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English What is Marketing Mix Modeling?
Marketing Mix Modeling, or MMM, is a statistical method that calculates how much each media channel contributes to your revenue. It includes TV and radio, with no dependence on cookies.
Marketing Mix Modeling (MMM) is an econometric method that analyses historical data on media spend, revenue and external factors to determine the incremental contribution of each channel to revenue.
How does Marketing Mix Modeling work?
MMM analyses the statistical relationship between your media spend and your revenue over a long period. The model does not look at individual clicks or cookies, but at patterns: if your TV budget increases, what happens to revenue a few weeks later?
A good MMM model corrects for three effects that simple analyses miss. Adstock describes how a campaign continues to work after it has ended. Saturation describes the point at which additional budget yields diminishing returns, see also the saturation curve. External factors such as season, price and weather are weighted separately so they are not incorrectly attributed to media.
The result is a distribution of your revenue across all channels, with a confidence interval per channel. You see not only what a channel delivered, but also how certain that figure is.
What MMM delivers
MMM answers the questions that last-click and platform reports cannot.
Online and offline in one model
TV, radio, print and digital channels are included in the same analysis. You compare like with like.
Incremental contribution per channel
You see which revenue would not have existed without a channel, not just which conversions passed through it.
Evidence-based budget allocation
You allocate budget based on actual contribution and saturation, not on intuition or platform claims.
No cookie dependence
MMM works on aggregated data and remains reliable as third-party tracking disappears.
When does MMM make sense?
MMM works best for organisations with a significant media budget spread across multiple channels and at least one year of historical data. The more variation there is in your spend, the more precisely the model can isolate effects.
For short-term campaign signals, you complement MMM with first-party attribution. The combination gives you both the strategic picture across all channels and rapid feedback per campaign.
Frequently asked questions
What is the difference between MMM and attribution?
Attribution tracks individual conversion paths, usually within digital channels. MMM analyses aggregated data across all channels, including offline, and isolates the incremental contribution. MMM looks at the whole, attribution looks at the path.
How much data do I need for MMM?
Ideally at least two to three years of weekly data on media spend, revenue and external factors. With one year a first model is possible, but more history and more variation in spend make the model more accurate.
Is MMM a black box?
It does not have to be. A transparent MMM model shows estimated parameters, confidence intervals and model fit per channel. Datafy shows that supporting evidence so you can evaluate the outcome rather than simply accept it.
Does MMM work without cookies?
Yes. MMM uses aggregated spend and revenue data and is therefore not dependent on third-party cookies or user-level tracking. It continues to work as those data sources disappear.
See MMM working on your own data.
Book a demo and see how Datafy calculates the contribution of each channel, including TV and radio.