Media mix modeling (MMM) is a statistical method that uses historical data to quantify how each marketing channel contributes to sales, and to recommend optimal budget allocation across channels.
MMM regresses sales (or another business outcome) against marketing investment by channel and against external variables such as seasonality, pricing, distribution, and macroeconomic factors. Unlike attribution models, MMM does not require user-level tracking — making it resilient to cookie deprecation, app tracking restrictions, and walled-garden data limitations. Modern MMM has shifted from annual offline studies to ongoing models that refresh weekly or monthly. Google’s Meridian and Meta’s Robyn are open-source frameworks accelerating MMM adoption among mid-market advertisers who previously could not afford traditional consultancy-led models.
Coegi runs MMM for clients with sufficient media spend and historical data, typically using open-source frameworks adapted to client-specific business drivers. MMM outputs are integrated with attribution and incrementality findings to produce a unified view of marketing effectiveness.
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