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In this article we are going to define the overall customer relationship management (CRM) and Data mining, Factors between the techniques and software to "data mining" in "CRM" and the interaction between two concepts. For this purpose and after that in past studies and reports on issues of "data mining" and "CRM" took place between them. The effect of "data mining" and extract latent information from large databases of valuable customer has made their determination, and maintenance in order toattract customers through its taken a step forward and ultimately achieve profitability and efficiency are good.

In order to continue life-sustaining competitive advantage, many organizations focus on maximizing the marketing relationship with their customer lifetime value and customer churn management. In fact, more organizations are realizing that their most valuable resource is their current customer base. In the present study are to go through a database collected from 300 customers, including an insurance company in Iran has been used. In order to check the model presented with a desire to review a decision tree classification methods (C5.0, CART, CHAID, and Quest), Bayesian networks and neural networks will be paid with respect to sample. Survey results can help managers, marketers in this arena is in various industries. Reduction strategies appropriate to offer in this field. The entire paper must be in A4 size and "Moderate" margin

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Churn customer, one of the most important issues in customer relationship management and marketing is especially in industries such as telecommunications, the financial and insurance. In recent decades much research has been done in this area. In this research, the index set for the reasons set reason churn customers for our customers is of particular importance. In this study we are intended to provide a formula for the index churn customers, the better to understand the reasons for customers to provide churn. Therefore, in order to evaluate the formula provided through six Classification methods (Decision tree QUEST, Decision tree C5.0, Decision tree CHAID, Decision trees CART, Bayesian network, Neural network) to evaluate the formula will be involved with individual indicators