Marketing Mix Modelling.

Marketing Mix Modelling (MMM) is a statistical methodology that uses econometric regression analysis on aggregate, time-series data — ad spend by channel, sales volume, seasonality, pricing, distribution, and external factors — to estimate the contribution of each marketing channel to sales or revenue. MMM does not require individual user tracking, making it privacy-safe and immune to cookie deprecation.

MMM vs. Multi-Touch Attribution

Multi-touch attribution (MTA) tracks individual user journeys across touchpoints and assigns fractional credit to each interaction. MMM operates on aggregated data and statistical inference — it doesn't track any individual. MTA is more granular and real-time but is degrading with cookie loss; MMM is more privacy-forward and provides strategic, channel-level insight rather than individual-level optimisation.

Running an MMM

MMM requires 2-3 years of weekly marketing spend and sales data, external factors (seasonality, economic conditions, competitor promotions), and statistical expertise. Traditional MMM was outsourced to consulting firms on annual cycles. Modern MMM tools (Meta's Robyn, Google's Meridian, Lightweight MMM from Google, and commercial platforms like Northbeam, Rockerbox) make the methodology more accessible with faster iteration cycles.

MMM Output

MMM produces: return on ad spend (ROAS) by channel, budget optimisation recommendations (how to reallocate spend to maximise revenue), saturation curves (at what spend level does each channel see diminishing returns), and adstock curves (how long the effect of an ad continues after it runs). These outputs inform annual budget planning and channel mix strategy.