ABSTRACT
One of the important issues in service organizations is to identify the customers, understanding
their difference and ranking them. Recently, the customer value as a quantitative parameter has been
used for segmenting customers. A practical solution for analytical development is using analytical
techniques such as dynamic clustering algorithms and programs to explore the dynamics in consumer
preferences. The aim of this research is to understand the current customer behavior and suggest a
suitable policy for new customers in order to attain the highest benefits and customer satisfaction. To
identify such market in life insurance customers. We have used the FKM.pf.niose fuzzy clustering
technique for classifying the customers based on their demographic and behavioral data of 1071
people in the period April to October 2014. Results show the optimal number of clusters is 3. These
three clusters can be named as: investment, security of life and a combination of both. Some
suggestions are presented to improve the performance of the insurance company.
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