LiDDA: Data Driven Attribution at LinkedIn
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916054178988032 |
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| author | Bencina, John Aykutlug, Erkut Chen, Yue Zhang, Zerui Sorenson, Stephanie Tang, Shao Wei, Changshuai |
| author_facet | Bencina, John Aykutlug, Erkut Chen, Yue Zhang, Zerui Sorenson, Stephanie Tang, Shao Wei, Changshuai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiDDA: Data Driven Attribution at LinkedIn Bencina, John Aykutlug, Erkut Chen, Yue Zhang, Zerui Sorenson, Stephanie Tang, Shao Wei, Changshuai Machine Learning Artificial Intelligence Information Retrieval Methodology 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. |
| title | LiDDA: Data Driven Attribution at LinkedIn |
| topic | Machine Learning Artificial Intelligence Information Retrieval Methodology |
| url | https://arxiv.org/abs/2505.09861 |