What is media mix modeling?

A statistical model that estimates how much each channel contributes to sales over time.

Media mix modeling, or MMM, is a statistical model that estimates how much each channel contributes to sales over time. It uses years of weekly spend and sales data, plus things like price, season and distribution.

Fed well, it can see channels that click-based attribution misses, like TV, outdoor and retail. It also needs a lot of clean history, careful setup and regular checks against real tests, or it starts producing confident numbers nobody can verify. For a brand team without analysts, the model often becomes a black box that an outside vendor reads for you.

Why it matters

It needs a lot of clean history to work, and a data team to keep it honest.

An example

Example (made-up numbers): a model says TV drove 15% of last year’s sales. A test that turned TV off in two markets suggests something closer to 5%. The test is the stronger evidence, and the model should be adjusted to match it.

Where it misleads

Treating model output as measured fact. It’s an estimate, and only as good as the history behind it and the tests used to check it.

How Arcus handles it

We don’t lead with a model. We start from your own numbers, count each result once, and test for cause by running ads in some regions and not others. If you already have an MMM, we line its estimates up with your own numbers and any test results, and show where they disagree.

More in our guide: Do you need a data team? A checklist