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lookalike-customer-finder

lookalike-customer-finder

github.com/onewave-aiMarketing & growth393329 installsUpdated 1w ago
Repository

About lookalike-customer-finder

A marketing skill that analyzes a company's best customers and develops a ranked list of similar target accounts. It defines an Ideal Customer Profile, scores candidates, and structures findings into market insights and an action plan.

Key features

  1. 01Analyze shared customer traits
  2. 02Build an Ideal Customer Profile
  3. 03Score candidates from 0–100
  4. 04Rank accounts into tiers
  5. 05Create targeting and action plans

Use cases

  1. 01Finding prospects similar to top customers
  2. 02Prioritizing target accounts for outreach
  3. 03Building a tiered account targeting plan

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/onewave-ai/claude-skills --skill lookalike-customer-finder

After installing, type/lookalike-customer-finderin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
data-sources.md
examples.md
output-template.md
scoring-model.md

Details

From the project README

Lookalike Customer Finder

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

Contents

  • 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.

Workflow

  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.

Comments

Markdown supported