niche-signal-discovery

# 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. ## Pipeline ``` 0. Discover target company (what they sell, who they sell to) 0.5. Discover ecosystem (competitors, tech stack, buyer personas) 1. Prepare input CSV (deduplicate within won/lost groups) 1.0.5 Build "do not re-contact" index from user's existing list (scripts/dedupe_utils.py) 1.5. Generate verti...