An Optimal Churn Prediction Model using Support Vector Machine with Adaboost

Customer churn is a common measure of lost customers. By minimizing churn, a company can maximize its profits. Companies have recognized that existing customers are most valuable assets. Customer retention is important for a good marketing and a customer relationship management strategy. In this paper, a detailed scheme is worked out to convert raw customer data into meaningful and useful data that suits modelling buying behaviour and in turn to convert this meaningful data into knowledge for which predictive data mining techniques are adopted. In this work, a boosted version of SVM which is a combination of SVM with Adaboost is used for increasing the accuracy of generated rules. Boosted versions have high accuracy and performance than non-boosted versions. The aim of churn prediction model is to detect the customers with high tendency to leave the firm and also increase the revenue for the firm.