This repository contains materials related to Hajime Takeda's presentation on media mix modeling at ODSC East 2023. The talk demonstrates how to measure the effectiveness of advertising using Python/LightweightMMM and R/Robyn library.

Contents

• LightweightMMM(Python) Demo Code
• Robyn(R) Demo Code

Supplementary Contents

• Presentation Slide
• ODSC blog

Related Links

• LightweightMMM : A lightweight Bayesian Marketing Mix Modeling (MMM) library (Python)

• Robyn : The Open Source Marketing Mix Model Package from Meta Marketing Science (R)

• sibylhe/mmm_stan : Python/STAN Implementation of Multiplicative Marketing Mix Model

Key Reference

• Jin, Y., Wang, Y., Sun, Y., Chan, D., & Koehler, J. (2017). Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects. Google Inc.
• Chan, D., & Perry, M. (2017). Challenges and Opportunities in Media Mix Modeling.