This repository contains materials related to Hajime Takeda's presentation on media mix modeling at PyData Global 2022. The talk demonstrates how to measure the effectiveness of advertising using Python and the LightweightMMM library.

Contents

• Slide Deck
• Demo Code
• Video

Supplementary Contents

• Towards Data Science Article

Related Libraries

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