This repository provides a toolkit for media mix modeling that respects user privacy. Marketing teams and analysts can estimate the incremental impact of different marketing channels (e.g., TV, search, social, email) on key outcomes such as conversions or revenue without relying on user‑level tracking. Instead, the toolkit uses aggregated and anonymized data to build robust models.
- Aggregated Data Pipelines: Ingest channel‑level spend, impressions, and conversions aggregated over time, ensuring no personal data is collected.
- Modeling Frameworks: Includes baseline linear models and advanced Bayesian hierarchical models to estimate channel contribution while accounting for saturation and ad‑stock effects.
- Privacy Preservation: Demonstrates how to apply techniques such as differential privacy to add noise to input data so individual consumers cannot be identified.
- Visualization Tools: Generate charts that show marginal return curves, channel saturation, and expected lift versus spend, helping stakeholders understand media efficiency.
- Extensible Design: Modular codebase so analysts can plug in their own data sources, priors, and model structures.
- Clone the repository and install dependencies listed in
requirements.txt. - Place your aggregated channel data in the
data/directory following the provided schema. - Run the example notebook in
PRO_1.pyto explore a simple MMM using synthetic data. - Use
analysis_summary.pyto produce a summary report of channel efficiencies. - Check
recommendations.mdfor guidance on interpreting model outputs and making investment decisions.
By modeling marketing spend at an aggregated level and applying privacy‑preserving techniques, this toolkit allows companies to optimize their media budgets without violating consumer trust or privacy regulations. Teams can identify the most efficient channels and reallocate budgets to maximize ROI while complying with privacy laws.