Analytical Approaches in Customer Relationship Management

dc.contributor.author Akata, Mustafa Aşkım
dc.contributor.author Ergin, Kaan
dc.contributor.author Kaya, Büşra
dc.contributor.author Kızılay, Ayşe
dc.contributor.author Çakar, Tuna
dc.contributor.author Şahin, Zeynep
dc.date.accessioned 2024-01-25T08:13:02Z
dc.date.available 2024-01-25T08:13:02Z
dc.date.issued 2023
dc.description.abstract This 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.
dc.identifier.citation Sahin, 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.
dc.identifier.doi 10.1109/IISEC59749.2023.10391021
dc.identifier.isbn 9798350318036
dc.identifier.scopus 2-s2.0-85184668559
dc.identifier.uri https://doi.org/10.1109/IISEC59749.2023.10391021
dc.identifier.uri https://hdl.handle.net/20.500.11779/2216
dc.language.iso en
dc.publisher IEEE
dc.relation.ispartof 2023 4th International Informatics and Software Engineering Conference (IISEC)
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Rfm analysis
dc.subject Customer satisfaction
dc.subject Analytical customer relationship management (acrm)
dc.subject Revenue performance
dc.subject Segmentation
dc.title Analytical Approaches in Customer Relationship Management
dc.type Conference Object
dspace.entity.type Publication
gdc.author.id Tuna Çakar / 0000-0001-8594-7399
gdc.author.institutional Çakar, Tuna
gdc.bip.impulseclass C5
gdc.bip.influenceclass C5
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gdc.coar.access metadata only access
gdc.coar.type text::conference output
gdc.description.department Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
gdc.description.endpage 5
gdc.description.publicationcategory Konferans Öğesi - Ulusal - Kurum Öğretim Elemanı
gdc.description.scopusquality N/A
gdc.description.startpage 1
gdc.description.wosquality N/A
gdc.identifier.openalex W4391021409
gdc.index.type Scopus
gdc.oaire.diamondjournal false
gdc.oaire.impulse 1.0
gdc.oaire.influence 2.7015854E-9
gdc.oaire.isgreen false
gdc.oaire.popularity 3.5477883E-9
gdc.oaire.publicfunded false
gdc.openalex.collaboration National
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gdc.openalex.normalizedpercentile 0.7
gdc.opencitations.count 1
gdc.plumx.mendeley 26
gdc.plumx.scopuscites 1
gdc.publishedmonth Kasım
gdc.relation.journal 2023 4th International Informatics and Software Engineering Conference
gdc.scopus.citedcount 1
gdc.virtual.author Çakar, Tuna
gdc.wos.publishedmonth Kasım
gdc.yokperiod YÖK - 2023-24
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