Marketing Mix Modeling (MMM)
Statistical analysis to measure marketing channel effectiveness using aggregate data.
Definition
Marketing Mix Modeling (MMM) is a statistical technique that analyzes aggregate historical data to measure how different marketing channels and external factors impact business outcomes. Unlike attribution models that track individual user journeys, MMM uses regression analysis across channels to understand contribution.
MMM has resurged as privacy changes disrupt user-level tracking. It provides a privacy-safe way to measure channel effectiveness and optimize budget allocation using aggregate data that doesn't require cookies or individual consent.
Why It Matters
As individual-level tracking becomes unreliable, MMM offers a proven alternative for understanding marketing effectiveness. It measures channels that attribution models miss, like brand advertising, TV, and out-of-home.
For marketers navigating privacy changes, MMM complements attribution models to provide a complete picture of marketing impact.
Examples in Practice
A retail brand uses MMM to discover that their TV campaigns drive significant search lift that wasn't captured in digital attribution.
A CPG company applies MMM to optimize the balance between brand and performance marketing across a $100M annual budget.
A DTC brand uses MMM to finally measure the contribution of influencer partnerships that were invisible to last-click attribution.