Discreet Personalized Pricing

36 Pages Posted: 28 Oct 2022

See all articles by Benjamin Shiller

Benjamin Shiller

Brandeis University - Department of Economics

Date Written: 2022


Emerging tracking data allow precise predictions of individuals’ reservation values. However, firms are reluctant to conspicuously implement personalized pricing because of concerns about consumer and regulatory reprisals. This paper proposes and applies a method which disguises personalized pricing as dynamic pricing. Specifically, a firm can sometimes tailor the “posted” price for the arriving consumer but privately commits to change price infrequently. Note such pricing may unintentionally arise through algorithmic pricing. I examine outcomes in four contexts: one empirical and three hypothetical distributions of consumer valuations. I find that this strategy is most intense and raises profits most for medium popularity products. Furthermore, improvements in the precision of individual-level demand estimates raise the range of popularities this strategy can be profitably applied to. I conclude that this is an auspicious strategy for online platforms, if not already secretly in use.

Keywords: personalized pricing, algorithmic pricing, price discrimination, targeted pricing, behavioural pricing, dynamic pricing, sticky pricing

JEL Classification: L810, D400, L100

Suggested Citation

Shiller, Benjamin, Discreet Personalized Pricing (2022). CESifo Working Paper No. 10025, Available at SSRN: https://ssrn.com/abstract=4259622 or http://dx.doi.org/10.2139/ssrn.4259622

Benjamin Shiller (Contact Author)

Brandeis University - Department of Economics ( email )

Waltham, MA 02454-9110
United States
781-736-5205 (Phone)

HOME PAGE: http://benjaminshiller.com

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