On this week's episode of the podcast, I am joined by Michael Kaminsky, co-founder and co-CEO of Recast. We explore the rise of open-source Marketing Mix Modeling (MMM) tools and the challenges of measuring modern ad spend. Among other things, we discuss:

Whether open-source MMM libraries like Robyn and Meridian are truly unbiased tools or subtle instruments for big tech self-grading
How marketers can effectively evaluate the accuracy of MMM outputs through techniques like parameter recovery and predictive forecasting
Why smaller, performance-driven brands might actually find more value in last-touch attribution than in complex econometric modeling
If the inherent uncertainty in MMM estimates makes them fundamentally incompatible with the fast-paced feedback loops of digital advertising
What role generative AI and large language models play in democratizing access to sophisticated marketing data science analysis
When a brand should transition from simple attribution methods to a multi-layered approach involving incrementality and econometric modeling
How the shift toward CTV and non-trackable channels is forcing a resurgence in probabilistic measurement frameworks like MMM

Thanks to the sponsors of this week’s episode of the Mobile Dev Memo podcast:

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⁠Branch ⁠. Branch is an AI-powered MMP, connecting every paid, owned, and organic touchpoint so growth teams can see exactly where to put their dollars to bring users in the door and keep them coming back

Interested in sponsoring the Mobile Dev Memo podcast? Contact Mobile Dev Memo advertising.

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Season 7, Episode 26: Demystifying open source MMM (with Michael Kaminsky)

Mobile Dev Memo Podcast

Season 7, Episode 26: Demystifying open source MMM (with Michael Kaminsky)