Abstract
This study aims to predict customer defection in the growing market of the cloud software industry. Using the original unstructured data of a company, we propose a procedure to identify the actual defection condition (i.e., whether the customer is defecting from the company or merely stopped using a current product to up/downgrade it) and to produce a measure of customer loyalty by compiling the number of customers’ purchases and renewals. Based on the results, we investigated important variables for classifying defecting customers using a random forest and built a prediction model using a decision tree. The final results indicate that defecting customers are mainly characterized by their loyalty and their number of total payments.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Colombus, L.: Predicting Enterprise Cloud Computing Growth. Forbes (April 9, 2013), http://www.forbes.com/sites/louiscolumbus/2013/09/04/predicting-enterprise-cloud-computing-growth/ (accessed July 20, 2014)
Huang, B.Q., Kechadi, M.-T., Buckley, B.: Customer Churn Prediction for Broadband Internet Services. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 229–243. Springer, Heidelberg (2009)
Wei, C., Chiu, I.: Turning telecommunications call detail to churn prediction: A data mining approach. Expert Systems with Applications 23, 103–112 (2002)
Yung, S., Yen, D., Wang, H.: Applying data mining to telecom churn management. Expert System with Applications 31, 515–524 (2006)
Tiwari, A., Roy, R., Hadden, J., Ruta, D.: Churn Prediction: Does Technology Matter. International Journal of Intelligent Systems and Technologies 1 (2006)
Prasasti, N., Ohwada, H.: Applicability of Machine-Learning Techniques in Predicting Customer Defection. In: International Symposium on Technology Management and Emerging Technologies (ISTMET 2014) (2014)
Prasasti, N., Okada, M., Kanamori, K., Ohwada, H.: 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.) ACIIDS 2014, Part II. LNCS (LNAI), vol. 8398, pp. 62–71. Springer, Heidelberg (2014)
Quinlan, J.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)
Xiong, Y., Syzmanski, D., Kihara, D.: Characterization and Prediction of Human Protein-Protein Interaction. In: Biological Data Mining and Its Applications in Healthcare, pp. 237–260 (2014)
Breiman, L.: Random Forests. Machine Learning 45, 25–32 (2001)
Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., Lawler, J.J.: Random Forest for Classification in Ecology. Ecology 88(11), 2783–2792 (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
Martono, N.P., Kanamori, K., Ohwada, H. (2014). Utilizing Customers’ Purchase and Contract Renewal Details to Predict Defection in the Cloud Software Industry. In: Kim, Y.S., Kang, B.H., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2014. Lecture Notes in Computer Science(), vol 8863. Springer, Cham. https://doi.org/10.1007/978-3-319-13332-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-13332-4_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13331-7
Online ISBN: 978-3-319-13332-4
eBook Packages: Computer ScienceComputer Science (R0)