Application of enhanced decision tree algorithm to churn analysis


Customer churn can be described as the process by which consumers of goods and services discontinue the consumption of a product or service and switch to a business competitor of that particular good or service. It is of great concern to many companies, thus business intelligence techniques are needed in-order to detect and predict the possibility that a customer will churn. Customer churn prediction unlike most conventional business intelligence techniques deals with customer demographics, net worth-value, and market opportunities. It is used in determining customers who are likely to churn, those likely to remain loyal to the organization, and also for prediction of future churn rate. Customer defection is naturally a slow rate event, thus accurate and precise prediction methods are needed to detect the churning trend. In this study we propose an enhanced decision tree classification algorithm to predict churn trend analysis, given a database of customer records. The algorithm clusters and classifies customer records in order to predict the likely-hood that a customer will churn.

Publication Title

International Conference on Artificial Intelligence and Pattern Recognition 2009, AIPR 2009

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