Show simple item record

dc.contributor.authorBilgic, Emrah
dc.contributor.authorCakir, Ozgur
dc.contributor.authorKantardzic, Mehmed
dc.contributor.authorDuan, Yanqing
dc.contributor.authorCao, Guangming
dc.date.accessioned2021-04-12T11:07:25Z
dc.date.available2022-10-09T00:00:00Z
dc.date.available2021-04-12T11:07:25Z
dc.date.issued2021-04-29
dc.identifier.citationBilgic E, Cakir O, Kantardzic M, Duan Y, Cao G (2021) 'Retail analytics: store segmentation using rule-based purchasing behaviors analysis', The International Review of Retail, Distribution and Consumer Research, 31 (4), pp.457-480.en_US
dc.identifier.issn0959-3969
dc.identifier.doi10.1080/09593969.2021.1915847
dc.identifier.urihttp://hdl.handle.net/10547/624902
dc.description.abstractRetailers are facing challenges in making sense of the significant amount of data for better understanding of their customers. While retail analytics plays an increasingly important role in successful retailing management, comprehensive store segmentation based on a Data Mining-based Retail Analytics is still an under-researched area. This study seeks to address this gap by developing a novel approach to segment the stores of retail chains based on “purchasing behavior of customers” and applying it in a case study. The applicability and benefits of using Data Mining techniques to examine purchasing behavior and identify store segments are demonstrated in a case study of a global retail chain in Istanbul, Turkey. Over 600K transaction data of a global grocery retailer are analyzed and 175 stores in İstanbul are successfully segmented into five segments. The results suggest that the proposed new retail analytics approach enables the retail chain to identify clusters of stores in different regions using all transaction data and advances our understanding of store segmentation at the store level. The proposed approach will provide the retail chain the opportunity to manage store clusters by making data-driven decisions in marketing, customer relationship management, supply chain management, inventory management and demand forecasting.en_US
dc.language.isoenen_US
dc.publisherTaylor and Francisen_US
dc.relation.urlhttps://www.tandfonline.com/doi/abs/10.1080/09593969.2021.1915847
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectretailen_US
dc.subjectbusiness analytics (BA)en_US
dc.subjectdata-miningen_US
dc.subjectSubject Categories::N240 Retail Managementen_US
dc.titleRetail analytics: store segmentation using rule-based purchasing behaviors analysisen_US
dc.typeArticleen_US
dc.identifier.eissn1466-4402
dc.contributor.departmentIskenderun Technical Universityen_US
dc.contributor.departmentMarmara Universityen_US
dc.contributor.departmentUniversity of Louisvilleen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.contributor.departmentAjman Universityen_US
dc.identifier.journalThe International Review of Retail, Distribution and Consumer Researchen_US
dc.date.updated2021-04-12T11:02:26Z
dc.description.note18m embargo


Files in this item

Thumbnail
Name:
Retail+Analytics+-+Store+Segme ...
Embargo:
2022-10-29
Size:
746.4Kb
Format:
PDF
Description:
author's accepted version

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International