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Marketing Mix Modeling (MMM)

Marketing mix modeling uses aggregated time series data to estimate how marketing channels contribute to outcomes (for example revenue) when user-level tracking is limited.

Updated 2026-01-24

Definition

Marketing mix modeling uses aggregated time series data to estimate how marketing channels contribute to outcomes (for example revenue) when user-level tracking is limited.

How to use it

  • Useful for privacy-constrained environments and for long time windows.
  • MMM needs clean historical data, consistent spend records, and controls for seasonality.

Common mistakes

  • Treating MMM coefficients as precise at short horizons (they are noisy).
  • Ignoring creative and offer shifts that change channel effectiveness.

Why this matters

This term matters because it affects how you interpret performance and make budget decisions. If you use inconsistent definitions or windows, ROAS/CPA can look "better" while profit gets worse.

Practical checklist

  • Write a 1-line definition for "Marketing Mix Modeling (MMM)" that your team will use consistently.
  • Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
  • Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
  • Sanity-check with a related calculator from the same category on MetricKit.
  • Read the related guide (e.g., Attribution vs incrementality: what to trust, when, and how to test) for context and common pitfalls.

Where to use this on MetricKit

Calculators

Guides