Dynamic Pricing and Demand Learning with Limited Price Experimentation

Operations Research, Forthcoming

30 Pages Posted: 22 Jun 2014 Last revised: 3 Sep 2017

See all articles by Wang Chi Cheung

Wang Chi Cheung

Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)

David Simchi-Levi

Massachusetts Institute of Technology (MIT) - School of Engineering

He Wang

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE)

Date Written: February 26, 2017

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 to 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 at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings.

Keywords: Revenue Management, Dynamic Pricing, Learning-Earning Trade-off, Price Experimentation

Suggested Citation

Cheung, Wang Chi and Simchi-Levi, David and Wang, He, Dynamic Pricing and Demand Learning with Limited Price Experimentation (February 26, 2017). Operations Research, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2457296 or http://dx.doi.org/10.2139/ssrn.2457296

Wang Chi Cheung

Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR) ( email )

Singapore

David Simchi-Levi (Contact Author)

Massachusetts Institute of Technology (MIT) - School of Engineering ( email )

MA
United States

He Wang

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE) ( email )

765 Ferst Drive
Atlanta, GA 30332-0205
United States

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