Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/2216
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DC Field | Value | Language |
---|---|---|
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.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. | en_US |
dc.identifier.isbn | 9798350318036 | - |
dc.identifier.uri | https://doi.org/10.1109/IISEC59749.2023.10391021 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11779/2216 | - |
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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Rfm analysis | en_US |
dc.subject | Customer satisfaction | en_US |
dc.subject | Analytical customer relationship management (acrm) | en_US |
dc.subject | Revenue performance | en_US |
dc.subject | Segmentation | en_US |
dc.title | Analytical Approaches in Customer Relationship Management | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/IISEC59749.2023.10391021 | - |
dc.identifier.scopus | 2-s2.0-85184668559 | en_US |
dc.authorid | Tuna Çakar / 0000-0001-8594-7399 | - |
dc.description.PublishedMonth | Kasım | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Ulusal - Kurum Öğretim Elemanı | en_US |
dc.identifier.endpage | 5 | en_US |
dc.identifier.startpage | 1 | en_US |
dc.department | Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.relation.journal | 2023 4th International Informatics and Software Engineering Conference | en_US |
dc.institutionauthor | Çakar, Tuna | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | embargo_20400101 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Conference Object | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.02. 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 | Size | Format | |
---|---|---|---|---|
Relationship_Management.pdf Until 2040-01-01 | Proceedings Paper | 3.63 MB | Adobe PDF | View/Open Request a copy |
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