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Segmentation of the Customer's
Mellat Bank in Arak and
Determining (CRM) Strategies for each Segment

Abstract:
Meticulous recognition of customers, especially loyal and profitable ones and
customer's Relationship Management (CRM) to realize profitability have become
and indispensable part of required accoutrements for finance and credit institutes,
especially the banks. One approach applied to achieve this goal is using
segmentation technique to analyze the customer's behavior considering criteria
like loyalty and profitability.
Recency, Frequency Monetary (RFM) model is one of the most widely used
models for segmentation. RFM's optimized model, weighted RFM is in the
limelight.
This research is a case study conducted in 19 branches of Mellat bank in
Arak. In the first step, the variables of RFM model are extracted. In the next step,
applying AHP technique, the weights of respective variables are gauged.
Advancing the research, enjoying the k-means algorithm, customers are clustered
on the basis of weighted RFM. According to the results, seven clusters of
customers are clustered on the basis of weighted RFM. According to the results,
seven clusters of customers are identified. For each cluster, customer's loyalty is
calculated using comprehensive ranking approach. By ranking the loyalty of each
cluster, the most loyal and consequently the most profitable customers are
catalogued. The findings are rectified by the professionals and managers of Mellat
bank using expert judgment technique.

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1- Customer Segmentation
2- Clustering
3- Data Mining
4- Customer Loyalty
5- Customer Relationship Management (CRM)
6- Analytical CRM
7- Market Segmentation
8- K - means
9- W RFM
10- AHP