期刊
JOURNAL OF RETAILING AND CONSUMER SERVICES
卷 77, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jretconser.2023.103620
关键词
Store location screening; Machine learning; Target group indices; Point -of -interest; Sequential ensemble model
类别
This paper proposes a machine learning-based model to recommend store locations using public data, including target group indices and competitor data. The effectiveness of the approach is demonstrated through testing with real data from a jewelry retailing chain, outperforming the best available industry benchmarks.
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.
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