{"title":"基于贝叶斯网络的客户流失分析模型","authors":"Peng Sun, Xin Guo, Yunpeng Zhang, Zi-yan Wu","doi":"10.1109/CIS.2013.63","DOIUrl":null,"url":null,"abstract":"A customer churn analytical model based Bayesian network is built for prediction of customer churn. We propose Bayesian Network approaches to predict churn motivation, mining the result in churn characters in order to help decision-making manager formulate corresponding detainment strategy. Experimental results show that classification performance of both methods is resultful.","PeriodicalId":294223,"journal":{"name":"2013 Ninth International Conference on Computational Intelligence and Security","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Analytical Model of Customer Churn Based on Bayesian Network\",\"authors\":\"Peng Sun, Xin Guo, Yunpeng Zhang, Zi-yan Wu\",\"doi\":\"10.1109/CIS.2013.63\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A customer churn analytical model based Bayesian network is built for prediction of customer churn. We propose Bayesian Network approaches to predict churn motivation, mining the result in churn characters in order to help decision-making manager formulate corresponding detainment strategy. Experimental results show that classification performance of both methods is resultful.\",\"PeriodicalId\":294223,\"journal\":{\"name\":\"2013 Ninth International Conference on Computational Intelligence and Security\",\"volume\":\"82 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Ninth International Conference on Computational Intelligence and Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIS.2013.63\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Ninth International Conference on Computational Intelligence and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS.2013.63","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analytical Model of Customer Churn Based on Bayesian Network
A customer churn analytical model based Bayesian network is built for prediction of customer churn. We propose Bayesian Network approaches to predict churn motivation, mining the result in churn characters in order to help decision-making manager formulate corresponding detainment strategy. Experimental results show that classification performance of both methods is resultful.