The Targeting Signals Playbook, Extended

Buying a list is buying a guess about who might be a fit. A signal is knowing what just changed about a company or a person — a new round of funding, a decision-maker who just took the seat, a team that suddenly started scaling, an acquisition that just closed. That difference is the whole game in paid acquisition: the same creative and the same offer convert several times better when they land on an audience that is in-market this week rather than a static segment that was true last year. This document is built like an analyst's brief: it leads with the five signals that move the needle most, turns them into six campaign recipes you can run as-is, and then proves the breadth — the full surface area the same engine can target on, on request. Every dataset statistic here is real, queried from the production graph, with the date it was measured.

Why a signal beats a list

The reason timing wins is structural, and it is now well documented. By the time a buyer talks to a vendor, most of the decision is already made — they research, shortlist, and form a preference while they are still anonymous. The job of a campaign is to be present and credible during that window, not to arrive after it has closed.

The practical consequence: broad targeting wastes most of its budget on accounts that are simply not in-market, and the accounts that are never show up on a static list. Targeting by change fixes both ends. It is also where the measured return sits — account-based, signal-led programmes beat untargeted spend on ROI across every region Forrester studied.

None of this is exotic. It is the established discipline the industry calls buying signals, intent data, or signal-based selling. The question for any data provider is simply: which signals can you actually produce, how fresh are they, and do they resolve to a real person you can reach? That is what the rest of this brief answers.

The market's signal map — and where we play

The market has converged on a recognisable set of signal families. Naming all seven up front does two things: it lets us speak the same language a media buyer or RevOps lead already uses, and it lets us be precise about which parts of the map our data is built for — and which parts it is not.

The single most useful thing to know about this market: most of what is sold as "intent" today is third-party topic surge — a co-op that watches which companies are reading about your category — plus reverse-IP de-anonymisation of website visitors. Those are real, and they are not what we do. The signals that buyers trust most and that convert best outside of that category are job change and funding / hiring — and those are built from primary career history, which is exactly what we hold. On technographics, this extended edition can say more than the first one did: we now expose both layers — detected tech-stack tags on 3.6M companies (the website layer the incumbents sell) and the builder layer (what engineers actually use and contribute to), which nobody else has.

The universe those signals filter is large in every vertical a campaign tends to target — the count below is companies, before any signal narrows them to the in-market few. (Re-verified 2026-06-12; figures stable since the first edition.)

How to read this playbook

We could list forty signals alphabetically. That is a dump, not an analysis — and it would bury the few that actually decide a campaign's economics. So this brief is ranked, in three passes. Part 1 works the five signals that carry the most leverage in depth: each one independently predicts budget, intent, timing, or fit strongly enough to build a campaign around. Part 2 is new in this edition: six ready-made recipes that stack those signals into campaigns for the buyer types we meet most often — take them as-is or as templates. Part 3 is the full catalogue across the market's seven families, with proof and a use for each axis — the answer to "can you also target on…?" is almost always in there.

Scored on what each of the five core signals predicts:

● strong predictor · ◐ partial · ○ not the point of this signal. Contactability scores only on reach by design — it is the layer that turns any of the other four into a person you can actually message.

One more thing worth knowing before the deep dives: signals do not all fire at the same rate. A funding round is rare and high-value; a hiring surge is common and high-frequency. A campaign usually wants both — a few high-intent triggers and a steady, large flow to keep the pipeline full.

// recently-funded buyers, last 90 days, growth stages
GET outricher.com/api/v2/signals/funding
     ?stage=seed,series_a,series_b
     &closed_after=2026-03-14
     &role=founder,vp,head        // resolve to decision-makers

// one call resolves a target to a full, reachable dossier
GET outricher.com/api/v2/profile?profile=<target>
// → name · role · personal_emails · phone · socials · work history

Six campaigns you can run as-is

Signals become valuable when they are stacked into a campaign. These six recipes cover the buyer types we meet most often; each is a concrete audience definition built only from the signals in this playbook. Take them literally, or treat them as templates — every line in a stack is swappable.

The full catalogue — seven families

The five core signals carry most of the leverage; the recipes show how to stack them. What follows is the breadth — every targeting surface the engine exposes, grouped by the market's seven signal families. Entries marked NEW were not in the first edition of this playbook; each carries its proof and a primary use. The point of this section is range: whatever a specific campaign needs to target on, the surface area is here.

Economics & the takeaway

The pricing mirrors how a campaign actually runs: discovery and sizing are free, segmentation is free, and you pay only to put contact details on the people you decide to reach.

A weekly, signal-driven audience of thousands of in-market companies costs effectively nothing to build. You spend only to reach the few hundred a campaign actually activates — which is the unit economics of an acquisition engine that never runs out of well-timed, reachable targets.

The takeaway: the value is not the 1.17B profiles — anyone can buy rows. It is the ability to query the professional economy as a live graph, so a static list becomes a timed, ranked, reachable audience: who just got the budget, who just took the seat, who is scaling right now, which engineers run the stack, whose site does the traffic, who just got acquired — each resolved to a person you can message. Lead with the five core signals; run the recipes as written; and when a campaign needs an axis nobody else offers, the catalogue — forty-plus surfaces and counting — is the answer to "can you target on that?"