Nonparametric Pricing Analytics with Customer Covariates

42 Pages Posted: 16 May 2018 Last revised: 17 Feb 2020

See all articles by Ningyuan Chen

Ningyuan Chen

University of Toronto at Mississauga - Department of Management; University of Toronto - Rotman School of Management

Guillermo Gallego

HKUST; Columbia University

Date Written: May 3, 2018

Abstract

Personalized pricing analytics is becoming an essential tool in retailing. Upon observing the personalized information of each arriving customer, the firm needs to set a price accordingly based on the covariates such as income, education background, past purchasing history to extract more revenue. For new entrants of the business, the lack of historical data may severely limit the power and profitability of personalized pricing. We propose a nonparametric pricing policy to simultaneously learn the preference of customers based on the covariates and maximize the expected revenue over a finite horizon. The policy does not depend on any prior assumptions on how the personalized information affects consumers' preferences (such as linear models). It is adaptively splits the covariate space into smaller bins (hyper-rectangles) and clusters customers based on their covariates and preferences, offering similar prices for customers who belong to the same cluster trading off granularity and accuracy. We show that the algorithm achieves a regret of order $O(\log(T)^2 T^{(2+d)/(4+d)})$, where $T$ is the length of the horizon and $d$ is the dimension of the covariate. It improves the current regret in the literature \citep{slivkins2014contextual}, under mild technical conditions in the pricing context (smoothness and local concavity). We also prove that no policy can achieve a regret less than $O(T^{(2+d)/(4+d)})$ for a particular instance and thus demonstrate the near optimality of the proposed policy.

Keywords: multi-armed bandit, dynamic pricing, online learning, regret analysis, contextual information

Suggested Citation

Chen, Ningyuan and Gallego, Guillermo, Nonparametric Pricing Analytics with Customer Covariates (May 3, 2018). Available at SSRN: https://ssrn.com/abstract=3172697 or http://dx.doi.org/10.2139/ssrn.3172697

Ningyuan Chen (Contact Author)

University of Toronto at Mississauga - Department of Management ( email )


Canada

University of Toronto - Rotman School of Management ( email )

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

Guillermo Gallego

HKUST ( email )

Clearwater Bay
Kowloon, 999999
Hong Kong

HOME PAGE: http://https://seng.ust.hk/about/people/faculty/guillermo-gallego

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

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