Personalized Dynamic Pricing with Machine Learning: High-Dimensional Features and Heterogeneous Elasticity

• 成果类型: Article
• 署名作者: Ban, Gah-Yi; Keskin, N. Bora
• 署名单位: University System of Maryland; University of Maryland College Park; Duke University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3680
• 发表日期: 2021
• 页码: 5549-5568
• 关键词: Dynamic pricing; Demand Learning; Demand uncertainty; Regret Analysis; Lasso; Machine Learning

摘要

We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers' characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters of which depend on s out of the d features. The seller initially does not know the relationship between the customer features and the product demand but learns this through sales observations over a selling horizon of T periods. We prove that the seller's expected regret, that is, the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order s root T under any admissible policy. We then design a near-optimal pricing policy for a semiclairvoyant seller (who knows which s of the d features are in the demand model) who achieves an expected regret of order s root T log T. We extend this policy to a more realistic setting, where the seller does not know the true demand predictors, and show that this policy has an expected regret of order s root T (log d + log T), which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods, such as myopic pricing and segment-then-optimize policies. Furthermore, our policy improves upon the loan company's historical pricing decisions by 47% in expected revenue over a six-month period.