Churn Analysis in a Romanian Telecommunications Company

Andreea Dumitrache, Monica Mihaela Maer Matei


Telecommunications is one of the sectors where the customer base plays a significant role in maintaining stable revenues, so special attention is paid to prevent their migration to other providers. Over time, businesses in the telecommunications industry have faced multiple threats of financial loss from migrating customers who want to leave their telecom service provider in exchange for other offers from competing companies. An effective prediction model of this action can not only be viewed as an insurance policy, supporting stable revenue, but also provides suggestions for database management so that potential migrant customers can benefit from personalized offers and services, depending on their profile, thus preventing their loss. The aim of this paper is to predict customers who are going to defect in a Romanian mobile telecommunications company. The churn analysis is developed for post-paid customers. We used logistic regression to predict churn and a solution based on smoothed bootstrap technique to correct for the drawbacks of imbalanced classes. In our study this procedure did not significantly improve the performance of the logistic classifier measured by AUC (Area Under the Receiver Operating Characteristic curve). So even after balancing the sample we still obtain a really reduced value of the AUC, making it difficult to correctly predict churn phenomenon on the available data set.


Churn; class imbalance; customer; telecommunications

Full Text:

View PDF


Berry, M. J. A., Linoff, G. S. (2004). Data mining techniques second edition – for marketing, sales, and customer relationship management (2nd ed.). United States of America.

Chawla, N. V., Bowyer, K. W., Hall, K. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(3), 321–357.

De Caigny, A., Coussement, K., & De Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research. European Journal of Operational Research, 269 (2), 760-772.

Shaaban, E., & Hassanien, A. E., (2016). Churn Prediction Retention Framework. International Journal of Advances in Computer Science & Its Applications, (6)1, 11- 16.

Galvan, A. R. M., & Navarro, K. R. C. (2015). Big Data Architecture for Predicting Churn Risk in Mobile Phone Companies. In Information Management and Big Data (pp. 120-132). Springer.

Hassouna, M., Tarhini, A., Elyas, T., & AbouTrab, M. S. (2016). Customer Churn in Mobile Markets a Comparison of Techniques. arXiv preprint arXiv:1607.07792.

Huang, B., Kechadi, M. T., & Buckley, B. (2012). Customer churn prediction in telecommunications. Expert Systems with Applications, 39(1), 1414-1425.

Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecom churn management. Expert Systems with Applications, 31(3), 515-524, doi10.1016/j.eswa.2005.09.080.

Keller, K. L., & Kotler, P. (2016). Marketing management. Pearson.

Kisioglu, P., & Topcu, Y. I. (2011). Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey. Expert Systems with Applications, 38(6), 7151-7157.

Lunardon, N., Menardi, G., & Torelli, N. (2014). ROSE: A Package for Binary Imbalanced Learning. The R Journal, 6(1),79-89 DOI: 10.32614/RJ-2014- 008

Menardi, G., & Torelli, N. (2014). Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery, 28(1), 92-122.

Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553.

Zhao, Y., Li, B., Li, X., Liu, W., & Ren, S. (2005). Customer churn prediction using improved one-class support vector machine. In International Conference on Advanced Data Mining and Applications (pp. 300-306). Springer.

Zhu, B., Baesens, B., Backiel, A. E., & vanden Broucke, S. K. (2018). Benchmarking sampling techniques for imbalance learning in churn prediction. Journal of the Operational Research Society, 69(1), 49-65.



  • There are currently no refbacks.

Copyright (c) 2019 The Authors & LUMEN Publishing House

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright © Postmodern Openings | A LUMEN Peer Reviewed Gold Open Access Journal |

Journal covered in | Web of Science (WOS); EBSCO; ERIH+; Google Scholar; Index Copernicus; Ideas RePeC; Econpapers; Socionet; CEEOL; Ulrich ProQuest; Cabell, Journalseek; Scipio; Philpapers; SHERPA/RoMEO repositories; KVK; WorldCat; CrossRef; J-GATE