message BeReach drafts is approved by a human before it sends, and there is one included AI model doing the drafting rather than a picker to configure. That is the point of the approval step: the signal gave you a reason to write, and a person still decides whether the reason survived contact with the profile.

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