想在不碰用户隐私的前提下搞清楚每个广告战役的效果？这篇论文的IMA框架把MMM和贝叶斯归因揉在一起，给出细粒度结果还不出格，做营销分析的可以看看。
该论文提出集成营销归因（IMA）框架，将营销组合模型（MMM）与渠道特定贝叶斯归因模型结合。IMA利用MMM先验信息，从聚合数据中推导出战役级效果，实现了隐私安全且细粒度的归因。与传统MMM的粗粒度优化和MTA在隐私限制下不可靠的问题相比，IMA在保持与MMM一致性的同时提供更精细的洞察。该方法在零售营销场景中可有效替代依赖用户级追踪的方案。
Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM
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.