Journal
OPERATIONS RESEARCH
Volume 65, Issue 6, Pages 1722-1731Publisher
INFORMS
DOI: 10.1287/opre.2017.1629
Keywords
revenue management; learning-earning trade-off; price experimentation; dynamic pricing
Funding
- Groupon
- Accenture-MIT Alliance in Business Analytics
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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.
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