LiDDA: Data Driven Attribution at LinkedIn
This work addresses attribution modeling for marketing intelligence at LinkedIn, providing a scalable solution with broad applicability to marketing and ad tech fields, though it appears incremental as it builds on existing transformer methods.
The paper tackles the problem of assigning conversion credits to marketing interactions by introducing a unified transformer-based attribution approach that handles member-level and aggregate-level data, as well as external macro factors, and demonstrates significant impact through large-scale implementation at LinkedIn.
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 businesses 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 learning and insights that are broadly applicable to the marketing and ad tech fields.