Retail Store Location Screening : A Machine Learning-Based Approach

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

8 Scopus Citations
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Detail(s)

Original languageEnglish
Article number103620
Journal / PublicationJournal of Retailing and Consumer Services
Volume77
Online published21 Nov 2023
Publication statusPublished - Mar 2024

Abstract

With numerous location choices across dispersed markets and a lack of detailed store-level information, the initial screening process for selecting store locations is challenging. We propose a machine learning-based model that uses public city-, competitor-, and point-of-interest (POI)-level data, including target group indices (TGIs), and apply machine learning to recommend sites based on predicted store performance. We demonstrate the effectiveness of our approach with real data from a jewelry retailing chain. Three machine learning approaches were developed and tested using data from 743 same-brand jewelry stores, and we find that a customized sequential ensemble model performs the best and outperforms the best available industry benchmarks. Our approach offers a new scalable and cost-efficient screening process for retailers to identify potentially top-performing locations. © 2023 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Store location screening, Machine learning, Target group indices, Point-of-interest, Sequential ensemble model