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.

Robyn is available in R and Python. For installation and usage guide see below. Please note that the current Python version is a LLM-translated Beta version and might encounter bugs.

Quick start for R

1. Installing the package

• Install Robyn latest R 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 while installing the package, try setting options(timeout=400).

• Robyn requires the Python library Nevergrad. If encountering Python-related error during installation, please check out the step-by-step guide as well as this issue to get more info.

• For Windows, if you get openssl error, please see instructions here and here to install and update openssl.

2. Getting started

• Use this 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 to explore more details about Project Robyn.

• Join our public group to exchange with other users and interact with team Robyn.

• Take Meta's official Robyn blueprint course online

Quick start for Python (Beta)

The Python version of Robyn is rewritten from Robyn's R package version 3.11.1 to Python using object oriented programming principles and modular architecture for a robust solution. It was developed by utilizing various LLMs and AI workflows like Llama. As is common with any AI-based solutions, there may be potential challenges in translating code from one language to another. In this case, we anticipate that there could be some issues in the translation from R to Python. However, we believe in the power of community collaboration and open-source contribution. Therefore, we are opening this project to the community to participate and contribute. Together, we can address and resolve any issues that may arise, enhancing the functionality and efficiency of the Python version of Robyn. We look forward to your contributions and to the continuous improvement of this project.

1. Installing the package

• Install Robyn latest Python package version:

## Pypi
pip3 install robynpy

## DEV VERSION
# if you are pulling source from github, install dependencies using requirements.txt
pip3 install -r requirements.txt

2. Getting started

• The directory python/src/robyn/tutorials contains tutorials for most common scenarios. Tutorials use simulated dataset provided in the package.

• There are two ways of running Python Robyn; one is tutorial1.ipynb and second is tutorial1_src.ipynb.

3. Running end-to-end

Option 1:

• tutorial1.ipynb is the main notebook that runs the end-to-end flow. It is designed for majority of the users who would prefer a one click solution that runs the robyn flow end-to-end with minimal knowledge of the underlying logic. It should run without any changes required if you wish to use the simulated dataset for testing purposes.

• This notebook uses APIs available in python/src/robyn/robyn.py to set the configs, run feature engineering, run model training, evaluate models with clustering, generate one pagers and perform budget allocation.

• Change any of the configs directly in the notebook and avoid changes to robyn.py for what can be configurable.

Option 2:

• tutorial1_src.ipynb runs the end-to-end flow of robyn python but with a lot more flexibility. It is designed for users who would like to have more control over which modules are and aren't run (ie. skipping clustering/one pager plots/budget allocation etc.). It should run without any changes required if you wish to use the simulated dataset for testing purposes.

• This notebook doesn't use APIs available in python/src/robyn/robyn.py but instead, calls the modules directly with the appropriate parameters. In this way, it is more flexible but still expects the users to understand the underlying logic that may change when using various parameter values.

Quick start Python wrapper (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. It serves as a workaround when the Python native version is not yet available or up-to-date. Please see the usage guide here.

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

• [email protected], Gufeng Zhou, Marketing Science, Robyn creator
• [email protected], Igor Skokan, Marketing Science Director, open source