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 attractcustomers and make a distinction between different groups of customers and rankingthem and trying to keep the customers. To do this, the use of customer relationshipmanagement, and measurement of customer value is helpful. The main objective of thispaper is segmentation insurance customers based on the factors affecting the value ofour customers. In line with this objective, all data from 2311 life insurance customersare extracted from Taavon Insurance Company in a period of one year and accordingto 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 theinsurance 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 ofinsured people, golden customers cluster for study of insurer’s behavior was analyzedat first step. By use of algorithm, decision tree for life insurance data was classifiedbased on insured feature in order to predict position of every new entrance customer ineach cluster. This research can be used to develop marketing plans and develop andoffer products and services for each group of customers.