[Online Meetup] Bayesian Marketing Mix Models: State of the Art and their Future (Sep 21, 2022)


Thomas Wiecki and Luca Fiaschi


11am ET/15:00 UTC


60 minutes

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OR livestream: Bayesian Marketing Mix Models - YouTube


-Improving the Speed and Accuracy of Bayesian Media Mix Models - PyMC Labs
-Bayesian Media Mix Models: Modelling changes in marketing effectiveness over time - PyMC Labs

Event Description

This event is a discussion between Luca and Thomas about Bayesian modeling in online marketing with a special focus on Marketing Mix Models and Customer Lifetime Value models. The panel discussion will be followed by Q&A.

In this panel discussion, Luca Fiaschi will share his thoughts on the current state and the future of Bayesian data science in online marketing, based on many years of real-world experience leading teams at HelloFresh and Stitchfix. A special focus will be placed on Marketing Mix Models (MMM) and the work PyMC Labs has done with Luca during his time at HelloFresh. In addition, Customer Lifetime Value models play an ever more important role in marketing but remain stuck in old modeling approaches. We will discuss a potential way forward.

About the speakers

Thomas Wiecki
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.

Luca Fiaschi
Dr. Luca Fiaschi is the Chief Data & AI Officer at Mistplay the leading loyalty platform for gamers. Luca specializes in leading applied research and machine learning teams at disruptive tech companies where he has a track managing significant tech investments in supply chain, marketing and digital product. Prior to Mistplay he has been VP Data HelloFresh, VP Machine Learning at Stitch Fix, Director of AdTech at Rocket Internet. Luca holds a PhD at Heidelberg University in Machine Learning and Computer Vision.


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