An Approach to Churn Prediction for Cloud Services Recommendation and User Retention

The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service providers for recommending adjustme...

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Bibliographic Details
Published inInformation (Basel) Vol. 13; no. 5; p. 227
Main Authors Saias, José, Rato, Luís, Gonçalves, Teresa
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2022
Subjects
Accuracy
Algorithms
Brand loyalty
churn
Cloud computing
Customer services
Customers
Data mining
Datasets
decision analysis
Discriminant analysis
forecasting
Machine learning
Multilayers
Neural networks
Retention
Software services
Subscriptions
Survival analysis
Variance analysis
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Summary:The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service providers for recommending adjustments in the service subscription level, both to promote rational resource consumption and to avoid CSP customer loss. A training dataset was built from real data about the customer, the subscribed service and its usage history, and it was used in a supervised machine-learning approach for prediction. Classification models were built and evaluated based on multilayer neural networks, AdaBoost and random forest algorithms. From the experiments with our dataset, the best results for a churn prediction were obtained with a random forest-based model, with 64 estimators, having 0.988 accuracy and 0.997 AUC value.
ISSN:2078-2489
2078-2489
DOI:10.3390/info13050227