Media Mix Modeling existed long before digital advertising. CPG companies used it in the 1960s to measure TV and print effectiveness. Then digital attribution came along, and everyone forgot about MMM. Now, as user-level tracking dies, MMM is back—and more relevant than ever.
What Is Media Mix Modeling?
MMM uses statistical analysis to measure how different marketing inputs (spend by channel) affect business outputs (revenue, installs, etc.). It doesn't require user-level tracking—only aggregate data.
How It Works
MMM analyzes historical data to build regression models that estimate:
- Contribution of each channel to outcomes
- Saturation curves (diminishing returns)
- Lag effects (delayed impact of spend)
- Synergies between channels
MMM vs Attribution
- Attribution: User-level, real-time, requires tracking
- MMM: Aggregate, retrospective, privacy-safe
Complementary, Not Competitive
Best practice is using both. Attribution for daily optimization. MMM for strategic allocation. Incrementality for validation. The "triangulation" approach catches blind spots in any single method.
MMM Limitations
- Requires historical data (minimum 1 year, ideally 2-3)
- Can't provide real-time optimization signals
- Struggle with new channels lacking data
- Require expertise to build and interpret
Modern MMM Tools
Several options for implementing MMM:
- Meta Robyn: Open-source, built by Meta
- Google LightweightMMM: Open-source, Bayesian
- Commercial vendors: Nielsen, IRI, Analytic Partners
Unified Measurement
ClicksFlyer integrates with MMM outputs alongside attribution data, helping you see the complete picture of marketing effectiveness across methodologies.