| name | lookalike-customer-finder-skill |
| description | Input your best customers and find 100+ companies that match the profile. Uses firmographic data, tech stack, growth signals, and similarity scoring to identify ideal prospects. Use when building target account lists or expanding to new markets. |
Lookalike Customer Finder
Analyze your best customers to build an ideal customer profile (ICP), then find 100+ companies that match using firmographic data, tech stack, growth signals, and similarity scoring. Produces tiered target account lists ranked by match quality for account-based outreach.
<quick_start>
Trigger: "find companies like [customer names]" or "build a lookalike target account list"
Output: ICP analysis, 100+ ranked lookalike companies with similarity scores, tiered targeting strategy
</quick_start>
<success_criteria>
Instructions
You are an expert at account-based prospecting and market analysis. Your mission is to analyze a company's best customers and find similar companies that match the same profile, creating high-quality target account lists.
Analysis Framework
Customer Profile Dimensions:
- Firmographics - Industry, size, revenue, location, public/private
- Technographics - Tech stack, tools used, platforms
- Growth Signals - Funding, hiring, expansion, momentum
- Behavioral - How they buy, budget cycles, decision-making
- Psychographics - Company culture, values, priorities
Similarity Scoring
Weighted Scoring Model:
- Industry Match: 25%
- Company Size Match: 20%
- Tech Stack Similarity: 15%
- Growth Stage Match: 15%
- Geography Match: 10%
- Revenue Range Match: 15%
Similarity Score: 0-100
- 90-100: Near-perfect match
- 80-89: Strong match
- 70-79: Good match
- 60-69: Moderate match
- Below 60: Weak match
Output Format
# Lookalike Customer Analysis
**Analysis Date**: [Date]
**Best Customers Analyzed**: [X] companies
**Lookalike Companies Found**: [X] companies
**Avg Similarity Score**: [X]/100
---
## 🎯 Ideal Customer Profile (ICP)
Based on analysis of your best customers:
**Firmographics**:
- **Industry**: [Primary industry] ([X]% of best customers)
- **Company Size**: [X-Y] employees (median: [X])
- **Revenue**: $[X]M - $[Y]M annually
- **Stage**: [Startup/Growth/Enterprise]
- **Geography**: [Primary regions]
- **Company Type**: [Public/Private/VC-backed]
**Tech Stack** (Common technologies):
- [Technology 1]: [X]% of best customers use
- [Technology 2]: [X]% of best customers use
- [Technology 3]: [X]% of best customers use
- [Technology 4]: [X]% of best customers use
**Growth Indicators**:
- [X]% recently raised funding
- [X]% actively hiring ([X]+ open roles)
- [X]% expanding to new markets
- [X]% launching new products
**Buying Behavior**:
- **Decision Maker**: Typically [C-level/VP/Director]
- **Deal Size**: $[X]K - $[Y]K
- **Sales Cycle**: [X] days average
- **Evaluation Process**: [Demo → Pilot → Purchase / Committee / etc.]
---
## 🏆 Your Best Customers (Reference)
### Top Customer #1: [Company Name]
**Why They're Great**:
- Revenue: $[X]K ARR
- Growth: [X]% YoY
- Engagement: [High usage, expansion, referrals]
- Profile: [Industry, size, stage]
**What They Have in Common** (with other best customers):
- All in [industry/vertical]
- All between [X-Y] employees
- All use [technology platform]
- All experiencing [growth phase]
---
## 📊 Lookalike Companies (Ranked by Similarity)
### #1 - [Company Name] | Similarity: 94/100 ⭐ EXCELLENT MATCH
**Company Profile**:
- **Industry**: [Industry]
- **Size**: [X] employees
- **Revenue**: $[X]M (estimated)
- **Location**: [City, State]
- **Founded**: [Year]
- **Stage**: [Growth stage]
- **Website**: [URL]
**Similarity Breakdown**:
- Industry: ✅ Perfect match ([same industry])
- Size: ✅ [X] employees (vs your avg [Y])
- Tech Stack: ✅ Uses [X]/[Y] common technologies
- Growth: ✅ Raised $[X]M in last 12 months
- Geography: ✅ [Same region as best customers]
- Revenue: ✅ $[X]M (within target range)
**Why They're a Great Prospect**:
1. **Same Problem**: [Specific pain point your best customers had]
2. **Buying Window**: [Indicators they're ready to buy]
3. **Budget Signals**: [Funding/growth = budget available]
4. **Tech Fit**: Already using [complementary technology]
**Contact Intelligence**:
- **Decision Maker**: [Name], [Title]
- **Champion Candidate**: [Name], [Title]
- **Mutual Connections**: [X] 2nd degree connections
- **Recent Activity**: [Hiring/funding/expansion news]
**Recommended Approach**:
> "Hi [Name], noticed [Company] recently [growth signal]. We work with similar companies like [Best Customer 1] and [Best Customer 2] to solve [problem]. Given [their situation], thought it might be relevant..."
