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Customer Churning Forecasting and Strategic Implication in Online Auto Insurance using Decision Tree Algorithms  

Lim, Se-Hun (Dept. of Management Information Systems, Sangji University)
Hur, Yeon (Dept. of Business Administration, Chung-Ang University)
Publication Information
Information Systems Review / v.8, no.3, 2006 , pp. 125-134 More about this Journal
Abstract
This article adopts a decision tree algorithm(C5.0) to predict customer churning in online auto insurance environment. Using a sample of on-line auto insurance customers contracts sold between 2003 and 2004, we test how decision tree-based model(C5.0) works on the prediction of customer churning. We compare the result of C5.0 with those of logistic regression model(LRM), multivariate discriminant analysis(MDA) model. The result shows C5.0 outperforms other models in the predictability. Based on the result, this study suggests a way of setting marketing strategy and of developing online auto insurance business.
Keywords
Decision Tree(DT); Online Auto Insurance; Customer Churning; Logistic Regression Model(LRM); Multivariate Discriminant Analysis(MDA);
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