Fan Zhang

Assistant Professor of Marketing

Nova School of Business and Economics

Email: fan.zhang@novasbe.pt

Research Interest: Quantitative Marketing, Industrial Organization, Digital Platforms, Information Desgin

Research

Publication

A Method to Estimate Discrete Choice Models that is Robust to Consumer Search

with Jason Abaluck and Giovanni Compiani, forthcoming at the Journal of Political Economy

Abstract: We state conditions under which choice data suffices to identify preferences when consumers may not be fully informed about the attributes of goods. Our approach can be used to test for full information, to forecast how consumers will respond to information, and to conduct welfare analysis when consumers are imperfectly informed. In a lab experiment, we successfully forecast the response to new information when consumers engage in costly search. In data from Expedia, our method identifies which attribute was not immediately visible to consumers in search results, and we then use the model to compute the value of additional information.

Working Papers

Variety Seeking in High-Frequency Consumption: Implications for Targeted Marketing

with Carol Hengheng Lu and Zhijie Lin

Abstract: This paper studies consumers' variety-seeking preferences and their implications for targeted marketing on a food delivery platform. Our analysis reveals that a substantial fraction of consumers exhibit variety-seeking preferences, with an average consumer willing to pay 23.1% less to reorder from the same seller they purchased from in the previous period. Through counterfactual analysis, we find that optimizing rankings to account for variety-seeking preferences can enhance revenue, consumer welfare, and purchase probability. Furthermore, we find that the optimal targeted pricing strategy involves a general price increase with a discount for recent consumers, and consumers' variety-seeking preferences soften price competition.

Learning Through Ratings Under Endogenous Product Quality

with Przemyslaw Jeziorski

This paper examines the design of rating systems in markets where product quality is unobserved and dynamically managed by sellers. Ratings serve a dual purpose: they inform consumers about unobserved quality and, meanwhile, incentivize sellers' quality provision efforts. We model this interaction as a dynamic Bayesian game in which sellers can enhance quality through costly effort, while buyers update their beliefs about quality based on observed ratings. Using data from a short-term rental platform where we directly observe sellers' quality provision efforts, we empirically quantify sellers' intertemporal incentives and consumer learning. Using the structural estimates, we conduct counterfactual experiments to assess the impact of alternative rating systems, explicitly accounting for the endogenous provision of quality. We find that rating manipulation reduces equilibrium revenue, quality, and consumer welfare, while rating forgiveness improves these market outcomes.

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