MMM uses regression to decompose total sales into the contribution of each marketing channel, baseline demand, and external factors — so you can see what actually drove results.

Readiness

Marketing Mix Modeling (MMM) is a top-down statistical technique that uses aggregate time-series data — weekly spend, impressions, sales, seasonality, promotions — to estimate the contribution of each marketing input to business outcomes.

The Core Equation
At its heart, every MMM fits a regression of this form:
Sales = Baseline + (Media × Efficiency) + External Factors

Baseline represents demand that would exist without any marketing — brand equity, organic traffic, seasonal patterns, and pricing effects. Media terms capture the incremental contribution of each channel (TV, social, search, email, etc.), typically transformed through saturation curves to model diminishing returns.

Key Assumption
MMM assumes the past is a reasonable guide to the future. If your media mix, creative, or competitive landscape changes dramatically, historical coefficients may not hold. Always calibrate MMM outputs with incrementality experiments.

Each bar shows a channel’s incremental contribution, stacking left to right from baseline to total sales.

Incremental revenue generated by channel spend. Each channel is clearly labeled where curves diverge.

Average vs Marginal ROI
Average ROI divides total revenue by total spend. Marginal ROI (mROI) measures the return on the next dollar spent. A channel can have a high average ROI but a low marginal ROI if it is heavily saturated.

● mROI ≥ 0.5 (efficient)    ● mROI < 0.5 (saturated)