Meta Lead Quality Analyzer - Lead Scoring & Offline CAPI Feedback

Score Meta lead quality and feed offline conversions back to the algorithm.

No hosted demo for this project — it runs locally. See install instructions below.

View Source on GitHub →

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Meta Ads

Overview

Meta Lead Quality Analyzer scores the quality of leads generated from Meta lead ads, flags fake or low-intent leads (burner emails, invalid phone numbers) and structures offline conversion feedback so the advertising algorithm learns which leads actually become customers.

Screenshot of the meta-lead-quality-analyzer project
Report preview from the repository docs showing a lead quality scoring output.

Problem It Solves

Meta lead campaigns can fill pipelines with junk leads. When algorithms optimize for the wrong signals, cost per qualified lead rises and sales teams waste time on unqualified contacts.

Target Users

  • Lead generation marketers
  • Agencies managing Meta lead ads
  • CRM and sales operations teams

Key Features

  • Lead quality scoring and cost-per-qualified-lead (CPQL) analysis
  • Fake-lead detection heuristics for burner emails and invalid numbers
  • Offline conversion feedback pattern for Meta Conversions API
  • CSV export of scored leads
  • CLI interface with CI (ruff, mypy, pytest)

Technologies

  • Python
  • Typer
  • Meta Conversions API patterns
  • Pydantic
  • pytest

Contribution & Skills

Sole author. Designed the scoring model, fake-lead heuristics, offline feedback mapping and reporting exports.

Install & Usage

Install

git clone https://github.com/hamid-digitalmarketer/meta-lead-quality-analyzer.git
cd meta-lead-quality-analyzer
pip install -e ".[dev]"

Run analysis

meta-lead audit
meta-lead export --format csv

Full documentation lives in the repository README →

FAQ

How does it detect fake leads?

It applies heuristics for burner email patterns, invalid or inconsistent phone numbers and low-intent signals, then scores each lead so sales can prioritize follow-up.

Does it upload to Meta automatically?

The repository provides the offline-conversion feedback structure and guidance; uploads are performed through your Meta Conversions API integration using the generated payloads.

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