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| Date | Time | Room | Speaker | Affiliation | Synopsis | Paper |
|---|---|---|---|---|---|---|
| 9:00AM to 10:30AM | 4151 Grainger Hall | Omid Rafieian | University of Washington | See synopsis | Pending |
| 9:00AM to 10:30AM | 4151 Grainger Hall | Tesary Lin | University of Chicago | Pending | Pending |
| 9:00AM to 10:30AM | 4151 Grainger Hall | Matt McGranaghan | Cornell University | Pending | Pending |
| 9:00AM to 10:30AM | 4151 Grainger Hall | Dan Yavorsky | University of California-Los Angeles | Pending | Pending |
| 9:00AM to 10:30AM | 4151 Grainger Hall | Cheng He | Georgia Tech University | Pending | Pending |
| 9:00AM to 10:30AM | 4151 Grainger Hall | Unnati Narang | Texas A&M University | Pending | Pending |
Omid Rafieian, Doctoral Student, University of Washington
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Synopsis
Mobile in-app advertising has grown exponentially in the last few years. In-app ads are often shown in a sequence of short-lived exposures for the duration of a user’s stay in an app. The current state of both research and practice ignores the dynamics of ad sequencing and instead adopts a myopic framework to serve ads. In this paper, we propose a unified dynamic framework for adaptive ad sequencing that optimizes user engagement in the session, e.g., the number of clicks or length of stay. Our framework comprises of two components – (1) a Markov Decision Process
that captures the domain structure and incorporates inter-temporal trade-offs in ad interventions, and (2) an empirical framework that combines machine learning methods such as Extreme Gradient Boosting (XGBoost) with ideas from the causal inference literature to obtain counterfactual estimates of user behavior. We apply our framework to large-scale data from the leading in-app ad-network of an Asian country. We document significant gains in user engagement from adopting a dynamic framework. We show that our forward-looking ad sequencing policy outperforms all the existing methods by comparing it to a series of benchmark policies often used in research and practice. Further, we demonstrate that these gains are heterogeneous across sessions: adaptive forward-looking ad sequencing is most effective when users are new to the platform. Finally, we use a descriptive approach to explain the gains from adopting the dynamic framework.
Revenue-Optimal Dynamic Auctions for Adaptive Ad Sequencing
Synopsis
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.
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