OPTIMIZATION-BASED BUDGET PACING IN EBAY SPONSORED SEARCH

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 propose 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.