Monday, November 28, 2022 - 11:00am to 12:00pm
Pricing Analytics Under Heterogeneous Consumer Behaviors
We consider intertemporal pricing in the presence of reference effects and consumer heterogeneity. Our research question encompasses how to estimate heterogeneous consumer reference effects from data and how to efficiently compute the optimal pricing policy. Understanding reference effects is essential for designing pricing policies in modern retailing. Our work contributes to this area by incorporating consumer heterogeneity under arbitrary distributions. We propose a mixed logit demand model that allows arbitrary joint distributions of valuations, responsiveness to prices, and responsiveness to reference prices among consumers. We use a nonparametric estimation method to learn consumer heterogeneity from transaction data. Further, we formulate the pricing optimization as an infinite horizon dynamic programming problem and solve it by applying a modified policy iteration algorithm. Moreover, we investigate the structure of optimal pricing policies and prove the sub-optimality of constant pricing policies even when all consumers are loss-averse according to the classical definition. Our numerical studies show that our estimation and optimization framework improves the expected revenue of retailers via accounting for heterogeneity. We validate our model using real data from JD.com, a large E-commerce retailer, and find empirical evidence of consumer heterogeneity. In practice, ignoring consumer heterogeneity may lead to a significant loss of revenue. Furthermore, heterogeneous reference effects offer a strong motive for promotions and price fluctuations.
Hansheng Jiang is a final-year Ph.D. candidate in the Department of Industrial Engineering and Operations Research at UC Berkeley, co-advised by Zuo-Jun Max Shen and Aditya Guntuboyina. Her research focuses on developing methodologies and algorithms for sequential and data-driven decision-making, especially in the presence of human behaviors. Her recent works address real-world problems in retailing platforms, on-demand shared mobility systems, and supply chain management. She is a recipient of the Berkeley Fellowship, a winner of the IISA best student paper award in theory and methodology, and a finalist of the MSOM data-driven research challenge.