Intertemporal Pricing via Nonparametric Estimation: Integrating Reference Effects and Consumer Heterogeneity
研究在消费者存在参考效应和异质性时的跨期定价问题，提出混合Logit需求模型和非参数估计方法，利用京东数据验证模型，发现忽略异质性会导致显著收益损失。
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. History: This paper has been accepted as part of the 2020 MSOM Data Driven Research Challenge. Funding: This work was partially supported by the National Natural Science Foundation of China [Grant 71991462]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1134 .