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无需依赖用户级跟踪的前提下，获取细粒度的活动级洞察。然而，两种主流方法——营销组合建模（MMM）与多触点归因（MTA）——往往产生碎片化的见解。MMM在渠道级规划中具有隐私安全性和稳健性，但过于粗粒度而无法用于活动优化；MTA虽能提供细粒度归因，但在日益严格的隐私限制下可靠性下降。我们提出整合营销归因（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，从聚合数据中推导活动级效应。通过利用MMM启发的先验信息，IMA在保持与MMM一致性的同时，实现了细粒度且隐私安全的归因。