MMM is a statistical approach that estimates each channel's contribution to revenue from aggregate historical data — spend, sales, seasonality, external factors — without tracking any individual user.
Definition
MMM regresses outcomes against channel spend and control variables over months or years of data, producing contribution and saturation estimates per channel: how much each drove, and where returns diminish. Once an enterprise-TV-era tool, it's been revived for digital because it's privacy-proof — no cookies, no user-level tracking — and open-source frameworks lowered the barrier for mid-size advertisers.
Why it matters
MMM answers the questions attribution can't: what's the marginal return of the next dollar per channel, and where does saturation start. Its limits are real — it's directional, needs spend variation to learn from, and won't judge one campaign against another — but as the top-level allocation compass it's the strongest privacy-era option.
In agentic execution
MMM outputs are allocation guidance; someone still has to move the money. Adlyse's agents execute the reallocation the model and the team agree on — across platforms, with approval — closing the loop between measurement and action.
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