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.
How to Run It
The/niche-signal-discovery skill automates this entire play. Give it your won and lost domain lists and it handles enrichment, analysis, and reporting.
- Discovers your vertical - researches what you sell, who you sell to, and your competitive landscape
- Generates vertical-specific configs - keywords, tech stack tools, and job roles tailored to your buyer persona
- Enriches every company - pulls multi-page website content (~5 pages per domain) and job listings via Deepline CLI
- Runs differential analysis - computes Laplace-smoothed lift scores across every dimension to find what separates wins from losses
- Generates a full report with signal strength dashboards, scoring models, Apollo search recipes, and buyer persona quick-reference cards
What You Get
- Signal strength dashboard with lift scores and visual bars (e.g., “Hiring 3+ SDRs simultaneously” at 4.2x lift)
- Anti-fit signals that flag accounts to skip (e.g., “Consumer checkout language” at 0.2x lift)
- Lead scoring model - 0-100 point system with three tiers (Core Fit, Buying Intent, Infrastructure Readiness)
- Pre-built Apollo URLs for people and company searches based on discovered signals
- Source evidence - exact quotes with page URLs and job listings backing every signal
- Scoring cheatsheet with “How to Check” column for each signal
Cost
The skill uses Serper for page discovery and Firecrawl for content extraction - the cheapest enrichment path available.| Step | Tool | Credits per company | Total (60 companies) |
|---|---|---|---|
| Discover relevant pages | Serper Google Search | 0.02 | 1.20 |
| Scrape ~5 pages per domain | Firecrawl Scrape | 0.05 | 3.00 |
| Job listings | Crustdata Job Listings | 0.40 | 24.00 |
| Total | ~0.47 | ~28 |
If you have an Apollo API key connected, job postings are BYOK (free on
Deepline) - bringing the total down to ~0.07 credits per company.
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" | "$20M-$80M ARR with engineering team > 30% of headcount” |
Account Scoring → | Closed-Lost Recovery → | I Have X, I Want Y →