Niche Signal Discovery
Every team knows the obvious ICP signals — company size, industry, funding stage. The signals that actually separate your wins from your losses are usually more specific: a particular tech stack combination, a hiring pattern, a revenue growth rate, a specific competitor on the account. This play finds those signals by mining your actual deal history.The Play
Export wins and losses from CRM
Pull closed-won and closed-lost deals from HubSpot or Salesforce for the last 12 months. Include deal size, sales cycle, competitor mentioned, close reason, and any custom fields your team tracks.
Enrich with deep firmographic data
For each company, pull detailed firmographics: employee count, department breakdown, tech stack, hiring velocity by role, funding history, revenue estimates, geographic presence, recent news.
Run comparative analysis
Ask Claude to compare enriched wins vs losses across every dimension. Look for attributes where the distribution is meaningfully different between wins and losses.
Identify niche signals
The goal is non-obvious patterns. Examples from real analyses:
- “We win 4x more often at companies with 3-8 open engineering roles”
- “Deals close 2x faster when the company uses both Salesforce and Outreach”
- “We lose 80% of deals where the primary competitor is [X]”
- “Companies that raised Series B in the last 6 months close at 3x the rate of Series A”
Validate with holdout data
Test the discovered signals against held-out deals. If a signal predicts wins/losses on data the model hasn’t seen, it’s real. If it only works on training data, it’s noise.
Feed into account scoring
Add validated niche signals to your account scoring model. These signals become the weighted dimensions that separate generic ICP from your specific propensity model.
Tell Claude Code
“Pull all closed-won and closed-lost deals from HubSpot for the last 12 months. Enrich each company with Crustdata firmographics — I want headcount by department, tech stack, hiring velocity, funding history, and revenue. Compare wins vs losses across every dimension. Find the signals where wins and losses look meaningfully different. Validate each signal on 25% held-out deals. Write findings to niche-signals.csv.”
What Niche Signals Look Like
Generic ICP attributes (company size, industry) get you to the right ballpark. Niche signals get you to the right accounts.| Generic signal | Niche signal |
|---|---|
| ”SaaS companies" | "SaaS companies using Salesforce + Outreach with 200-500 employees" |
| "Series A-C funding" | "Series B in the last 6 months with 20%+ headcount growth" |
| "Hiring GTM roles" | "3+ SDR/BDR roles open simultaneously" |
| "US-based" | "US-based with at least one office outside the Bay Area" |
| "Enterprise" | "80M ARR with engineering team > 30% of headcount” |
Cost Estimate
- CRM data pull: Free (via MCP)
- Company enrichment: ~0.4 credits per account (Crustdata)
- Signal analysis and validation: no enrichment cost (runs on already-enriched data)
- 200 historical deals: ~80 credits for full enrichment
Account Scoring → | Closed-Lost Recovery → | I Have X, I Want Y →