Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM
For retail marketers, IMA provides a unified, privacy-safe attribution method that bridges the gap between coarse MMM and granular MTA, enabling campaign optimization without user-level tracking.
The paper proposes Integrated Marketing Attribution (IMA), a Bayesian framework that combines Marketing Mix Modeling (MMM) with channel-specific attribution models to deliver granular, privacy-safe campaign-level insights while maintaining consistency with MMM. The approach addresses the fragmentation between MMM and Multi-Touch Attribution (MTA) under increasing privacy restrictions.
Retail marketing measurement increasingly requires granular campaign-level insights without relying on user-level tracking. However, the two dominant approaches, Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA), often produce fragmented insights. MMM is privacy-safe and robust for channel-level planning but is too coarse for campaign optimization, while MTA provides granular attribution but has become less reliable under increasing privacy restrictions. We propose Integrated Marketing Attribution (IMA), a unified framework that combines MMM with channel specific Bayesian attribution models to derive campaign-level effects from aggregated data. By leveraging MMM-informed priors, IMA delivers granular, privacy-safe attribution while preserving consistency with MMM.