An integrated AHP-RFM method to banking customer segmentation

Seyed Mahdi Rezaeinia · Abbas Keramati · Amir Albadvi

10 citations0/0 files storedInternational Journal of Electronic Customer Relationship Management

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

Abstract

The recognition and retention of valuable customers are of great importance for the banks, and other financial institutions. In this regard, the banks make their customers segmented in a variety of methods. In this paper, the data from customers of a commercial bank were considered and segmented using proposed weighted RFM method. Although RFM is not a new method, however, its new versions, including weighted RFM, have been applied in recent years, because they are highly accurate and efficient. In this research, we proposed the AHP method to calculate the weights of recency, frequency and monetary values. Also, we applied clustering algorithm like K-means to segment the customers. At last, we calculated and re-evaluated each customer group after a few months in order to test the results.

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References

2008Classifying the segmentation of customer value via RFM model and RS theory 2008Knowledge discovery on RFM model using Bernoulli sequence 2007Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression 2005Evaluation of knowledge management tools using AHP 2006Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming 2007Customer segmentation revisited: The case of the airline industry 2004A purchase-based market segmentation methodology 2008Quantitative models for direct marketing: A review from systems perspective

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2020Clustering of bank customers based on lifetime value using data mining methods

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