Personalization from Customer Data Aggregation Using List Price

Zibin Xu () and Anthony Dukes ()
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Zibin Xu: College of Business, City University of Hong Kong, Kowloon Tong 999077, Hong Kong
Anthony Dukes: Marshall School of Business, University of Southern California, Los Angeles, California 90089

Management Science, 2022, vol. 68, issue 2, 960-980

Abstract: When consumers’ inferences of their reservation values are subject to environmental noise, firms can use customer data aggregation to obtain superior knowledge. This facilitates personalized pricing but may also induce consumer suspicions of overpaying. To alleviate the suspicions and convince consumers of their value, the firm may design its personalization scheme to include a list price in addition to the personalized prices. We find that only a separating equilibrium with list pricing survives the intuitive criterion. Specifically, when consumers underestimate their value, it is essential to use a binding list price to inform the consumers about the market’s price ceiling. Contrary to the conventional wisdom, the firm cannot abuse its informational advantage to steer consumers into overestimation, and price discrimination may strictly benefit the consumers who avoid overpaying.

Keywords: personalized pricing; customer data aggregation; list price; superior knowledge; consumer privacy (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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