Intertemporal Pricing via Nonparametric Estimation: Integrating Reference Effects and Consumer Heterogeneity

Hansheng Jiang (), Junyu Cao () and Zuo-Jun Max Shen ()
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Hansheng Jiang: Department of Industrial Engineering and Operations Research, University of California–Berkeley, Berkeley, California 94720
Junyu Cao: McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712
Zuo-Jun Max Shen: Department of Industrial Engineering and Operations Research, University of California–Berkeley, Berkeley, California 94720; Faculty of Engineering and Faculty of Business and Economics, University of Hong Kong, Hong Kong S.A.R., China

Manufacturing & Service Operations Management, 2024, vol. 26, issue 1, 28-46

Abstract: Problem definition : 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. Academic/practical relevance : 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. Methodology : 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. Results : Moreover, we investigate the structure of optimal pricing policies and prove the suboptimality 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. Managerial implications : In practice, ignoring consumer heterogeneity may lead to a significant loss of revenue. Furthermore, heterogeneous reference effect offers a strong motive for promotions and price fluctuations.

Keywords: reference effect; consumer heterogeneity; data-driven; intertemporal pricing; nonparametric estimation; online retailing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:1:p:28-46

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