Robyn: Continuous & Semi-Automated MMM

The Open Source Marketing Mix Model Package from Meta Marketing Science

Introduction

• What is Robyn?: Robyn is an experimental, semi-automated and open-sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. It uses various machine learning techniques (Ridge regression, multi-objective evolutionary algorithm for hyperparameter optimization, time-series decomposition for trend & season, gradient-based optimization for budget allocation, clustering, etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich data sources.

• Why are we doing this?: MMM used to be a resource-intensive technique that was only affordable for "big players". As the privacy needs of the measurement landscape evolve, there's a clear trend of increasing demand for modern MMM as a privacy-safe solution. At Meta Marketing Science, our mission is to help all businesses grow by transforming marketing practices grounded in data and science. It's highly aligned with our mission to democratizing MMM and making it accessible for advertisers of all sizes. With Project Robyn, we want to contribute to the measurement landscape, inspire the industry and build a community for exchange and innovation around the future of MMM and Marketing Science in general.

Quick start for R

1. Installing the package

• Install Robyn latest package version:

  
  ## CRAN VERSION
  install.packages("Robyn")

DEV VERSION

If you don't have remotes installed yet, first run: install.packages("remotes")

remotes::install_github("facebookexperimental/Robyn/R")

  * If it's taking too long to download, you have a slow or unstable internet connection, and have [issues](https://github.com/facebookexperimental/Robyn/issues/309) while installing the package, try setting `options(timeout=400)`.

  * Robyn requires the Python library [Nevergrad](https://facebookresearch.github.io/nevergrad/). If encountering Python-related 
  error during installation, please check out the [step-by-step guide](https://github.com/facebookexperimental/Robyn/blob/main/demo/demo.R) as well as this [issue](https://github.com/facebookexperimental/Robyn/issues/189) to get more info.

  * For Windows, if you get openssl error, please see instructions
  [here](https://stackoverflow.com/questions/54558389/how-to-solve-this-error-while-installing-python-packages-in-rstudio/54566647) and
  [here](https://dev.to/danilovieira/installing-openssl-on-windows-and-adding-to-path-3mbf) to install and update openssl.

**2. Getting started**

  * Use this [demo.R](https://github.com/facebookexperimental/Robyn/tree/main/demo/demo.R) script as step-by-step guide that is
  intended to cover most common use-cases. Test the package using simulated dataset provided in the package. 

  * Visit our [website](https://facebookexperimental.github.io/Robyn/) to explore more details about Project Robyn.

  * Join our [public group](https://www.facebook.com/groups/robynmmm/) to exchange with other users and interact with team Robyn.

  * Take Meta's [official Robyn blueprint course](https://www.facebookblueprint.com/student/path/253121-marketing-mix-models?utm_source=readme) online 

## Quick start Python (Robyn API for Python beta)

The Robyn API for Python (beta), first released on Nov.22nd 2023, is a plumber-based solution that requires the installation of the Robyn R pacakge first. Please see the usage guide [here](https://github.com/facebookexperimental/Robyn/blob/main/robyn_api/robyn_python_notebook.ipynb).

## License

Meta's Robyn is MIT licensed, as found in the LICENSE file.

- Terms of Use - https://opensource.facebook.com/legal/terms 
- Privacy Policy - https://opensource.facebook.com/legal/privacy
- Defensive Publication - https://www.tdcommons.org/dpubs_series/4627/

## Contact

* gufeng@meta.com, Gufeng Zhou, Marketing Science, Robyn creator
* igorskokan@meta.com, Igor Skokan, Marketing Science Director, open source