12-03, 14:30–15:00 (UTC), Talk Track I
Media Mix Modeling, also called Marketing Mix Modeling (MMM), is a technique that helps advertisers to quantify the impact of several marketing investments on sales.
If a company advertises in multiple media (TV, digital ads, magazines, etc.), how can we measure the effectiveness and make future budget allocation decisions? Traditionally, regression modeling has been used, but obtaining actionable insights with that approach has been challenging.
Recently, many researchers and data scientists have tackled this problem using Bayesian statistical approaches. For example, Google has published multiple papers about this topic.
In this talk, I will show the key concepts of a Bayesian approach to MMM, its implementation using Python, and practical tips.
Agenda
- Introduction
- What is Media Mix Modeling?
- Data Preparation
- Modeling : Bayesian approach with Carryover & Shape Effect proposed by Google
- Demo with LightweightMMM
- Insights and Actions
- Summary
Key Takeaways
- You will understand the key concepts and approaches of Media Mix Modeling.
- You will learn how to build Bayesian models using Python for media spend optimization.
Target Audience
- Data analysts and data scientists who are interested in marketing and advertising.
- Data analysts, data scientists, data engineers, software developers, or other IT specialists who want to collaborate with marketing teams more effectively.
- Marketers or executives who want to improve media spending efficiency.
The following knowledge is preferred to get the most out of this talk :
- A basic understanding of Python
Slide
https://docs.google.com/presentation/d/1pPra3eLJ9-lYwwvx8_Ivj_sj3V2gmE75cb13comV9pc/edit?usp=sharing
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.
- LightweightMMM : A lightweight Bayesian Marketing Mix Modeling (MMM) library (Python)
- Robyn : An experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science (R)
- sibylhe/mmm_stan : Python/STAN Implementation of Multiplicative Marketing Mix Model
No previous knowledge expected
I started my career as a data analyst at a global consumer goods company. Currently, I am a leader in data analytics, web development, and digital marketing at a startup.