Machine learning and operation research based method for promotion optimization of products with no price elasticity history

Abstract Many leading e-commerce retailers adopt a consistent pricing strategy to build customer trust and promote just a small portion of their catalog each week. Promotion optimization for consistent pricing retailers is a challenging problem, as they need to decide which products to promote, with no historical price elasticity information for the candidate products. In this paper, we introduce a novel approach for predicting product price elasticity impact for e-commerce retailers who use a consistent pricing strategy. We combine the commonly used operation research-based log-log demand model with the nonlinear gradient boosting machines algorithm to predict the price elasticity impact of products with no historical price elasticity information. A pessimistic prediction interval measure is used to accelerate the learning period and reduce the probability of selecting low impact promotions due to high model prediction uncertainty. We demonstrate the effectiveness of our approach on a real-world dataset collected from an online European department store.