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

TL;DR

This paper introduces a data-driven, interpretable pricing framework for e-commerce fresh retail, combining counterfactual demand prediction with multi-period profit optimization, effectively handling demand uncertainty and improving pricing strategies.

Contribution

It develops a semi-parametric demand model and a stochastic, multi-period pricing algorithm based on Markov decision processes, tailored for perishable goods in e-commerce.

Findings

The proposed algorithm outperforms traditional methods in profit maximization.

The framework effectively models demand uncertainty, leading to better pricing decisions.

Successful deployment in Freshippo demonstrates practical applicability.

Abstract

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.