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