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How Wrong Is Your Marketing Mix Model (MMM)?

L4 · DeveloperResearchTowards Data Science· 10/7/2026

Deep technical analysis of MMM validation challenges and simulation-based testing approaches that matter for engineers building marketing attribution systems.

AI Summary

New simulation research evaluates budget phasing algorithms that address three core problems in Marketing Mix Modeling: multi-collinearity, selection bias, and non-identifiable adstock/saturation effects.

Excerpt

A well-built MMM can still be wrong, because the spend it learns from never held the information it needs. This article tests whether changing when you spend, not how much, can fix that. The post How Wrong Is Your Marketing Mix Model (MMM)? appeared first on Towards Data Science.

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