ad-lead-quality-analyzer

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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.

Data & Documents 1,055 stars 194 forks Updated 3 days ago MIT

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Skill Content

# Ad Lead Quality Analyzer Meta optimizes for whatever conversion event you fire. For lead-gen and participant-recruitment campaigns that's almost always "signup" — but a signup is worthless if the lead never qualifies, never completes the requested action, or never gets paid out. The lowest-CPA campaign is often the one bringing in the *worst* leads. This skill joins what the ad platform knows (spend, signups) with what your own product knows (downstream funnel) and replaces vanity CPA with **true CAC per qualified lead**. It then classifies every creative into actionable buckets so you stop scaling the wrong winners. **Core principle:** The ad platform's CPA is a half-truth. Real optimization needs both halves of the funnel — pre-signup (the platform has it) and post-signup (you have it). Until they're joined, you're flying blind. ## When to Use - "Which ads are bringing in real leads vs. junk?" - "True CAC per qualified contributor / customer / participant" - "Why is my lowest-CPA campaign performing worst downstream?" - "Audit lead quality across creatives / audiences / placements" - "Should I trust Meta's CPA when scaling?" - "Find the creatives that look like winners but aren't" ## Pipeline Pattern Assumptions (Read First) This skill is opinionated about **what** to measure (true CAC per qualified lead, with cohort maturation, with vanity scoring) and agnostic about **how** the data is sourced. It assumes one of three standard attribution patterns: | Pattern | ...

Details

Author
gooseworks-ai
Repository
gooseworks-ai/goose-skills
Created
4 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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