Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/2216
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAkata, Mustafa Aşkım-
dc.contributor.authorErgin, Kaan-
dc.contributor.authorKaya, Büşra-
dc.contributor.authorKızılay, Ayşe-
dc.contributor.authorÇakar, Tuna-
dc.contributor.authorŞahin, Zeynep-
dc.date.accessioned2024-01-25T08:13:02Z-
dc.date.available2024-01-25T08:13:02Z-
dc.date.issued2023-
dc.identifier.citationSahin, Z., Ergin, K., Akata, M. A., Kaya, B., Kizilay, A., & Cakar, T. (2023, December). Analytical Approaches in Customer Relationship Management. In 2023 4th International Informatics and Software Engineering Conference. IEEE. pp. 1-5.en_US
dc.identifier.isbn9798350318036-
dc.identifier.urihttps://doi.org/10.1109/IISEC59749.2023.10391021-
dc.identifier.urihttps://hdl.handle.net/20.500.11779/2216-
dc.description.abstractThis study examines the impact of analytical customer relationship management (aCRM) strategies, specifically the segmentation approach using RFM analysis and artificial learning methods, on customer satisfaction, revenue performance, and loyalty in businesses. The research adopts an approach that integrates data from both online and offline channels onto a single platform, providing a holistic view of customer behaviors. Combining the segmentation obtained through RFM analysis and artificial learning methods with timely campaigns has enhanced shopping opportunities for customers and increased customer satisfaction and loyalty. The use of aCRM as a strategic marketing and sales tool has enabled businesses to manage customer relationships more effectively. This paper contributes to the literature in this field by presenting in detail the advantages offered by aCRM, its application methods, and the results obtained.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRfm analysisen_US
dc.subjectCustomer satisfactionen_US
dc.subjectAnalytical customer relationship management (acrm)en_US
dc.subjectRevenue performanceen_US
dc.subjectSegmentationen_US
dc.titleAnalytical Approaches in Customer Relationship Managementen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/IISEC59749.2023.10391021-
dc.identifier.scopus2-s2.0-85184668559en_US
dc.authoridTuna Çakar / 0000-0001-8594-7399-
dc.description.PublishedMonthKasımen_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US
dc.identifier.endpage5en_US
dc.identifier.startpage1en_US
dc.departmentMühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.relation.journal2023 4th International Informatics and Software Engineering Conferenceen_US
dc.institutionauthorÇakar, Tuna-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextembargo_20400101-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeConference Object-
item.cerifentitytypePublications-
crisitem.author.dept02.02. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Files in This Item:
File Description SizeFormat 
Relationship_Management.pdf
  Until 2040-01-01
Proceedings Paper3.63 MBAdobe PDFView/Open    Request a copy
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

1
checked on Nov 30, 2024

Page view(s)

78
checked on Dec 2, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.