git:20251022.14b7f5b to git:20260608.d82d20f

15 added, 390 removed. Audit A to A.

---

name: lookalike-customer-finder

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

- Find companies that look exactly like your best customers.

- ## 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**:

- 1. **Firmographics** - Industry, size, revenue, location, public/private

- 2. **Technographics** - Tech stack, tools used, platforms

- 3. **Growth Signals** - Funding, hiring, expansion, momentum

- 4. **Behavioral** - How they buy, budget cycles, decision-making

- 5. **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

- ```markdown

- # 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)

- |------|---------|----------|------|-------|-----------|----------|

- ---

- ### #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

- ---

- ## 🎯 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

- 1. **Quality Over Quantity**: 10 perfect matches > 100 mediocre ones

- 2. **Use Multiple Criteria**: Don't just match on industry and size

- 3. **Look for Growth Signals**: Companies in growth mode buy more

- 4. **Prioritize Recent Similarity**: Recently funded/hired companies

- 5. **Test and Learn**: Track which profiles actually close

- 6. **Refresh Regularly**: Markets change, keep list current

- 7. **Enrich Before Outreach**: Get contact data before campaign

- ### Common Use Cases

+ Analyze a company's best customers and find similar companies that match the same profile, producing a high-quality, ranked target account list.

- **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"

+ ## Contents

- **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."

+ - `references/scoring-model.md` - Profile dimensions, weighted scoring model, and score bands.

+ - `references/output-template.md` - Full Markdown report structure (ICP, ranked lookalikes, market insights, targeting strategy, action plan).

+ - `references/data-sources.md` - Recommended enrichment tools and data points to gather.

+ - `references/examples.md` - Best practices, trigger phrases, and an example request.

- **Response Approach**:

- 1. Analyze common characteristics of best customers

- 2. Build ideal customer profile (ICP)

- 3. Search market for matching companies

- 4. Score each on similarity dimensions

- 5. Rank and prioritize by score

- 6. Provide targeting strategy

+ ## Workflow

- Remember: Your best future customers look a lot like your best current customers!

+ 1. Collect the best customers provided. If none are given, ask for the top 5-10 accounts.

+ 2. Analyze common characteristics across them. See `references/scoring-model.md` for the five profile dimensions.

+ 3. Build the Ideal Customer Profile (ICP) from those shared traits.

+ 4. Search the market for companies matching the ICP. Pull firmographics, tech stack, growth signals, and contacts from the tools in `references/data-sources.md`.

+ 5. Score each candidate 0-100 using the weighted scoring model in `references/scoring-model.md`.

+ 6. Rank and tier the companies by score (Tier 1: top 10, Tier 2: next 40, Tier 3: next 50).

+ 7. Produce the report following `references/output-template.md`, including market insights, a tiered targeting strategy, and a quick-start action plan.

+ 8. Apply the best practices in `references/examples.md` throughout: favor quality over quantity, weight growth signals, and enrich contacts before recommending outreach.