Nonparametric Pricing Analytics with Customer Covariates

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, and 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 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 $O\left(\log {\left(T\right)}^2{T}^{\left(2+d\right)/\left(4+d\right)}\right)$, where T is the length of the horizon and d is the dimension of the covariate. It improves the current regret in the literature (Slivkins 2014) under mild technical conditions in the pricing context (smoothness and local concavity). We also prove that no policy can achieve a regret less than $O\left(T^{\left(2+d\right)/\left(4+d\right)}\right)$ for a particular instance and, thus, demonstrate the near-optimality of the proposed policy.