Purpose: this study wants to find any relationship between the numbers of purchase and the income the customer brings to the company. The attempt is to find those customers who buy more than one life insurance policy and represent the signs of good payments at the same time by the help of data mining tools.
Design/ methodology/ approach: the approach of this research is to use data mining tools based on CRISP-DM methodology. The classification is based on the K-means algorithm and prediction is applied by a proposed formula by the researcher in Excel worksheet.
Findings: By selecting the customers who bought more than one policy, and filtering the Income bringer customers, the researcher could extract some simple rules to predict which customer belongs to which cluster. Based on the prediction, the company can change its strategies in relation to different customers.
Originality/value: Utilizing data mining approach to classify different customers in life insurance and prediction based on the classification is a new approach amongIran insurance companies. There is not enough research and implementation in relation to the CRM and data mining in the insurance industry. Especially CRISP-DM methodology was used very hardly before in a life insurance investigation.