Personalized Pricing and Customer Welfare
49 Pages Posted: 26 Jun 2017 Last revised: 21 Feb 2020
Date Written: August 3, 2019
Abstract We study the welfare implications of personalized pricing, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. We conduct a randomized controlled pricing field experiment to train a demand model and to conduct inferences about the effects of personalized pricing on firm and customer surplus. In a second experiment, we validate our predictions out of sample. Personalized pricing improves the firm's expected posterior profits by 19%, relative to optimized uniform pricing, and by 86%, relative to the firm's status quo pricing. On the demand side, customer surplus declines slightly under personalized pricing relative to a uniform pricing structure. However, over 60% of customers benefit from personalized prices that are lower than the optimal uniform price. Based on simulations with our demand estimates, we find several cases where customer surplus increases when the firm is allowed to condition on more customer features and classify customers into more granular segments. These findings indicate a need for caution in the current public policy debate regarding data privacy and personalized pricing. Some data restrictions could harm consumers and even reduce total consumer welfare.
Keywords: price discrimination, targeting, scalable price targeting, welfare, lasso regression, weighted likelihood bootstrap, data-mining, field experiment
JEL Classification: C11,C55, C93, D4, L11, M3
Suggested Citation: Suggested Citation