Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach

Abstract

While markdowns in retail have been studied for decades in traditional business, nowadays e-commerce fresh retail brings much more challenges. Due to the limited shelf life of perishable products and the limited opportunity of price changes, it is difficult to predict sales of a product at a counterfactual price, and therefore it is hard to determine the optimal discount price to control inventory and to maximize future revenue. Traditional machine learning-based methods have high predictability but they can not reveal the relationship between sales and price properly. Traditional economic models have high interpretability but their prediction accuracy is low. In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization. Firstly, we build a semi-parametric structural model to learn individual price elasticity and predict counterfactual demand. This semi-parametric model takes advantage of both the predictability of nonparametric machine learning model and the interpretability of economic model. Secondly, we propose a multi-period dynamic pricing algorithm to maximize the overall profit of a perishable product over its finite selling horizon. Different with the traditional approaches that use the deterministic demand, we model the uncertainty of counterfactual demand since it inevitably has randomness in the prediction process. Based on the stochastic model, we derive a sequential pricing strategy by Markov decision process, and design a two-stage algorithm to solve it. The proposed algorithm is very efficient. It reduces the time complexity from exponential to polynomial. Experimental results show the advantages of our pricing algorithm, and the proposed framework has been successfully deployed to the well-known e-commerce fresh retail scenario - Freshippo.