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.

翻译：零售营销测量日益需要在不依赖用户级追踪的情况下获得细粒度的广告活动层面洞察。然而，两种主流方法——营销组合模型（MMM）和多触点归因（MTA）——常常产生碎片化的洞察。MMM在渠道级规划中具有隐私安全性和鲁棒性，但对于广告活动优化而言过于粗糙；而MTA提供粒度归因，但在日益严格的隐私限制下可靠性下降。我们提出集成营销归因（IMA），这是一个统一框架，将MMM与特定渠道的贝叶斯归因模型相结合，从聚合数据中推导出广告活动层面的效果。通过利用MMM提供的先验信息，IMA在保持与MMM一致性的同时，提供粒度化、隐私安全的归因。