Enhancing Position Auctions in Retail Media

Abstract

Retail media, a fast-growing channel for digital advertising, surpassed $55 billion of ad spend in 2024. A common retail media format involves position auctions, in which advertisers bid for higher placements on a retail platform’s product listing page. Advertiser bids are combined with a platform-set quality score to determine the allocation of sponsored slots and the resulting payments. Quality scores boost certain advertisers’ positions and reduce their per-click price. Unlike search engine advertising, retail media position auctions can monetize sales commissions as well as clicks. This paper develops a quality score approach to effectively balance these monetization options. To connect quality scores to retail revenues, the paper develops a structural model linking advertiser bids and revenues to the platform’s quality score choices coupled with a machine learning model of consumer behavior. These models are estimated using auction-advertiser level data from a quality score experiment conducted at a mid-size US based retail marketplace. Counterfactual simulations show that a quality score approach that balances clicks and commissions improves platform’s profits by 17% and advertiser surplus by 8% over click-based approaches typically used by retail platforms, leading to a win-win outcome for both.