Niche Signal Discovery

Discover differential signals between Closed Won and Closed Lost accounts by extracting multi-page website content and job listings, then computing Laplace-smoothed lift scores to identify what distinguishes buyers from non-buyers.

Prerequisites

• Deepline CLI — All enrichment runs through deepline enrich. No separate API keys for exa/crustdata/apollo etc.
• Python 3 stdlib only — no pip dependencies for any shipped script.
• Credits — ~0.47 credits/company (serper 0.02 + firecrawl 0.05 + crustdata 0.40). Step 7 contact discovery is additional. Always get user approval before paid steps.

Deepline-First Principle

Use deepline enrich for all enrichment, deepline tools execute for one-offs, deepline playground for inspection. Reruns are idempotent. Refer to deepline-gtm for command patterns and provider playbooks.

Input requirements

• Won and lost customer domain lists (≥20 won + ≥10 lost for statistical significance)
• Lookalikes can supplement Won if Closed Won < 15. Add a Dataset Caveat to the report.
• Target company context from Step 0 — what they sell, who they sell to, key personas.