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arXiv · 2606.16878

Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM

Meghana R. Bhat·Ankit Umare·Utsav Aggarwal·Richard Vecsler·Arunkumar Mani·Karthik Nair·Chandhu Nair
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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.

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Meghana R. Bhat, Ankit Umare, Utsav Aggarwal, Richard Vecsler, Arunkumar Mani, Karthik Nair, Chandhu Nair. 2026-06-15. Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM. https://arxiv.org/abs/2606.16878

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