Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms  

作　　者：Khulood Ebrah Selma Elnasir 

机构地区：[1]Department of Information Technology,International University of Africa,Khartoum,Sudan [2]Department of Computer Science,International University of Africa,Khartoum,Sudan

出　　处：《Journal of Computer and Communications》2019年第11期33-53,共21页电脑和通信（英文）

摘　　要：Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effective plans for customer retention since the cost of acquiring a new customer is much higher than the cost of retaining the existing one. In this paper, three machine learning algorithms have been used to predict churn namely, Na?ve Bayes, SVM and decision trees using two benchmark datasets IBM Watson dataset, which consist of 7033 observations, 21 attributes and cell2cell dataset that contains 71,047 observations and 57 attributes. The models’ performance has been measured by the area under the curve (AUC) and they scored 0.82, 0.87, 0.77 respectively for IBM dataset and 0.98, 0.99, 0.98 respectively for cell2cell dataset. The proposed models also obtained better accuracy than the previous studies using the same datasets.Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effective plans for customer retention since the cost of acquiring a new customer is much higher than the cost of retaining the existing one. In this paper, three machine learning algorithms have been used to predict churn namely, Na?ve Bayes, SVM and decision trees using two benchmark datasets IBM Watson dataset, which consist of 7033 observations, 21 attributes and cell2cell dataset that contains 71,047 observations and 57 attributes. The models’ performance has been measured by the area under the curve (AUC) and they scored 0.82, 0.87, 0.77 respectively for IBM dataset and 0.98, 0.99, 0.98 respectively for cell2cell dataset. The proposed models also obtained better accuracy than the previous studies using the same datasets.

关 键 词：CHURN Prediction TELECOMMUNICATION Modeling Analysis SVM Na?ve BAYES DECISION Trees Cell2cell IBM 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]