Related papers: LiDDA: Data Driven Attribution at LinkedIn

LiDDA: Data Driven Attribution at LinkedIn

URL: http://arxiv.org/abs/2505.09861v2
Date: Wed, 21 May 2025 22:19:59 GMT
Title: LiDDA: Data Driven Attribution at LinkedIn
Authors: John Bencina, Erkut Aykutlug, Yue Chen, Zerui Zhang, Stephanie Sorenson, Shao Tang, Changshuai Wei,
Abstract summary: We introduce a unified transformer-based attribution approach that can handle member-level data, aggregate-level data, and integration of external macro factors.<n>We detail the large scale implementation of the approach at LinkedIn, showcasing significant impact.
Score: 6.198783492309074
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 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.

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