Personalized Retail Promotions Through a Directed Acyclic Graph–Based Representation of Customer Preferences

Abstract

A Framework to Run Personalized Promotions The availability of individual-level transaction data allows retailers to implement personalized operational decisions. Although such decisions have been around for several years now in online platforms, recent technological developments open new opportunities to extend similar practices to bricks-and-mortar settings (e.g., by using electronic price tags to show different prices to different customers or by using beacon-based technology to send promotion offers to targeted customers). In “Personalized Retail Promotions through a DAG-Based Representation of Customer Preferences,” Jagabathula, Mitrofanov, and Vulcano propose a back-to-back procedure for running customized promotions in retail operations contexts, from the construction of a nonparametric choice model where customer preferences are represented by directed acyclic graphs to the formulation of the promotion optimization problem. The empirical validation of their proposal on real supermarket data shows the promising performance of their approach over state-of-the-art benchmarks.