Platforms increasingly deploy algorithms that provide information to both sides of a market, yet the value of this information may be unevenly distributed. We study this asymmetry in the context of Zillow, a leading online real estate platform that publishes “Zestimates,” algorithmic estimates of home values, for over 110 million U.S. residential properties. We leverage an exogenous negative shock to trust in the Zestimate that reduced reliance on the algorithm among both buyers and sellers. We develop a simple framework that captures how algorithmic information shapes the beliefs and pricing strategies of buyers and sellers, and test its predictions using secondary data on 27,908 properties listed for sale. We find that following the shock, sellers move away from the Zestimate, setting list prices that deviate more from the algorithmic estimate. The effect is robust to controls for housing composition and observed local market conditions and is not driven by contemporaneous changes in Zestimate values. Although we cannot rule out some delayed adjustment of the Zestimate to local market conditions, our analyses suggest that it is unlikely to fully explain the results. Despite higher list prices, homes sell faster and at higher prices. The shorter time on market is consistent with the Zestimate being more informative to buyers than to sellers, such that the negative shock disproportionately increases buyer uncertainty. These findings highlight how asymmetries in the value of algorithmic information can shape market outcomes, with implications for predicting market responses and designing platform policies.
Most users of educational technology disengage within weeks of joining, and whether temporary incentives can durably change engagement remains unclear. We study contest-based gamification in a randomized controlled trial with 10,000 students on an English-learning app in India. Treated students entered a reading contest with leaderboard rankings and prizes for top performers. During the contest, treated students completed 45% more stories than control (0.96, SE 0.32). Twelve weeks after all incentives were removed, treated students completed 76% more stories (0.30, SE 0.14), relative to a control mean of 0.40, and the share of active users rose from 6.5% to 9.6%. Effects are concentrated in the upper tail of the engagement distribution, where nearly all platform activity is generated. Each story completed during the contest generates more lasting engagement for treated than for control students, and persistence extends beyond students who received prizes. Temporary contest-based incentives can durably expand the small segment of engaged users on which educational technology platforms depend.
Why do consumers search some products more than once (revisit) before making a purchase decision? How are these revisits related to search outcomes, such as consumers’ consideration sets and choices? In this paper, we build an online shopping website and run an incentive-aligned research study to find out. We show that most consumers revisit with the goal of comparing products, followed second by an expressed desire to obtain more information, and third by forgetting. Also, we document the ways in which search behavior differs across revisit motivations. Products revisited with the goal of obtaining additional information and comparing are more likely to be purchased than those revisited due to forgetting. Interestingly, revisits also reveal information about consumers’ consideration sets, which are typically unobserved: most consumers have eliminated a product from consideration if they don’t revisit it. This behavior implies consideration sets are dynamic, which we illustrate. Finally, managerial implications and possible extensions of the results are discussed.
We study players’ engagement decisions with different game modes in a popular multi-generational franchise video game. Some game modes are competitive where players’ utility might derive from performance, whereas other game modes are casual and feature the aspect of social interactions with other players. Model-free data patterns suggest that three potential mechanisms could jointly determine players’ decisions to engage in competitive versus casual game mode: unobserved preference heterogeneity, learning about their preferences for different modes, and skill accumulation. We develop a novel model that explains players’ decisions along both the extensive and the intensive margins with respect to different modes, while incorporating all three mechanisms. These mechanisms can be separately identified in our data set due to the panel feature of the multi-generational game. Our estimation results show several key insights about each mechanism. First, playing the competitive mode accumulates skills much faster than the casual mode. In addition, we find that 27 ∼ 32 ∼ 28
This study draws implications for targeting by incorporating switching reasons into a factor-analytic choice model for conducting benefit segmentation. In contrast to survey or conjoint-based studies, our segmentation task relies on a time series of brand choice decisions in real practice to improve external validity. Using physician-level panel data, we estimate a factor-analytic choice model to identify the primary product benefits each physician seeks. The key empirical challenge is that standard prescription data are entirely agnostic about the set of product benefits that underlie drug preferences. To address this issue, we utilize self-reported switching reasons in addition to the observed prescription choices. Accordingly, we extend the standard factor-analytic choice model to incorporate this augmented data and develop a Markov chain Monte Carlo (MCMC) procedure for estimation. Our proposed model enables us to directly identify which physicians are more efficacy, side effects, and/or cost saving oriented, an essential input to conducting benefit segmentation and fine-tuning subsequent targeted marketing promotion activities. We also investigate how misleading statistical inferences from standard factor-analytic choice models can be without the aid of augmented switching reasons data.
