A combined approach for customer profiling in video on demand services using clustering and association rule mining

Basit Öğe Kaydı
dc.authoridPeker, Serhat / 0000-0002-6876-3982
dc.authorid, Sinemmg / 0000-0003-4408-9601
dc.authoridTurhan, Cigdem / 0000-0002-6595-7095
dc.authorscopusid57195278688
dc.authorscopusid57192819774
dc.authorscopusid24315330000
dc.authorwosidPeker, Serhat/A-9677-2016
dc.contributor.authorGüney, Sinem
dc.contributor.authorPeker, Serhat
dc.contributor.authorTurhan, Çiğdem
dc.date.accessioned2022-02-15T16:57:47Z
dc.date.available2022-02-15T16:57:47Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractThe purpose of this paper is to propose a combined data mining approach for analyzing and profiling customers in video on demand (VoD) services. The proposed approach integrates clustering and association rule mining. For customer segmentation, the LRFMP model is employed alongside the k-means and Apriori algorithms to generate association rules between the identified customer groups and content genres. The applicability of the proposed approach is demonstrated on real-world data obtained from an Internet protocol television (IPTV) operator. In this way, four main customer groups are identified: high consuming-valuable subscribers, less consuming subscribers,less consuming-loyal subscribers and disloyal subscribers. In detail, for each group of customers, a different marketing strategy or action is proposed, mainly campaigns, special-day promotions, discounted materials, offering favorite content, etc. Further, genres preferred by these customer segments are extracted using the Apriori algorithm. The results obtained from this case study also show that the proposed approach provides an efficient tool to form different customer segments with specific content rental characteristics, and to generate useful association rules for these distinct groups. The proposed combined approach in this research would be beneficial for IPTV service providers to implement effective CRM and customer-based marketing strategies.en_US
dc.identifier.doi10.1109/ACCESS.2020.2992064
dc.identifier.endpage84335en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85084959479en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage84326en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2992064
dc.identifier.urihttps://hdl.handle.net/20.500.14034/275
dc.identifier.volume8en_US
dc.identifier.wosWOS:000549526700008en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.journalIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCustomer segmentationen_US
dc.subjectdata miningen_US
dc.subjectclusteringen_US
dc.subjectassociation rulesen_US
dc.subjectRFM modelen_US
dc.subjectVoD servicesen_US
dc.subjectRelationship Management-Systemsen_US
dc.subjectApplying Lrfm Modelen_US
dc.subjectSegmentationen_US
dc.subjectRfmen_US
dc.subjectIndustryen_US
dc.titleA combined approach for customer profiling in video on demand services using clustering and association rule miningen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
A combined approach for customer profiling in video on demand services using clustering and association rule mining.pdf
Boyut:
5.27 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
Yönetim Bilişim Sistemleri Bölümü Koleksiyonu