October 31, 2022
By Thomas Wiecki
Among Bayesian early adopters, digital marketing is chief. While many industries are embracing Bayesian modeling as a tool to solve some of the most advanced data science problems, marketing is facing unique challenges for which this approach provides elegant solutions. Among these challenges are a decrease in quality data, driven by an increased demand for online privacy and the imminent "death of the cookie" which prohibits online tracking. In addition, as more companies are building internal data science teams, there is an increased demand for in-house solutions.
In this talk Thomas explains how Bayesian modeling addresses these issues by:
(i) Incorporating expert knowledge of the structure as well as about plausible parameter rangers.
(ii) Connecting multiple different data sets to increase circumstantial evidence of latent user features.
(iii) Principled quantification of uncertainty to increase robustness of model fits and interpretation of the results.
Inspired by real-world problems we encountered at PyMC Labs, we will look at Media Mix Models for marketing attribution and Customer Lifetime Value models and various hybrids between them.
Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world class team of Bayesian modelers founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.
00:00 Welcome!
0:05 Speaker introduction and PyMC 4 release announcement
1:15 PyMC Labs- The Bayesian consultancy
2:39 Why is marketing so eager to adopt Bayesian solutions
3:49 Case Study: Estimating Marketing effectiveness
6:00 Estimating Customer Acquisition Cost (CAC) using linear regression
7:36 Drawbacks of linear regression in estimating CAC
10:02 Blackbox Machine learning and its drawbacks
11:27 Bayesian modelling
11:52 Advantages of Bayesian modelling
14:12 How does Bayesian modelling work?
16:53 Solution proposals(priors)
17:26 Model structure
19:57 Evaluate solutions
20:16 Plausible solutions(posterior)
22:36 Improving the model
23:38 Modelling multiple Marketing Channels
24:51 Modelling channel similarities with hierarchy
26:13 Allowing CAC to change over time
28:00 Hierarchical Time Varying process
30:05 Comparing Bayesian Media Mix Models
30:47 What-If Scenario Forecasting
31:53 Adding other data sources as a way to help improve or inform estimates
33:00 When does Bayesian modelling work best?
33:35 Intuitive Bayes course
34:38 Question 1: Effectiveness of including variables seasonality?
36:03 Question 2: What is your recommendation for the best way to choose priors?
38:16 Question 3: How to test if an assumption about the data is valid?
39:07 Question 4: Do you take the effect of different channels on each other into account?
41:33 Thank you!