Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments

Kanishka Misra (), Eric M. Schwartz () and Jacob Abernethy ()
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Kanishka Misra: Rady School of Management, University of California, San Diego, La Jolla, California 92093
Eric M. Schwartz: Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109
Jacob Abernethy: School of Computer Science, College of Computing, Georgia Institute of Technologyy, Atlanta, Georgia 30332

Marketing Science, 2019, vol. 38, issue 2, 226-252

Abstract: Pricing managers at online retailers face a unique challenge. They must decide on real-time prices for a large number of products with incomplete demand information. The manager runs price experiments to learn about each product’s demand curve and the profit-maximizing price. In practice, balanced field price experiments can create high opportunity costs, because a large number of customers are presented with suboptimal prices. In this paper, we propose an alternative dynamic price experimentation policy. The proposed approach extends multiarmed bandit (MAB) algorithms from statistical machine learning to include microeconomic choice theory. Our automated pricing policy solves this MAB problem using a scalable distribution-free algorithm. We prove analytically that our method is asymptotically optimal for any weakly downward sloping demand curve. In a series of Monte Carlo simulations, we show that the proposed approach performs favorably compared with balanced field experiments and standard methods in dynamic pricing from computer science. In a calibrated simulation based on an existing pricing field experiment, we find that our algorithm can increase profits by 43% during the month of testing and 4% annually.

Keywords: dynamic pricing; e-commerce; online experiments; machine learning; multiarmed bandits; partial identification; minimax regret; nonparametric econometrics; A/B testing; field experiments (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:38:y:2019:i:2:p:226-252

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