Retail store location screening: A machine learning-based approach

Jialiang Lu, Xu Zheng, Esterina Nervino, Yanzhi Li, Zhihua Xu and Yabo Xu

Journal of Retailing and Consumer Services, 2024, vol. 77, issue C

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.

Keywords: Store location screening; Machine learning; Target group indices; Point-of-interest; Sequential ensemble model (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:77:y:2024:i:c:s0969698923003715

DOI: 10.1016/j.jretconser.2023.103620

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