Lukas Correa

Signal-driven GTM for ambiguous markets.

I build GTM decision systems that extract buyer intent before you scale narrative, spend, or headcount.

The Problem

  • Demand exists, but buyer language is unstable
  • ICPs are unclear or shifting
  • Category awareness precedes problem awareness
  • Execution scales before intent is understood

Core Principle

  • Intent before messaging
  • Market truth before narrative
  • Structure before speed
  • No irreversible decisions without evidence or data

System Architecture (high level)

  1. Market Reality – capture raw market signals (no interpretation)
  2. Intent Signals – map intent clusters (job, failure, cost of inaction)
  3. Messaging Hypotheses – versioned, testable messages tied to clusters
  4. Outbound Execution – controlled interrogation; measures signal
  5. Human Conversation – highest-fidelity signal; structured analysis
  6. Feedback & Refinement – close loop; update ICP and messaging based on evidence

What the System Produces

  • Living ICP definition (not static personas)
  • Repeatable messaging engine
  • Faster learning with less wasted spend
  • Alignment across marketing, sales, product
  • Defensible GTM decisions for leadership

Relevant Work

  • Designed an end-to-end GTM operating system for an AI SaaS platform serving VC/PE; demand discovery + ICP validation + learning velocity.
  • Built a search-led GTM strategy using buyer intent as market intelligence to validate demand before scaling.
  • Built a centralized GTM decision system (Notion) covering ICPs, messaging, campaigns, metrics, learnings to prevent drift.
  • Structured campaigns as testable hypotheses tracked end-to-end (keyword → messaging → landing → meeting → deal).

Case study

Contact

You can reach me here.

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