Data Privacy in Pricing: Estimation Bias and Implications

72 Pages Posted: 5 Jul 2023 Last revised: 13 Aug 2023

See all articles by Ningyuan Chen

Ningyuan Chen

University of Toronto - Rotman School of Management

Ming Hu

University of Toronto - Rotman School of Management

Jialin Li

Rotman School of Management

Sheng Liu

Rotman School of Management

Date Written: June 22, 2023

Abstract

We study two privacy protection mechanisms motivated by emerging privacy regulations, limited retention and self-protection, in the context of data-driven (personalized) pricing. Limited retention refers to the removal of private data from a firm's database, and self-protection refers to a fraction of privacy-aware customers hiding their private information. Privacy protection affects the estimation of the demand model and, thus, the offered price. Assuming a linear demand curve, we find that, while the firm always earns lower revenues under privacy protection, which customer groups benefit from the protection depends on the product/service type. For inferior goods (e.g., those whose demand is negatively correlated with income as a focal private feature), offered prices would decrease for all customers due to limited retention, and counter-intuitively, under self-protection, privacy-protecting customers would see a price increase while privacy-unconscious customers would see a price decrease. The opposite results hold for normal goods (e.g., those whose demand is positively correlated with income). On the other hand, the magnitude of the resulting price change is greater for industries with stronger historical price personalization. We validate our theoretical findings with a real dataset of online auto loans which consists mostly of "inferior" loans. Remarkably, we indeed observe in extensive numerical experiments for self-protection that protective customers face consistent price increases, regardless of what features such customers protect. We extend the framework to nonlinear demand functions and a duopoly. In the duopoly, the situation can be that both firms earn higher revenues, whereas customers pay more under privacy protection, in contrast to the monopolistic setting.

Keywords: Privacy, Personalized Pricing, Model Estimation, Revenue Management

Suggested Citation

Chen, Ningyuan and Hu, Ming and Li, Jialin and Liu, Sheng, Data Privacy in Pricing: Estimation Bias and Implications (June 22, 2023). Available at SSRN: https://ssrn.com/abstract=4488404 or http://dx.doi.org/10.2139/ssrn.4488404

Ningyuan Chen

University of Toronto - Rotman School of Management ( email )

Ming Hu

University of Toronto - Rotman School of Management ( email )

105 St. George st
Toronto, ON M5S 3E6
Canada
416-946-5207 (Phone)

HOME PAGE: http://ming.hu

Jialin Li (Contact Author)

Rotman School of Management ( email )

105 St. George st
Toronto, ON M5S 3E6
Canada

HOME PAGE: http://jia-lin-li.github.io

Sheng Liu

Rotman School of Management ( email )

105 St. George st
Toronto, ON M5S 3E6
Canada

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
108
Abstract Views
290
Rank
412,919
PlumX Metrics