Speakers: Michael Johns & Zhenyu Wang
Title: A Bayesian Approach to Media Mix Modeling
Video: https://youtu.be/UznM_-_760Y
Event description:
This talk describes how we built a Bayesian Media Mix Model of new customer acquisition using PyMC. We will explain the statistical structure of the model in detail, with special attention to nonlinear functional transformations, discuss some of the technical challenges we tackled when building it in a Bayesian framework, and touch on how we use it in production to guide our marketing strategy.
Discourse Discussion
https://discourse.pymc.io/t/a-bayesian-approach-to-media-mix-modeling-by-michael-johns-zhenyu-wang/6024
## Outline
- Marketing at HelloFresh
- What is a Media Mixed Model?
- Media Mixed Model: Structure & Function
- Bayesian model building workflow
- Model applications
## Timestamps
00:00 Introduction and outline of presentation
01:16 Marketing at HelloFresh (funnels, conversion, channels)
02:40 Measuring the effectiveness of marketing
05:00 What is Media Mix Modelling? (Multivariate regression model)
06:20 Structure of a Media Mix Model
07:51 Transformation functions (Reach function and Adstock function)
10:53 Benefits of using Bayesian methods to build a Media Mix Model
13:07 HelloFresh's Media Mix Model structure
19:46 Geometric Adstock Function
20:54 Nonlinear Saturation Function
21:16 The Bayesian MMM workflow
22:39 Applications of HelloFresh's Media Mix Model
26:41 Constrained optimization algorithm
29:18 Thank you!
Speaker info:
Michael Johns is a data scientist at HelloFresh US. His work focuses on building statistical models for business applications, such as optimizing marketing strategy, customer acquisition forecasting and customer retention.
LinkedIn: N/A
Zhenyu Wang is a Senior Business Intelligence Analyst at HelloFresh International. He works on developing and implementing methods to measure the effectiveness of advertising campaigns using analytic and statistical methods.
LinkedIn: N/A
Part of PyMCon2020.
More details at http://www.pymcon.com
#bayesian #statistics #marketing