Data-driven decision-support system for expansions in retail stores: Dexter

Abstract

The expansion of retail stores is an important decision in the retail industry, involving significant investments and long-term consequences for customer service, satisfaction, and revenue growth. The decision-making process for store expansion is complex, and requires detailed information and a multilevel view of store operations over time. Yet, the literature shows a scarcity of data-driven decision-support systems for retail store expansions. To address this gap, we propose a data-driven decision support system called Dexter, which provides store expansion recommendations using a novel ensemble algorithm. Dexter was developed in collaboration with an industry partner, who provided access to various critical datasets. The results of the evaluation with domain experts show that Dexter has the potential to assist store owners in making informed decisions for retail store expansions. This work paves the way for further research and acts as an entry point to attract more works in this under-researched area.