Nonparametric Pricing Analytics with Customer Covariates

TL;DR

This paper introduces a nonparametric adaptive pricing policy that learns customer preferences based on covariates without prior assumptions, optimizing revenue with near-optimal regret bounds in personalized retail pricing.

Contribution

It develops a novel nonparametric pricing algorithm that adaptively clusters customers and achieves near-optimal regret bounds, improving upon existing methods in personalized pricing.

Findings

Achieves regret of order $O( ext{log}(T)^2 T^{(2+d)/(4+d)})$

Demonstrates near-optimality with a lower bound of $O(T^{(2+d)/(4+d)})$

Does not rely on parametric assumptions about customer preferences

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.