融合MMM与贝叶斯模型，实现无用户追踪的精准广告效果归因。
Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM
- 用MMM提供的先验信息指导贝叶斯模型，实现细粒度归因。
- 在不依赖用户追踪的前提下，获得接近MTA的颗粒度效果评估。
- 适合关注隐私合规且需优化具体营销活动的广告主。
零售营销评估日益需要不依赖用户级追踪的细粒度活动级洞察。然而，主流方法营销组合建模（MMM）和多触点归因（MTA）常产生碎片化结果：MMM虽隐私安全、适用于渠道级规划，但颗粒度不足；而MTA虽可提供细粒度归因，但在隐私限制加剧下可靠性下降。本文提出集成营销归因（IMA），将MMM与特定渠道的贝叶斯归因模型结合，从聚合数据中推导出活动级效应。通过利用MMM提供的先验信息，IMA在保障隐私安全的同时，实现细粒度归因，并保持与MMM结果的一致性。
原文摘要 · Abstract (English)
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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