What is marketing mix modeling?
Marketing mix modeling (MMM) is a statistical method that uses aggregate data, such as weekly spend by channel and total sales, to estimate how much each marketing channel and outside factor contributes to results.
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How marketing mix modeling works
An MMM looks at a long history of spend, sales, and other drivers like pricing, promotions, and seasonality. It finds the relationship between changes in each input and changes in sales.
Models often include adstock, to capture delayed effects, and saturation curves, to capture shrinking returns. The output can show each channel's contribution and suggest how to shift budget.
Strengths and limits
MMM works with aggregate data and doesn't need user-level tracking, so it fits privacy limits and covers offline channels such as TV and radio. It's useful for planning budgets across channels.
It needs enough history and variation in spend to produce stable estimates, and the results are only as good as the model's assumptions. Many teams check MMM findings against incrementality tests.
Common questions
What is the difference between MMM and MTA?
MMM uses aggregate spend and sales data across all channels, including offline ones. Multi-touch attribution uses user-level paths and covers only tracked digital touchpoints.
How much data does MMM need?
It needs a long history with varying spend, commonly two years or more of weekly data. The more your spend varies, the easier it is for the model to separate each channel's effect.
How often should you update an MMM?
Many teams refresh models on a regular schedule, such as quarterly, and when something major changes. Stale models can drift from current reality.
Is MMM accurate?
It can be useful but it produces estimates, not proof. Validate its findings with experiments, especially before large budget moves.
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