Optimization-Based Budget Pacing in eBay Sponsored Search
Name
3589335.3648331.pdf
Size
1.34 MB
Format
Adobe PDF
Checksum (MD5)
6ac2ab1ac66287b4c517f312a33d9e67
Author(s)
Chen, Qinyi
Nguyen, Phuong Ha
Gligorijevic, Djordje
Date Issued
May 13, 2024
Publisher
ACM
Citation
Chen, Qinyi, Nguyen, Phuong Ha and Gligorijevic, Djordje. 2024. "Optimization-Based Budget Pacing in eBay Sponsored Search."
Version
Final published version
Abstract
In online platforms like eBay, sponsored search advertising has become instrumental for businesses aiming for enhanced visibility. However, in automated ad auctions, the sellers (ad campaigns) run the risk of exhausting their budgets prematurely in the absence of proper pacing strategies. In response to this, online platforms have been prompted to employ budget pacing strategies to maintain consistent spending patterns for their sellers. While numerous budget pacing strategies have been introduced, they predominantly stem from either empirical or theoretical perspectives, often functioning in isolation. This paper aims to bridge this gap by investigating the performance of a theoretically inspired optimization-based bid shading method, AdaptivePacing, within eBay's sponsored search environment and proposing variants of the algorithm tailored to real-world environments. Our findings highlight the benefits of applying theoretical pacing approaches in practical contexts. Specifically, the optimization-based AdaptivePacing method offers the platform flexible control over campaign spending patterns, accounts for business constraints, and suggests tailored strategies for distinct advertisers. Furthermore, when evaluating AdaptivePacing alongside established empirical methods, we demonstrate its practical effectiveness and pinpoint areas for further refinement.
Description
WWW '24: Companion Proceedings of the ACM on Web Conference May 13–17, 2024, Singapore, Singapore
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3589335.3648331
