Akata, Mustafa AşkımErgin, KaanKaya, BüşraKızılay, AyşeÇakar, TunaŞahin, Zeynep2024-01-252024-01-252023Sahin, 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. 1-5.9798350318036https://doi.org/10.1109/IISEC59749.2023.10391021https://hdl.handle.net/20.500.11779/2216This 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.eninfo:eu-repo/semantics/closedAccessRfm analysisCustomer satisfactionAnalytical customer relationship management (acrm)Revenue performanceSegmentationConference Object10.1109/IISEC59749.2023.103910212-s2.0-85184668559N/AN/A51