Retail store location screening: A machine learning-based approach

Jialiang Lu

doi:10.1016/j.jretconser.2023.103620

Retail store location screening: A machine learning-based approach

2024-03-01

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AI

DOI：10.1016/j.jretconser.2023.103620

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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

Retail store location screening: A machine learning-based approach

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