Foundational Models for MMM: Interesting, but Built on a Stable Base?

Amazon Ads recently published an intriguing whitepaper proposing a “foundational model” approach to Marketing Mix Modeling (MMM), analogous to how large language models like GPT learn generalizable patterns across many data sources.

It’s an appealing idea: a shared, privacy-safe model that learns from many brands, reducing noise and cost while improving consistency. But I can’t help wondering whether this foundation may rest on shifting ground.

MMMs are, by nature, models. They depend heavily on the assumptions, priors, and data chosen by their modelers. A “foundational” MMM trained on hundreds of brand-level models risks compounding those assumptions rather than correcting them. And when synthetic data enters the mix, the system depends further on abstractions. (See our recent blog post on the risk of AI designing ad optimization on non-experimental assumptions of causality.)

It brings to mind Nassim Taleb's cautions about systems built on interconnected dependencies createing fragility, e.g., financial models leading up to the 2008 mortgage crisis. When many players adopt the same flawed premise, the system becomes brittle.

Aside from one passing reference, what's missing from the whitepaper is a discussion of validation through high-quality experiments. Randomized controlled trials (RCTs) remain the best evidence for causal advertising impact. In the hierarchy of evidence, only meta-analyses of many RCTs ranks higher. The industry should focus on building toward benchmarks of incrementality RCTs to calibrate and test any MMM foundation, preventing models from recursively learning from other models.

And it’s worth remembering who is proposing this. Amazon is now in the MMM business, joining Google’s Meridian and Meta’s Robyn. As I argued in The First Principle of Honest Advertising Measurement Is Independence from the Media, credible measurement depends on independence. It’s hard not to be cautious when the companies selling most of the media are also building the tools to “prove” its ROI.

Foundational MMMs could be a leap forward, but only if their footing is grounded in experiments, not self-reference. Don't build sand castles on the beach at low tide.

(Join the discussion of this essay on LinkedIn.)