What is Marketing Mix Modeling?
Marketing mix modeling (MMM) is a statistical method that uses historical sales and marketing data to estimate how much each marketing activity contributed to sales.
Why does Marketing Mix Modeling matter for marketers?
It measures from aggregate data, so it does not need individual-level tracking. That makes it a natural complement to attribution while tracking is restricted.
Where does Marketing Mix Modeling stand today?
Google released Meridian, an open-source marketing mix model, in limited availability on March 7, 2024 and announced wide availability on GitHub on January 29, 2025, with a partner program of certified measurement partners. Other open-source tools such as PyMC-Marketing and LightweightMMM have made Bayesian approaches more accessible.
Common use cases
- Estimating the return on each channel over time
- Scenario planning and budget optimization
- Calibrating other measurement with experiments
An MMM applies regression to historical time series. It splits total sales into base sales, which would happen anyway, and incremental sales driven by marketing, and it estimates the contribution and return of each element, including price and promotion. The approach accounts for advertising carryover and diminishing returns and supports what-if planning.
What Google’s Meridian adds
Google says Meridian is open source, so teams can see and change the code and parameters. Its features include calibration with incrementality experiments, the use of reach and frequency data, guidance on measuring search and inputs of indexed query volume. Google itself says that MMMs today are not perfect but are evolving.
Known limitations
Wikipedia lists a bias toward short-term effects over brand building, a bias toward time-specific media such as television, difficulty with new products that have little history, and aggregation bias when comparing tactics that target different groups.