Media mix modeling is back — but is your model measuring causation or just spend?

Media mix modeling is back — but is your model measuring causation or just spend?

MMM (regressing aggregate outcomes like sales on aggregate marketing inputs over time) is enjoying a revival because it needs no user-level tracking. The revival comes with a quiet correlation trap.

How it works

You fit a model — historically linear regression, now often Bayesian (Google's Meridian, Meta's Robyn) — that attributes sales to channels while controlling for price, seasonality, and macro factors. Two refinements matter: adstock (advertising effects decay over time, not instantly) and saturation curves (diminishing returns at higher spend).

The core danger

MMM is observational. If you always raise TV spend in Q4 when demand is already high, the model happily credits TV for seasonal sales it didn't cause. This is textbook confounding: spend correlates with the very demand it's supposed to explain.

The nuance

Bayesian MMM doesn't fix this by itself — priors and regularization reduce overfitting but cannot manufacture causal identification out of correlated regressors. Garbage collinearity in, confident-looking ROI out.

What to actually do

— Inject experimental priors: feed incrementality-test results into the model as informative priors on channel effects. This is the single biggest credibility upgrade.
— Deliberately vary spend (flighting, regional pulses) to create the variation the model needs to identify effects.
— Report credible intervals, not point ROAS. A channel whose interval spans zero is not a finding.

Bottom line for practitioners: MMM's value scales with how much real experimental variation you feed it. Without calibration, it's an elegant way to launder seasonality into channel credit.