Segmenting Bank Customers via RFM Model and Unsupervised Machine Learning
This addresses customer retention and service targeting for financial institutions, but it is incremental as it applies existing methods to a new dataset.
The paper tackled customer segmentation for a bank in Azerbaijan using RFM analysis and clustering algorithms on real data, aiming to improve retention and conversion rates by targeting services effectively.
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.