{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:56:27Z","timestamp":1764053787925,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Programa Operacional Regional do Alentejo 2014\/2020"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/info13050227","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T22:20:06Z","timestamp":1651184406000},"page":"227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Approach to Churn Prediction for Cloud Services Recommendation and User Retention"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3025-0687","authenticated-orcid":false,"given":"Jos\u00e9","family":"Saias","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"},{"name":"Centro ALGORITMI, Vista Laboratory, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4492-7548","authenticated-orcid":false,"given":"Lu\u00eds","family":"Rato","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"},{"name":"Centro ALGORITMI, Vista Laboratory, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1323-0249","authenticated-orcid":false,"given":"Teresa","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"},{"name":"Centro ALGORITMI, Vista Laboratory, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","unstructured":"PayPro Global Inc (2022, January 27). Tackling SaaS Churn. Available online: http:\/\/docs.payproglobal.com\/documents\/white-papers\/PayPro-WP-Tackling-SaaS-churn.pdf."},{"key":"ref_2","unstructured":"Kapoor, A. (2022, January 18). Churn in the Telecom Industry\u2013Identifying Customers Likely to Churn and How to Retain Them. Technical Report. Available online: https:\/\/wp.nyu.edu\/adityakapoor\/."},{"key":"ref_3","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_4","unstructured":"Vivek, S. (2022, January 18). Using Linear Discriminant Analysis to Predict Customer Churn. Available online: https:\/\/www.datascience.com\/blog\/predicting-customer-churn-with-a-discriminant-analysis."},{"key":"ref_5","first-page":"8","article-title":"Computational Efficiency Analysis of Customer churn Prediction Using Spark and Caret Random Forest Classifier","volume":"8","author":"Olasehinde","year":"2018","journal-title":"Inf. Knowl. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dalvi, P.K., Khandge, S.K., Deomore, A., Bankar, A., and Kanade, V.A. (2016, January 18\u201319). Analysis of customer churn prediction in telecom industry using decision trees and Logistic Regression. Proceedings of the Symposium on Colossal Data Analysis and Networking (CDAN) 2016, Indore, India.","DOI":"10.1109\/CDAN.2016.7570883"},{"key":"ref_7","unstructured":"Jensen, C. (2020). Customer Churn Prediction: A Study of Churn Prediction Using Different Algorithem from Machine Learning. [Master\u2019s Thesis, Copenhagen Business School]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1140\/epjds\/s13688-018-0165-5","article-title":"Behavioral Attributes and Financial churn Prediction","volume":"7","author":"Kaya","year":"2018","journal-title":"Epj Data Sci."},{"key":"ref_9","unstructured":"Amuda, K., and Adesesan, A. (2019). Customers churn Prediction in Financial Institution Using Artificial Neural Network. arXiv."},{"key":"ref_10","unstructured":"\u00c5man, R. (2017). Understanding When Customers Leave: Defining Customer Health and How It Correlates with Software Usage. [Master\u2019s Thesis, Uppsala University]."},{"key":"ref_11","unstructured":"Sergue, M. (2020). Customer Churn Analysis and Prediction Using Machine Learning for a B2B SaaS Company, School of Engineering Sciences, KTH Royal Institute of Technology. Degree Project in Engineering Physics."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12547","DOI":"10.1016\/j.eswa.2009.05.032","article-title":"Customer churn prediction by hybrid neural networks","volume":"36","author":"Tsai","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1016\/j.asoc.2020.106164","article-title":"From Big Data to business analytics: The case study of churn prediction","volume":"90","author":"Zdravevski","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"458","DOI":"10.3390\/jtaer17020024","article-title":"B2C E-Commerce Customer churn Prediction Based on K-Means and SVM","volume":"17","author":"Xiahou","year":"2022","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s10115-016-0995-z","article-title":"Intelligent Data Analysis Approaches to churn as a Business Problem: A Survey","volume":"51","author":"Nebot","year":"2017","journal-title":"Knowl. Inf. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1145\/1656274.1656280","article-title":"KNIME\u2014The Konstanz information miner","volume":"11","author":"Berthold","year":"2009","journal-title":"Acm Sigkdd Explor. Newsl."},{"key":"ref_17","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Multi-class AdaBoost","volume":"2","author":"Zhu","year":"2009","journal-title":"Stat. Interface"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/ICNN.1993.298623","article-title":"A direct adaptive method for faster backpropagation learning: The RPROP algorithm","volume":"Volume 16","author":"Riedmiller","year":"1993","journal-title":"Proceedings of the IEEE International Conference on Neural Networks (ICNN)"},{"key":"ref_20","unstructured":"Raschka, S. (2018). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. arXiv.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_22","unstructured":"Saias, J., Maia, M., Rato, L., and Gon\u00e7alves, T. (2018). Estudo Sobre Modelos Preditivos Para Churn, Universidade de \u00c9vora. Relat\u00f3rio T\u00e9cnico do Projeto APRA-CP."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/5\/227\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:02:55Z","timestamp":1760137375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/5\/227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,28]]},"references-count":22,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["info13050227"],"URL":"https:\/\/doi.org\/10.3390\/info13050227","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2022,4,28]]}}}