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FractalFB - Facebook Ad Analysis and Reporting Tool

Overview

FractalFB is a powerful Python-based tool for analyzing Facebook ad performance data and generating comprehensive PDF reports with visualizations and actionable insights. It processes data from Facebook Ads Manager and evaluates performance against user-defined KPI ranges.

Features

  • Automated PDF report generation with detailed ad performance analysis
  • Interactive funnel visualizations and performance graphs
  • Historical performance tracking and trend analysis
  • Customizable KPI ranges and performance metrics
  • Automated recommendations for ad optimization
  • Sphinx documentation generation

Installation

pip install -r requirements.txt

Requirements

  • Python 3.x
  • pandas
  • PyYAML
  • FPDF
  • Pillow
  • Sphinx (for documentation)

Usage

Basic Usage

python app.py --csv facebook_ads.csv --yaml settings.yaml --output report.pdf

Generate Documentation

python app.py --generate-docs --docs-dir docs

Command Line Arguments

  • --csv: Path to Facebook ads CSV file (default: facebook_ads.csv)
  • --yaml: Path to settings YAML file (default: settings.yaml)
  • --output: Output PDF file path (default: kpi_report.pdf)
  • --database: Path to YAML database file (default: reports_database.yaml)
  • --generate-docs: Generate Sphinx documentation
  • --docs-dir: Directory for Sphinx documentation (default: docs)

Configuration

Create a settings.yaml file with your organization name and KPI ranges:

organization: "Your Organization Name"
kpi_ranges:
  "Quality ranking":
    min: 7
    max: 10
  "CTR (link click-through rate)":
    min: 0.5
    max: 3
  "Cost per results":
    min: 0.01
    max: 30

Output

The tool generates:

  1. Comprehensive PDF reports with:
    • KPI analysis
    • Funnel visualizations
    • Performance graphs
    • Recommendations
  2. Historical performance database
  3. Sphinx documentation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Copyright

Copyright © Michael Hoyt 2024. All Rights Reserved.

Support

For support, please open an issue in the GitHub repository.

Authors

  • Michael Hoyt

Acknowledgments

Special thanks to all contributors and users of FractalFB.