Marketing Mix Modeling Is Back — and It's Eating Attribution's Lunch
A decades-old technique is suddenly the most trusted measurement method in marketing. Here's why MMM returned, and how it fits with what you already run.
Marketing mix modeling — a technique older than the banner ad — is having the comeback of the decade. The reason is blunt: the measurement method that replaced it for fifteen years stopped working. User-level attribution depended on following individuals across sites and devices, and privacy changes, browser restrictions, and walled gardens made that increasingly impossible. MMM never needed to track individuals at all, and eMarketer attributes its resurgence directly to mounting privacy regulation and accelerating signal loss.
From niche to near-default
MMM used to mean six-figure consulting engagements reserved for the largest advertisers. Two things changed. First, demand: in an eMarketer/TransUnion survey, 46.9% of US marketers said they plan to invest more in MMM over the next year, and 27.6% named it their single most reliable measurement method — ahead of multitouch attribution. Second, cost collapsed when the platforms open-sourced it: Google released Meridian to everyone in January 2025, a Bayesian model that uses only aggregated data, while Meta's Robyn offers a free open-source alternative.
It's not MMM versus attribution — it's triangulation
The 2026 consensus is a three-layer stack: MMM for strategic budget allocation, incrementality testing for causal validation, and platform data for day-to-day signal. Each layer answers a different question. MMM tells you where to put the next dollar. Incrementality tells you if that dollar caused anything. Platform data tells you what happened overnight. Used together, they form an account of what your spend actually did.