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

Wang Chi Cheung (), David Simchi-Levi () and He Wang ()
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Wang Chi Cheung: Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore 138632
David Simchi-Levi: Department of Civil and Environmental Engineering, MIT Institute for Data, Systems, and Society, Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
He Wang: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

Operations Research, 2017, vol. 65, issue 6, 1722-1731

Abstract: 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.

Keywords: dynamic pricing; learning–earning trade-off; price experimentation (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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