**Priority**: 🔴 HIGH - Reach out this week
---
### #2 - [Company Name] | Similarity: 91/100 ⭐ EXCELLENT MATCH
[Similar structure]
---
### #3-10 - Strong Matches (85-90 similarity)
| Rank | Company | Industry | Size | Score | Key Signal | Priority |
|------|---------|----------|------|-------|-----------|----------|
| 3 | [Company] | [Industry] | [X] emp | 89 | Just raised Series B | High |
| 4 | [Company] | [Industry] | [X] emp | 88 | Hiring 15+ roles | High |
| 5 | [Company] | [Industry] | [X] emp | 87 | Expanding to US | High |
| 6 | [Company] | [Industry] | [X] emp | 86 | New VP joined | Medium |
| 7 | [Company] | [Industry] | [X] emp | 86 | Product launch | Medium |
| 8 | [Company] | [Industry] | [X] emp | 85 | Same tech stack | Medium |
| 9 | [Company] | [Industry] | [X] emp | 85 | Similar customers | Medium |
| 10 | [Company] | [Industry] | [X] emp | 85 | [Signal] | Medium |
---
### #11-50 - Good Matches (70-84 similarity)
**Tier 2 Prospects** (50 companies)
Common characteristics:
- Industry: [X]% match your ICP
- Size: Slightly smaller/larger but close
- Tech: Using [X]/[Y] target technologies
- Geography: [X]% in target regions
**Export Available**: CSV with company details, contacts, and prioritization
---
### #51-100 - Moderate Matches (60-69 similarity)
**Tier 3 Prospects** (50 companies)
Why they score lower:
- Industry adjacent but not exact
- Size outside ideal range
- Different tech stack
- Different growth stage
**Recommendation**: Reach out if you exhaust Tier 1 & 2
---
## 🔍 Market Insights
### Industry Distribution
| Industry | # Companies | % of Lookalikes |
|----------|-------------|-----------------|
| [Industry 1] | XX | XX% |
| [Industry 2] | XX | XX% |
| [Industry 3] | XX | XX% |
| Other | XX | XX% |
**Insight**: [X]% of lookalikes concentrated in [industry], suggesting strong product-market fit there.
---
### Size Distribution
| Company Size | # Companies | % of Lookalikes |
|--------------|-------------|-----------------|
| 1-50 | XX | XX% |
| 51-200 | XX | XX% |
| 201-500 | XX | XX% |
| 500-1000 | XX | XX% |
| 1000+ | XX | XX% |
**Sweet Spot**: [X-Y] employees ([X]% of best customers in this range)
---
### Geographic Distribution
| Region | # Companies | % of Lookalikes |
|--------|-------------|-----------------|
| [Region 1] | XX | XX% |
| [Region 2] | XX | XX% |
| [Region 3] | XX | XX% |
**Insight**: [Observation about geographic concentration]
---
### Growth Stage Distribution
| Stage | # Companies | % of Lookalikes |
|-------|-------------|-----------------|
| Seed | XX | XX% |
| Series A | XX | XX% |
| Series B | XX | XX% |
| Series C+ | XX | XX% |
| Bootstrapped | XX | XX% |
**Best Stage**: [Stage] companies have highest win rate
---
### Stage S — Suppression Gate
Before including any contact in outreach or dial output:
- **EXCLUDE** if `bdr_suppression_until` IS SET AND `bdr_suppression_until` > TODAY
- **INCLUDE** if `bdr_suppression_until` IS NOT SET (never suppressed)
- **INCLUDE** if `bdr_suppression_until` < TODAY (cooling period expired)
HubSpot filter: `propertyName: "bdr_suppression_until", operator: "NOT_HAS_PROPERTY"` OR `operator: "LT", value: TODAY_ISO`
Reference: `lead-suppression-spec` (bdr_suppressed, bdr_suppression_reason, bdr_suppression_until)
---
## 🎯 Targeting Strategy
### Tier 1: Top 10 (Weeks 1-2)
**Approach**: Highly personalized, multi-channel outreach
- Research each company deeply
- Find warm intro paths
- Custom demos and case studies
- Executive-level engagement
**Expected Results**:
- Response Rate: 40-50%
- Meeting Rate: 25-30%
- Close Rate: 15-20%
---
### Tier 2: Next 40 (Weeks 3-6)
**Approach**: Personalized at scale
- AI-generated personalization
- Account-based sequences
- Industry-specific content
- Multi-threading
**Expected Results**:
- Response Rate: 20-30%
- Meeting Rate: 12-15%
- Close Rate: 8-12%
---
### Tier 3: Next 50 (Weeks 7-10)
**Approach**: Volume with relevance
- Template-based outreach
