Analytics
Marketing Mix Modeling (MMM) for Beginners
MMM estimates what each channel contributes using spend and sales over time, without tracking individuals. What it needs, what it tells you, and when it is worth doing.
BenchMarketing editorial team Updated October 2, 2026 3 min readShare
Marketing mix modelling is a statistical method that estimates how much each marketing channel contributes to sales, using aggregate data: weekly spend by channel, weekly sales, and the other things that move sales such as price, promotions and seasonality. It does not need cookies or user-level tracking, which is why interest in it has grown as tracking has become harder.
What it tells you
- How much each channel contributed to sales over the period.
- Diminishing returns: how the effect of each extra dollar shrinks as spend rises.
- Carryover: how long a channel's effect lasts after the spend, which matters for TV, video and brand campaigns.
- A suggested budget split that would have produced more sales for the same spend.
What it needs
- Two years of weekly data is a common starting point, so the model can separate seasonality from marketing.
- Variation in spend. If every channel's budget moved together, the model cannot tell them apart. Deliberate changes in spend help.
- The other drivers. Price changes, promotions, distribution, competitor activity and seasonality all need to be in the model, or their effects get credited to marketing.
Tools
Open-source options have made MMM accessible without a large analytics team. Google's Meridian and Meta's Robyn are both free, well documented and widely used. Both need someone comfortable with statistics to set up and check.
How to use the results
Treat MMM as one input, not a final answer. Its estimates come with uncertainty ranges, and some channels will have wide ones. The strongest setup combines three things:
- MMM for the overall budget split.
- Incrementality tests to check the model's biggest claims.
- Platform data for day-to-day optimisation inside each channel.
When it is worth it
MMM is most useful once you spend across several channels and at a scale where shifting 10% of budget matters, including offline channels that click-based attribution cannot see. For a small account spending on one or two platforms, simpler methods (blended efficiency and holdout tests) answer the same questions with less effort.
Try a simplified version with the media mix modeler, and read about full-funnel attribution.
About the figures
Benchmark figures in this article come from the BenchMarketing dataset and update when the benchmark pages do. Each benchmark page lists its sources and period; see our methodology.
Sources
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