Decision rule induction: Relieving complexity in detecting defection

Abstract

Customer attrition has become a serious problem globally, particularly in telecom service, resulting into substantial revenue decline. Attrition may result in accumulation ofdues as a resultof payment defaults. Proactive identification of potential attrite will help in retention as well as minimizing loss of revenue.For attrition detection many robust but complex algorithms are used. Depending on the severity of error, the complexity can be lessened and thus cost. Two methods of decision rules (1R& C5.0) are used to predict the attrition and predictive accuracy is judged withconfusion matrix. Comparison between models is made by sensitivity and specificity. It was found that 1R has a sensitivity of .60 against .69 for C5.0 and hence, the performance is not significantly different. It is suggested that 1R could be used instead of more complex algorithmsand also it can be adopted for benchmarking.