#126 MMM, CLV & Bayesian Marketing Analytics, with Will Dean

Feb 19, 2025

Join Will Dean, a statistician and data scientist at PyMC Labs, as he dives into the world of Bayesian marketing analytics. He shares insights on leveraging customer lifetime value (CLV) and media mix modeling to optimize marketing strategies. The discussion highlights the significance of productionizing models and the challenges that come with it, including version control and model management. With a focus on open-source collaboration, Will emphasizes the importance of continuous learning and innovative approaches to empower marketers with actionable data.

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INSIGHT

Media Mix Modeling

Media Mix Modeling (MMM) helps analyze marketing spend allocation over time, considering saturation and ad stock effects.
It's a time series regression model with custom inverse link functions to capture non-linearities.

ADVICE

Productionizing PyMC Models

Productionizing PyMC models involves storing model artifacts and reconstructing the model graph.
This allows versioning and code changes while preserving the model's state for later use.

INSIGHT

Customer Lifetime Value Models

Customer Lifetime Value (CLV) models analyze customer behavior, including purchase frequency, spending, and churn prediction.
They help understand customer profiles and predict future behavior based on various factors like subscriptions.

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Takeaways:

Marketing analytics is crucial for understanding customer behavior.
PyMC Marketing offers tools for customer lifetime value analysis.
Media mix modeling helps allocate marketing spend effectively.
Customer Lifetime Value (CLV) models are essential for understanding long-term customer behavior.
Productionizing models is essential for real-world applications.
Productionizing models involves challenges like model artifact storage and version control.
MLflow integration enhances model tracking and management.
The open-source community fosters collaboration and innovation.
Understanding time series is vital in marketing analytics.
Continuous learning is key in the evolving field of data science.

Chapters:

00:00 Introduction to Will Dean and His Work

10:48 Diving into PyMC Marketing

17:10 Understanding Media Mix Modeling

25:54 Challenges in Productionizing Models

35:27 Exploring Customer Lifetime Value Models

44:10 Learning and Development in Data Science

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz,...