The Value of Personalized Pricing

Adam N. Elmachtoub (), Vishal Gupta () and Michael L. Hamilton ()
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Adam N. Elmachtoub: Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University, New York, New York 10027
Vishal Gupta: Data Science and Operations, Marshall School of Business, University of Southern California, Los Angeles, California 90089
Michael L. Hamilton: Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260

Management Science, 2021, vol. 67, issue 10, 6055-6070

Abstract: Increased availability of high-quality customer information has fueled interest in personalized pricing strategies, that is, strategies that predict an individual customer’s valuation for a product and then offer a price tailored to that customer. Although the appeal of personalized pricing is clear, it may also incur large costs in the forms of market research, investment in information technology and analytics expertise, and branding risks. In light of these trade-offs, our work studies the value of personalized pricing strategies over a simple single-price strategy. We first provide closed-form lower and upper bounds on the ratio between the profits of an idealized personalized pricing strategy (first-degree price discrimination) and a single-price strategy. Our bounds depend on simple statistics of the valuation distribution and shed light on the types of markets for which personalized pricing has little or significant potential value. Second, we consider a feature-based pricing model where customer valuations can be estimated from observed features. We show how to transform our aforementioned bounds into lower and upper bounds on the value of feature-based pricing over single pricing depending on the degree to which the features are informative for the valuation. Finally, we demonstrate how to obtain sharper bounds by incorporating additional information about the valuation distribution (moments or shape constraints) by solving tractable linear optimization problems.

Keywords: price discrimination; personalization; market segmentation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:67:y:2021:i:10:p:6055-6070

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