MMM (Media Mix Modeling)
Statistical modeling of marketing impact across channels using historical data.
Media Mix Modeling (MMM) is a statistical technique that uses historical spend, impression, and revenue data to estimate the incremental impact of each marketing channel. MMM is attribution-independent — it doesn't rely on click tracking — and is robust to privacy changes.
Context
MMM is the historical industry standard for large brands (P&G, Coca-Cola, Unilever). It lost favor in the 2010s as digital attribution grew, then came back starting around 2020 as privacy changes (iOS 14+, cookie deprecation) degraded click-based attribution.
Modern MMM tools (Meta's Robyn, Google's Meridian, commercial offerings like Rockerbox and OptiMine) make the technique accessible to mid-market brands with $10M+ annual media spend. Below that spend, data is usually too sparse for reliable MMM.
A DTC brand running $3M/year on paid media discovered via MMM that their reported TV-attribution-free ROAS of 4.2x was actually incremental to only 1.8x — the 2.4x gap was base demand Meta and Google were credit-taking for.
MMM requires 2+ years of clean historical data to produce reliable outputs. New brands and brands that have drastically shifted spend patterns can't run MMM until enough data accumulates.
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