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This paper addresses the challenge of granular marketing measurement in privacy-constrained environments by unifying Marketing Mix Modeling (MMM) with Bayesian attribution techniques. The proposed Integrated Marketing Attribution framework leverages aggregated data to derive campaign-level insights without relying on user tracking, directly applying machine learning principles to solve a specific industry problem. While the application domain is retail analytics rather than general-purpose AI or quantum systems, the core methodology relies heavily on advanced statistical modeling and probabilistic inference central to modern machine learning research.
Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM
arXiv:2606.16878v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG)
arXiv:2606.16878 [cs.LG]
(or arXiv:2606.16878v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.16878 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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