Behavior-Based Recommendation System with and without Price Discrimination
COM3 Level 1
SR12, COM3 01-21
Abstract:
Consumer profiling from behavioral and personal data enables firms to discover consumers' intrinsic preference and personalize product recommendations. However, the firm may have biased incentives to steer consumers into accepting a less relevant but more profitable product, which then induces consumer suspicion and rejection of the recommendation. In this research, we build a micro-foundation of personalized recommendations based on both behavior-data and personal data with and without price discrimination. Specifically, we examine how firm may choose between the behavioral and demographic data to make product recommendations to uninformed consumers who have imperfect knowledge on their intrinsic preference. Our results show that information asymmetry leads to strategic ambiguity of semi-separating equilibrium, and behavioral-based recommendation may incur higher social welfare deadweight loss relative to behavioral-based price discrimination.
Bio:
Zibin XU is an associate professor of marketing and Ph.D. coordinator at City University of Hong Kong, He received a Ph.D. in marketing from University of Southern California (USC), a M.S. in statistics from University of Missouri Kansas City, and a B.S. in information and computing from Wuhan University. He studies the economics of marketing strategies such as the implications of big data, personalized product recommendation, and information design. His recent research has appeared in Marketing Science and Management Science. He teaches undergraduate, graduate, and executive courses ranging from marketing analytics to competitive strategies.