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arXiv · 2505.09861

LiDDA: Data Driven Attribution at LinkedIn

John Bencina·Erkut Aykutlug·Yue Chen·Zerui Zhang·Stephanie Sorenson·Shao Tang·Changshuai Wei
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Abstract

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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John Bencina, Erkut Aykutlug, Yue Chen, Zerui Zhang, Stephanie Sorenson, Shao Tang, Changshuai Wei. 2025-05-14. LiDDA: Data Driven Attribution at LinkedIn. https://arxiv.org/abs/2505.09861

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