Abstract
Marketing Mix Models (MMMs) are essential tools for determining the effectiveness of various marketing activities, but their reliance on aggregated data can limit causal insights. Incrementality tests, on the other hand, offer robust causal evidence at a granular level. This paper proposes a systematic approach to calibrating MMMs using incrementality test results to enhance predictive accuracy and decision-making. We also emphasize the evolving importance of MMMs in the context of heightened privacy regulations and changes such as Apple’s App Tracking Transparency (ATT) framework. We outline a step-by-step methodology, discuss potential challenges, and provide a case study to demonstrate the practical application of this integration. Keywords: MMM, ATT, GDPR, Randomized Control Trials, Bayesian Priors
Published Version
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