Signal-Based Targeting to Identify High-Intent Leads

Guide to Signal-Based Targeting for your ICP

An automated GTM workflow that turns website intent into qualified outreach
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A technical breakdown of a real, production-ready system
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Intent already exists.
Most GTM stacks just don’t know how to capture it.


Every day, buyers:

  • visit your pricing page
  • read a case study
  • return twice in the same week
  • hover around your solution without filling a form
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That’s not “top of funnel noise”.
That’s unstructured intent.
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The problem isn’t traffic.
It’s that most teams have no event-driven system to turn intent into action.

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The goal of the system
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Not to “identify people”.
Not to spy.
Not to guess.

The goal is simple:
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When intent happens, decide if it’s worth action — automatically.
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This requires four things:

  1. First-party signals
  2. Company-level identity
  3. Explicit ICP logic
  4. Deterministic routing
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Everything else is optional.

Step 1 — Capture first-party intent events
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We start where the data is clean: your website.
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High-intent events might include:

  • pricing page view
  • demo page view
  • case study view
  • repeated visits in a short window
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These events are:

  • timestamped
  • deterministic
  • owned by you
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No scraping. No guesswork.

Step 2 — Resolve company identity
(not people)
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From the event stream, we resolve company-level identity.

This is a critical design decision.
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At this stage, the system answers:

  • What company is behind this visit?
  • What domain?
  • What size, industry, geography?
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This avoids:

  • unreliable individual identification
  • compliance issues
  • brittle hacks
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We now have a real account, not a guess.

Step 3 — Enrich only what deserves enrichment
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Most stacks enrich everything upfront.
That’s backwards.
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At this point, enrichment is:

  • firmographic (size, industry, region)
  • optional technographic
  • scoped to one company at a time
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No lists.
No bulk operations.
No wasted credits.

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This keeps the system cheap, fast, and focused.

Step 4 — ICP qualification as a decision engine
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This is where most GTM stacks fail.

Instead of filtering rows, we apply explicit decision logic.
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Claude evaluates the company against:

  • ICP rules
  • role relevance
  • market focus
  • customer / non-customer status
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The output is not “yes/no”.

It’s:

  • score (0–100)
  • tier (A / B / C)
  • reason codes (why this scored that way)
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This turns GTM from opinion into deterministic judgment.

Step 5 — Route based on score, not clicks
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Now the system can act.
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Example routing:
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  • Tier A → find 2–3 contacts + prepare outreach
  • Tier B → wait for another intent signal
  • Tier C → ignore or cheap retargeting
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Nothing is sent “just because someone visited”!
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Action only happens when intent + fit intersect.

Step 6 — Activate outreach with full context
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For Tier A accounts only:

  • relevant roles are identified
  • emails are verified
  • context is attached (page viewed, score, reason)
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The outreach system receives:

  • fewer leads
  • higher confidence
  • better timing
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Sales doesn’t get “more”.
They get clearer.

This isn’t about tools
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The stack matters less than the order.
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What makes this work:

  • events before enrichment
  • identity before contacts
  • decisions before outreach
  • systems before campaigns
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Most GTM teams invert this and pay for it with noise.

The real takeaway
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This workflow doesn’t “create demand”.

It listens.
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Your buyers are already telling you when to talk to them.
You just need a system that knows how to listen.

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