Sequential rule mining for analysis of customer movements in different visits

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dc.authorscopusid57390638300
dc.authorscopusid57202924825
dc.contributor.authorTaşer, Pelin Yıldırım
dc.contributor.authorDoğan, Onur
dc.date.accessioned2023-03-22T19:47:56Z
dc.date.available2023-03-22T19:47:56Z
dc.date.issued2021
dc.departmentBelirleneceken_US
dc.description.abstractDeveloping technologies in customer analytics provide several opportunities to retailers. Analyzing customer movements in the stores as a part of customer analytics can reveal various shopping behaviors. It facilitates to understand better customers' visit purposes. This study applies four sequential mining algorithms, CMRules, CMDeo, ERMiner, and RuleGrowth, to analyze the visit purposes of customers in a supermarket. Moreover, it compares variations among different visits belonging to the same customers. This study concludes three main results. First, it indicates that customers prefer visiting the supermarket not only for their specific needs but also for all their needs at every visit. Second, the ERMiner algorithm is faster than the other algorithms. Third, customers who visit {Construction, Kitchen} and {Sanitary ware, Garden} bought at least one product with a high probability. Moreover, this study describes the concept of interesting rule, which has a lower support value and higher confidence value. Customers can visit the supermarket for various purposes resulting in different interesting rules. As an interesting rule in the second visit, purchased customers visited Construction, Garden and Kitchen aisles before leaving the supermarket whereas this rule did not appear in the first visits. Customers visited the Construction aisle more after they visited the Entrance and Ironmongery aisles in their second visit. © IEOM Society International.en_US
dc.description.sponsorshipHis projects are supported by Turkish Scientific and Research Council (TUBITAK) and Izmir Development Agency. His current research areas are process mining, machine learning, (fuzzy) data mining, fuzzy decision making.en_US
dc.identifier.endpage495en_US
dc.identifier.isbn9781792361272
dc.identifier.issn21698767
dc.identifier.scopus2-s2.0-85126265585en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage485en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14034/912
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEOM Societyen_US
dc.relation.journalProceedings of the International Conference on Industrial Engineering and Operations Managementen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCustomer analyticsen_US
dc.subjectCustomer movement analysisen_US
dc.subjectInteresting rulesen_US
dc.subjectRetail sectoren_US
dc.subjectSequential rule miningen_US
dc.titleSequential rule mining for analysis of customer movements in different visitsen_US
dc.typeConference Objecten_US

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