用Transformer统一建模用户行为，精准分配营销转化功劳
LiDDA: Data Driven Attribution at LinkedIn
- 基于Transformer建模用户全路径行为，融合个体与群体数据
- 在LinkedIn大规模落地，显著提升转化归因准确性
- 适合广告平台、营销分析及数据驱动决策团队参考
数据驱动的归因通过从数据中学习因果模式，将转化功劳分配给各类营销互动，是现代营销智能的基础，对营销业务和广告平台至关重要。本文介绍一种统一的基于Transformer的归因方法，可处理成员级数据、聚合级数据以及外部宏观因素的整合。我们详述了该方法在LinkedIn的大规模实现，并展示了显著的影响。同时分享了具有广泛适用性的经验与洞察，适用于营销与广告技术领域。
原文摘要 · Abstract (English)
Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundation of modern marketing intelligence and vital to any marketing business and advertising platform. In this paper, we introduce a unified transformer-based attribution approach that can handle member-level data, aggregate-level data, and integration of external macro factors. We detail the large scale implementation of the approach at LinkedIn, showcasing significant impact. We also share learnings and insights which are broadly applicable to the marketing and ad tech fields.
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