MMM Isn’t Causal: This is How Meta and Google Fix It

Editor's take

Meta and Google have identified that their prior measurement approaches, specifically Marketing Mix Models (MMM), lacked true causal inference. The core issue wasn't the statistical models themselves, but the experimental design used to gather the data.

This distinction is critical for advertisers. Without causal MMM, businesses like Meta and Google struggle to definitively prove how advertising spend directly impacts outcomes, leading to potentially inefficient budget allocation. This impacts marketers relying on these platforms to understand return on ad spend (ROAS) and optimize campaigns.

Future developments will focus on how Meta and Google implement these new experimental frameworks. The success of their proposed solutions will hinge on demonstrating measurable improvements in attribution accuracy and the ability to isolate the true impact of specific marketing channels, potentially shifting how digital advertising effectiveness is evaluated.