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Information Journal Paper

Title

DATA MINING FOR CUSTOMER CHURN PREDICTION IN INSURANCE

Pages

 Start Page 41 | End Page 55

Abstract

 Knowledge oriented marketing as a new paradigm leads companies to combining business processes and DATA MINING specially in different area of customer relationship management such as CUSTOMER CHURNmanagement. DATA MINING (DM) as a knowledge technology, penetrates enormous databases of pervious transactions reveals customer preferences and behaviors to extract patterns to support managerial decisions for decreasing customer defection rate. In This research data from one insurance company databases from fire insurance utilized to show the potential power of DM to develop CUSTOMER CHURN prediction models. The research methodology is based on Cross-Industry Standard Process for DATA MINING (CRISP-DM). Results show the critical factor in predict CUSTOMER CHURN is attraction channel and after that customer purchase history and usage of place are important to recognition customers who are likely to churn.

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