Revenue-Optimal Dynamic Auctions for Adaptive Ad Sequencing
Digital publishers often use real-time auctions to allocate their advertising inventory. These auctions are designed with the assumption that advertising exposures within a user’s browsing or app-usage session are independent. Rafieian (2019) empirically documents the interdependence in the sequence of ads in mobile in-app advertising, and shows that dynamic sequencing of ads can improve the match between users and ads. In this paper, we examine the revenue gains from adopting a revenue-optimal dynamic auction to sequence ads. We propose a unified framework with two components – (1) a theoretical framework to derive the revenue-optimal dynamic auction that captures both advertisers’ strategic bidding and users’ ad response and app usage, and (2) an empirical framework that involves the structural estimation of advertisers’ click valuations as well as personalized estimation of users’ behavior using machine learning techniques. We apply our framework to large-scale data from the leading in-app ad-network of an Asian country. We document significant revenue gains from using the revenue-optimal dynamic auction compared to the revenue-optimal static auction. These gains stem from the improvement in the match between users and ads in the dynamic auction. The revenue-optimal dynamic auction also improves all key market outcomes, such as the total surplus, average advertisers’ surplus, and market concentration.