客户流失预测模型在居家护理服务行业的应用

Raul Manongdo, Guandong Xu
{"title":"客户流失预测模型在居家护理服务行业的应用","authors":"Raul Manongdo, Guandong Xu","doi":"10.1109/BESC.2016.7804503","DOIUrl":null,"url":null,"abstract":"Client churn prediction model is widely acknowledged as an effective way of realizing customer life-time value especially in service-oriented industries and under a competitive business environment. Churn model allows targeting of clients for retention campaigns and is a critical component of customer relationship management(CRM) and business intelligence systems. There are numerous statistical models and techniques applied successfully on data mining projects for various industries. While there is literature for prediction modeling on hospital health care services, non-exist for home-based care services. In this study, logistic regression, random forest and C5.0 decision tree were the models used in building a binary client churn classifier for a home-based care services company based in Australia. All models yielded prediction accuracies over 90% with tree based classifiers marginally higher and C5.0 model found to be suitable for use in this industry. This study also showed that existing client satisfaction measures currently in use by the company does not adequately contribute to churn analysis.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Applying client churn prediction modeling on home-based care services industry\",\"authors\":\"Raul Manongdo, Guandong Xu\",\"doi\":\"10.1109/BESC.2016.7804503\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Client churn prediction model is widely acknowledged as an effective way of realizing customer life-time value especially in service-oriented industries and under a competitive business environment. Churn model allows targeting of clients for retention campaigns and is a critical component of customer relationship management(CRM) and business intelligence systems. There are numerous statistical models and techniques applied successfully on data mining projects for various industries. While there is literature for prediction modeling on hospital health care services, non-exist for home-based care services. In this study, logistic regression, random forest and C5.0 decision tree were the models used in building a binary client churn classifier for a home-based care services company based in Australia. All models yielded prediction accuracies over 90% with tree based classifiers marginally higher and C5.0 model found to be suitable for use in this industry. This study also showed that existing client satisfaction measures currently in use by the company does not adequately contribute to churn analysis.\",\"PeriodicalId\":225942,\"journal\":{\"name\":\"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)\",\"volume\":\"2016 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BESC.2016.7804503\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BESC.2016.7804503","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

客户流失预测模型被广泛认为是实现客户生命周期价值的有效方法,特别是在服务型行业和竞争激烈的商业环境中。流失模型允许针对客户进行保留活动,并且是客户关系管理(CRM)和商业智能系统的关键组成部分。有许多统计模型和技术成功地应用于各行各业的数据挖掘项目。虽然有文献对医院卫生保健服务进行预测建模,但没有对家庭护理服务进行预测建模。本研究采用logistic回归、随机森林和C5.0决策树模型,为澳大利亚一家居家护理服务公司构建二元客户流失分类器。所有模型的预测精度都超过90%,基于树的分类器略高,C5.0模型适合该行业使用。该研究还表明,公司目前使用的现有客户满意度措施并不能充分有助于客户流失分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying client churn prediction modeling on home-based care services industry
Client churn prediction model is widely acknowledged as an effective way of realizing customer life-time value especially in service-oriented industries and under a competitive business environment. Churn model allows targeting of clients for retention campaigns and is a critical component of customer relationship management(CRM) and business intelligence systems. There are numerous statistical models and techniques applied successfully on data mining projects for various industries. While there is literature for prediction modeling on hospital health care services, non-exist for home-based care services. In this study, logistic regression, random forest and C5.0 decision tree were the models used in building a binary client churn classifier for a home-based care services company based in Australia. All models yielded prediction accuracies over 90% with tree based classifiers marginally higher and C5.0 model found to be suitable for use in this industry. This study also showed that existing client satisfaction measures currently in use by the company does not adequately contribute to churn analysis.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信