This year the Wisconsin School of Business' Marketing Department is inviting doctoral candidates to come and present their research to our school. 

Optimizing User Engagement through Adaptive Ad Sequencing 

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.

Tesary Lin, Doctoral Student, University of Chicago

Valuing Intrinsic and Instrumental Preferences for Privacy

Synopsis

In this paper, I propose a framework for understanding why and to what extent people value their privacy. In particular, I distinguish between two motives for protecting privacy: the intrinsic motive, that is, a “taste” for privacy; and the instrumental motive, which reflects the expected economic loss from revealing one’s “type” specific to the transactional environment. Distinguishing between the two preference components not only improves the measurement of privacy preferences across contexts, but also plays a crucial role in developing inferences based on data voluntarily shared by consumers. Combining a two-stage experiment and a structural model, I measure the dollar value of revealed preference corresponding to each motive, and examine how these two motives codetermine the composition of consumers choosing to protect their personal data. The compositional differences between consumers who withhold and who share their data strongly influence the quality of firms’ inference on consumers and their subsequent managerial decisions. Counterfactual analysis investigates strategies firms can adopt to improve their inference: Ex ante, firms can allocate resources to collect personal data where their marginal value is the highest. Ex post, a consumer’s data-sharing decision per se contains information that reflects how consumers self-select into data sharing, and improves aggregate-level managerial decisions. Firms can leverage this information instead of imposing arbitrary assumptions on consumers not in their dataset.