Data Mining Using FRAT-RFM Analysis Approach For Customer Segmentation And Profiling: Case Study Co-operative Bank Of Kenya

Abstract

With the Mobile Phones access increases rapidly and multi-channeling becoming increasingly widespread, studies of consumers will need to focus not just on understanding product choice, the reasons for channel choice but also on understanding the Recency, Frequency, Monetary and the Transaction type carried out by the customers. By using FRAT version of the RFM together with demographic attributes (gender, age, location) and data mining analysis, precise patterns can be derived for segmentation and profiling. Companies can use customer lifetime value that consists of three factors namely: current value of customers, potential value, and customer churn. Potential value of customers focuses on the cross-selling opportunities for current customers. Therefore, cross selling models are built on the total customers of the database that is not interesting. To overcome this, we presented a framework that estimates the current and previous value and churn probability for the customers and then segmented them based on these elements and classified the customers as per their demographic and life time value attributes. Although different approaches have been brought forward by different researchers, CRM, Customer Lifetime Value, Recency Frequency and Monetary, Size of Wallet, there is little research on incorporating different attributes in the models. In this study we describe the customer behavior based on customers’ demographic and Life Time Value attributes as a case study on a banking database. The research proposal reports on a descriptive study to identify the effectiveness of using FRAT (RFM) attributes coupled with customer demographic features for Mobile Banking Customer Segmentation and Profiling.