Segmenting Bank Customers via RFM Model and Unsupervised Machine Learning

• URL: http://arxiv.org/abs/2008.08662v1
• Date: Wed, 19 Aug 2020 20:41:18 GMT
• Title: Segmenting Bank Customers via RFM Model and Unsupervised Machine Learning
• Authors: Musadig Aliyev, Elvin Ahmadov, Habil Gadirli, Arzu Mammadova and Emin Alasgarov
• Abstract summary: In recent years, one of the major challenges for financial institutions is the retention of their customers. In this paper, we used RFM technique and various clustering algorithms applied to the real customer data of one of the largest private banks of Azerbaijan.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, one of the major challenges for financial institutions is the retention of their customers using new methodologies of reliable and profitable segmentation. In the field of banking, the approach of offering all of the services to all the existing customers at the same time does not always work. However, being aware of what to sell, when to sell and whom to sell makes a huge difference in the conversion rate of the customers responding to new services and buying new products. In this paper, we used RFM technique and various clustering algorithms applied to the real customer data of one of the largest private banks of Azerbaijan.

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