Abstract
This paper proposes an estimation of Customer Lifetime Value (CLV) for a cloud-based software company by using machine learning techniques. The purpose of this study is twofold. We classify the customers of one cloud-based software company by using two classifications methods: C4.5 and a support vector machine (SVM). We use machine learning primarily to estimate the frequency distribution of the customer defection possibility. The result shows that both the C4.5 and SVM classifications perform well, and by obtaining frequency distributions of the defection possibility, we can predict the number of customers defecting and the number of customers retained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kumar, V.: Uses, misuses, and future advance. In: Grover, R., Vriens, M. (eds.) The Handbook of Market Research, pp. 602–628. Sage Publication, California (2005)
Flordal, P., Friberg, J., Berling, P.: Modeling Customer Lifetime Value in the Telecom Industry
Hwang, H., Jung, T., Suh, E.: An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert Systems with Applications 26(2), 181–188 (2004), doi:10.1016/S0957-4174(03)00133-7
Khan, A.A.: Applying Data Mining to Customer Churn Prediction in an Internet Service Provider 9(7), 8–14 (2010)
What are Cloud-Based Services? (2012), http://www.colorado.edu/oit/node/7532 (retrieved)
Abubakr, T.: Cloud app vs. web app: Understanding the differences (2012), http://www.techrepublic.com/blog/the-enterprise-cloud/cloud-app-vs-web-app-understanding-the-differences/5478/ (retrieved)
Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Data Mining Researchers (2003)
Szarvas, G., Farkas, R., Kocsor, A.: A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds.) DS 2006. LNCS (LNAI), vol. 4265, pp. 267–278. Springer, Heidelberg (2006)
Huang, J., Lu, J., Ling, C.X.: Comparing naive Bayes, decision trees, and SVM with AUC and accuracy. In: Third IEEE International Conference on Data Mining, pp. 553–556 (2003), doi:10.1109/ICDM.2003.1250975
Kotsiantis, S.B.: Supervised Machine Learning: A Review of Classification Techniques 31, 249–268 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Prasasti, N., Okada, M., Kanamori, K., Ohwada, H. (2014). Customer Lifetime Value and Defection Possibility Prediction Model Using Machine Learning: An Application to a Cloud-Based Software Company. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-05458-2_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05457-5
Online ISBN: 978-3-319-05458-2
eBook Packages: Computer ScienceComputer Science (R0)