Neural Network Approach to Demand Estimation and Dynamic Pricing in Retail

Abstract

This paper contributes to the literature on parametric demand estimation by using deep learning to model consumer preferences. Traditional econometric methods often struggle with limited within-product price variation, a challenge addressed by the proposed neural network approach. The proposed method estimates the functional form of the demand and demonstrates higher performance in both simulations and empirical applications. Notably, under low price variation, the machine learning model outperforms econometric approaches, reducing the mean squared error of initial price parameter estimates by nearly threefold. In empirical setting, the ML model consistently predicts a negative relationship between demand and price in 100% of cases, whereas the econometric approach fails to do so in 20% of cases. The suggested model incorporates a wide range of product characteristics, as well as prices of other products and competitors.