identify valuable customers of taavon insurance in field of life insurance with data mining approach

today the most crucial tasks of mentioned organizations is to recognize and attract customers and make  a distinction between different groups of customers and ranking them and trying to keep the customers. to do this, the use of customer relationship management, and measurement of customer value is helpful. the main objective of this paper is segmentation insurance customers based on the factors affecting the value of our customers. in line with this objective, all data from 2311 life insurance customers are extracted from taavon insurance company in a period of one year and according to the specification of insurance policy it was clustered by use of k-means algorithm. based on the results, customers were divided into five clusters.  according to the insurance experts view, chosen specifications were weighted. after calculating the clv, customers were ranked. each cluster got specific name like, “golden customer”, “valuable customer”, “favorable customer”, “less favorable value customer” and “less valuable customer”. by discovery of association rules based on the features of insured people, golden customers cluster for study of insurer’s behavior was analyzed at first step. by use of algorithm, decision tree for life insurance data was classified based on insured feature in order to predict position of every new entrance customer in each cluster. this research can be used to develop marketing plans and develop and offer products and services for each group of customers.