- Segment by characteristics
- Nurture over time
- Marketing automation
**Expected Results**:
- Response Rate: 10-15%
- Meeting Rate: 5-8%
- Close Rate: 3-5%
---
## 🚀 Quick Start Action Plan
### Week 1: Top 10 Deep Dive
- [ ] Research each of top 10 companies
- [ ] Find mutual connections
- [ ] Identify decision makers
- [ ] Draft personalized outreach
- [ ] Begin outreach
### Week 2: Tier 1 Follow-up + Tier 2 Prep
- [ ] Follow up with Tier 1 non-responders
- [ ] Schedule meetings with responders
- [ ] Export Tier 2 list (40 companies)
- [ ] Build outreach sequences
- [ ] Enrich contact data
### Week 3-4: Tier 2 Outreach
- [ ] Launch Tier 2 campaign
- [ ] Monitor responses
- [ ] Continue Tier 1 meetings
- [ ] Adjust messaging based on learnings
### Week 5-6: Tier 2 Follow-up + Tier 3 Launch
- [ ] Follow up Tier 2
- [ ] Prepare Tier 3 campaign
- [ ] Review what's working
- [ ] Optimize approach
---
## 💡 Enrichment Data Sources
**Recommended Tools**:
- **Company Data**: Crunchbase, ZoomInfo, LinkedIn
- **Tech Stack**: BuiltWith, Wappalyzer, Datanyze
- **Funding**: Crunchbase, PitchBook, CB Insights
- **Contacts**: Apollo, RocketReach, Hunter.io
- **Intent**: 6sense, Bombora, G2
**Data Points to Gather**:
- Decision maker names and emails
- Recent company news
- Tech stack details
- Employee count growth
- Job postings
- Social media activity
---
## 📈 Success Metrics
**Track These KPIs**:
- **Outreach Metrics**: Response rate, meeting rate
- **Quality Metrics**: Similarity score correlation to close rate
- **Efficiency Metrics**: Time to first meeting, sales cycle length
- **Outcome Metrics**: Win rate by similarity tier
**Hypothesis to Test**:
- Do 90+ similarity companies close faster?
- Do certain industries respond better?
- Does company size affect deal size?
---
## 🔄 Continuous Improvement
### Monthly Refresh
- Add new best customers to analysis
- Remove churned customers
- Update ICP based on recent wins
- Find new lookalikes matching updated profile
### Quarterly Review
- Analyze which lookalike tiers performed best
- Adjust similarity weightings
- Expand to adjacent markets
- Update targeting strategy
Best Practices
- Quality Over Quantity: 10 perfect matches > 100 mediocre ones
- Use Multiple Criteria: Don't just match on industry and size
- Look for Growth Signals: Companies in growth mode buy more
- Prioritize Recent Similarity: Recently funded/hired companies
- Test and Learn: Track which profiles actually close
- Refresh Regularly: Markets change, keep list current
- Enrich Before Outreach: Get contact data before campaign
Common Use Cases
Trigger Phrases:
- "Find 100 companies like my top 10 customers"
- "Who else looks like [Best Customer Company]?"
- "Build a lookalike target account list"
- "Identify companies similar to our best customers"
Example Request:
"Here are my top 10 customers: Stripe, Square, Braintree, Adyen, Checkout.com. All are payment processors between 200-1000 employees. Find 100 companies with similar profiles prioritized by similarity score."
Response Approach:
- Analyze common characteristics of best customers
- Build ideal customer profile (ICP)
- Search market for matching companies
- Score each on similarity dimensions
- Rank and prioritize by score
- Provide targeting strategy
Remember: Your best future customers look a lot like your best current customers!
Emit Outcome Sidecar
As the final step, write to ~/.claude/skill-analytics/last-outcome-lookalike-customer-finder.json:
{"ts":"[UTC ISO8601]","skill":"lookalike-customer-finder","version":"1.0.0","variant":"default",
"status":"[success|partial|error]","runtime_ms":[estimated ms from start],
"metrics":{"seed_customers_analyzed":[n],"lookalikes_found":[n],"icp_dimensions_scored":[n]},
"error":null,"session_id":"[YYYY-MM-DD]"}
Use status "partial" if some stages failed but results were produced. Use "error" only if no output was generated.
