Robyn is an open-source Marketing Mix Modeling (MMM) package from Meta Marketing Science, designed to democratize advanced modeling techniques for advertisers. It leverages machine learning for media channel efficiency analysis, adstock, and saturation curves, making sophisticated MMM accessible to businesses of all sizes, particularly those with granular digital data.

How It Works

Robyn employs a suite of ML techniques including Ridge regression, evolutionary algorithms for hyperparameter tuning, time-series decomposition, and gradient-based optimization for budget allocation. This approach aims to reduce human bias and automate complex modeling steps, providing insights into media effectiveness and optimizing marketing spend.

Quick Start & Requirements

• R: install.packages("Robyn") or remotes::install_github("facebookexperimental/Robyn/R"). Requires Python library Nevergrad.
• Python (Beta): Requires R and glmnet R package. Setup involves creating and activating a Python virtual environment. Install via pip3 install robynpy or from source.
• Python Wrapper (Beta): Requires the Robyn R package to be installed first.
• Resources: Official R demo script and Python tutorials are available.

Highlighted Details

• Experimental, semi-automated MMM package from Meta Marketing Science.
• Utilizes various ML techniques for media channel analysis.
• Available in both R and Python (Python version is a beta LLM translation).
• Aims to make MMM accessible and reduce bias in modeling.

Maintenance & Community

• Created by Gufeng Zhou and Igor Skokan from Meta Marketing Science.
• Community exchange is encouraged via a public group.
• Official online course available.

Licensing & Compatibility

• MIT Licensed.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The Python version is a beta LLM translation and may contain bugs. The Python wrapper is dependent on the R package.