Bayesian Modeling in Industry


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 I will explain 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; and (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 some of the best Bayesian modelers out there and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University. Website link:

Open Data Science




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