Applying client churn prediction modelling on home-based care services industry
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Client churn prediction is widely acknowledged as a cost-effective way of realising customer life-time value especially for service-oriented industries and operating under a competitive business environment. Churn prediction model allows identification of clients as targets for retention campaigns. While there are for hospital-based care services, the author was unable to find application for home-based care services. The objective of the study therefore is to develop an initial client churn prediction model in the context of home-based care services industry at Australia that can be adopted and subsequently enhanced. Real industry data as provided by a local and sizeable home-based care services provider was used in this study. For developing the model, various predictive models such as logistic regression, tree-based C5.0 and the ensemble Random Forest were tested. Feature selection techniques embedded in these models were integrated to identify significant and common variables in predicting a binary outcome of a client churning or not. All model evaluations yielded overall prediction accuracies over 83%. The C5.0 model, however, was chosen as its prediction accuracy was marginally better and model results were easier to understand and adopt by the case company. It was discovered that in general, clients who are enrolled in the government’s home assistance support program and with higher levels of home care needs (i.e. nursing) are more at-risk of churning. Clients enrolled in private and commercial programs are also at risk particularly those in the under-25 age group.
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