A New Application of RFM Clustering for Guild Segmentation to Mine the Pattern of Using Banks’ e-Payment Services

Hamid Khobzi · Elham Akhondzadeh Noughabi · Behrouz Minaei Bidgoli

24 citations0/0 files storedJournal of Global Marketing

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

With increasing use of point of sale terminals at stores, banks are seeking to achieve a bigger portion in such financial exchanges. An important problem for banks is to identify the most profitable professions. For this purpose, a new application using recency, frequency, and monetary (RFM)-based clustering and customer lifetime value analysis containing two extensions of RFM is proposed for guild segmentation. The methodology is applied on a real data from an Iranian state bank. The findings reveal that this methodology is applicable in practice and could be very effective for bank managers of any other banks.

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References

2008Application of data mining techniques in customer relationship management: A literature review and classification 2003Customers as assets 2005Customer segmentation and strategy development based on customer lifetime value: A case study 2007Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression 2004An integrated data mining and behavioral scoring model for analyzing bank customers 2009Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty 2010Data Mining Techniques in CRM: Inside Customer Segmentation 2008Modeling churn using customer lifetime value

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