Technical Note-Dynamic Pricing and Demand Learning with Limited Price Experimentation

• 成果类型: Article
• 署名作者: Cheung, Wang Chi; Simchi-Levi, David; Wang, He
• 署名单位: Agency for Science Technology & Research (A*STAR); A*STAR - Institute of High Performance Computing (IHPC); Massachusetts Institute of Technology (MIT); University System of Georgia; Georgia Institute of Technology
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2017.1629
• 发表日期: 2017
• 页码: 1722-1731
• 关键词: revenue management; learning-earning trade-off; price experimentation; Dynamic pricing

摘要

In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice, sellers often face business constraints that prevent them from conducting extensive experimentation. We consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The seller is allowed to make at most m price changes during T periods. The objective is to minimize the worst-case regret-i.e., the expected total revenue loss compared with a clairvoyant who knows the demand distribution in advance. We demonstrate a pricing policy that incurs a regret of O(log((m))T), or m iterations of the logarithm. Furthermore, we describe an implementation of this pricing policy at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings.