{"title":"提高客户流失预测软件的有效性","authors":"Joseph Halim, J. Vucetic","doi":"10.1109/GOCICT.2015.30","DOIUrl":null,"url":null,"abstract":"Despite the development of several churn prediction software that investigate hundreds of factors, there is still much space for improving the accuracy of churn prediction. This explorative case study research investigated the behavioral factors, economic factors, and carrier policies causing churn and revealed a new set of important factors that cause churn that should be incorporated into churn prediction models to enhance the accuracy of churn prediction software.","PeriodicalId":221523,"journal":{"name":"2015 Annual Global Online Conference on Information and Computer Technology (GOCICT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Increasing Effectiveness of Churn Prediction Software\",\"authors\":\"Joseph Halim, J. Vucetic\",\"doi\":\"10.1109/GOCICT.2015.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Despite the development of several churn prediction software that investigate hundreds of factors, there is still much space for improving the accuracy of churn prediction. This explorative case study research investigated the behavioral factors, economic factors, and carrier policies causing churn and revealed a new set of important factors that cause churn that should be incorporated into churn prediction models to enhance the accuracy of churn prediction software.\",\"PeriodicalId\":221523,\"journal\":{\"name\":\"2015 Annual Global Online Conference on Information and Computer Technology (GOCICT)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Annual Global Online Conference on Information and Computer Technology (GOCICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GOCICT.2015.30\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Annual Global Online Conference on Information and Computer Technology (GOCICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GOCICT.2015.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Increasing Effectiveness of Churn Prediction Software
Despite the development of several churn prediction software that investigate hundreds of factors, there is still much space for improving the accuracy of churn prediction. This explorative case study research investigated the behavioral factors, economic factors, and carrier policies causing churn and revealed a new set of important factors that cause churn that should be incorporated into churn prediction models to enhance the accuracy of churn prediction software.