Applying client churn prediction modeling on home-based care services industry

Manongdo, R; Xu, G

Applying client churn prediction modeling on home-based care services industry

Manongdo, R Xu, G

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Publication Type:: Conference Proceeding
Citation:: IEEE/ACM BESC 2016 - Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, 2017
Issue Date:: 2017-01-03

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Field	Value	Language
dc.contributor.author	Manongdo, R	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2017-01-03	en_US
dc.identifier.citation	IEEE/ACM BESC 2016 - Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, 2017	en_US
dc.identifier.isbn	9781509061648	en_US
dc.identifier.uri	http://hdl.handle.net/10453/103110
dc.description.abstract	© 2016 IEEE. 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.	en_US
dc.relation.ispartof	IEEE/ACM BESC 2016 - Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing	en_US
dc.relation.isbasedon	10.1109/BESC.2016.7804503	en_US
dc.title	Applying client churn prediction modeling on home-based care services industry	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/103110