Comparison of machine learning methods for estimating customer churn in the telecommunication industry

Research Article
Open access

Comparison of machine learning methods for estimating customer churn in the telecommunication industry

Thomas Tang 1*
  • 1 Stony Brook University    
  • *corresponding author thomas.tang@stonybrook.edu
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/17/20230928
ACE Vol.17
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-025-7
ISBN (Online): 978-1-83558-026-4

Abstract

With the rising competition in business, particularly in the telecommunications industry, there has been a growing emphasis on churn prediction. This is attributed to the higher cost involved in attracting new customers than retaining the remaining ones. In telecom churn analysis, the primary goal is to accurately estimate churn behavior by identifying customers who are at risk of leaving. Another objective is to determine the primary reasons for customer churn. Manually predicting the churn in telecommunications is expensive, tedious, and time-consuming. To relieve the burden, machine learning algorithms are introduced to tackle this problem. This article examines several machine learning algorithms for predicting customer churn, including Random Forest, Decision Tree, and Naive Bayes. By constructing and comparing these classification models, the effectiveness of these algorithms is demonstrated. The aim of this work is to examine and compare the effectiveness of various models according to accuracy, which can be applied in real-life scenarios. The result shows that Naive Bayes outperforms the other models.

Keywords:

machine learning, customer churn prediction, random forest, decision tree, Naïve Bayes

Tang,T. (2023). Comparison of machine learning methods for estimating customer churn in the telecommunication industry. Applied and Computational Engineering,17,157-162.
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References

[1]. Budianto, A. (2019). Customer loyalty: quality of service. Journal of management review, 3(1), 299-305.

[2]. Khan, R. U., Salamzadeh, Y., Iqbal, Q., & Yang, S. (2022). The impact of customer relationship management and company reputation on customer loyalty: The mediating role of customer satisfaction. Journal of Relationship Marketing, 21(1), 1-26.

[3]. Jain, H., Khunteta, A., & Srivastava, S. (2021). Telecom churn prediction and used techniques, datasets and performance measures: a review. Telecommunication Systems, 76, 613-630.

[4]. Xu, T., Ma, Y., & Kim, K. (2021). Telecom churn prediction system based on ensemble learning using feature grouping. Applied Sciences, 11(11), 4742.

[5]. 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.

[6]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

[7]. Umayaparvathi, V., & Iyakutti, K. (2016). A survey on customer churn prediction in telecom industry: Datasets, methods and metrics. International Research Journal of Engineering and Technology (IRJET), 3(04), 1065-1070.

[8]. Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society, 18(6), 275-285.

[9]. Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31-39.

[10]. Webb, G. I., Keogh, E., & Miikkulainen, R. (2010). Naïve Bayes. Encyclopedia of machine learning, 15, 713-714.

Cite this article

Tang,T. (2023). Comparison of machine learning methods for estimating customer churn in the telecommunication industry. Applied and Computational Engineering,17,157-162.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-025-7(Print) / 978-1-83558-026-4(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.17
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Budianto, A. (2019). Customer loyalty: quality of service. Journal of management review, 3(1), 299-305.

[2]. Khan, R. U., Salamzadeh, Y., Iqbal, Q., & Yang, S. (2022). The impact of customer relationship management and company reputation on customer loyalty: The mediating role of customer satisfaction. Journal of Relationship Marketing, 21(1), 1-26.

[3]. Jain, H., Khunteta, A., & Srivastava, S. (2021). Telecom churn prediction and used techniques, datasets and performance measures: a review. Telecommunication Systems, 76, 613-630.

[4]. Xu, T., Ma, Y., & Kim, K. (2021). Telecom churn prediction system based on ensemble learning using feature grouping. Applied Sciences, 11(11), 4742.

[5]. 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.

[6]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

[7]. Umayaparvathi, V., & Iyakutti, K. (2016). A survey on customer churn prediction in telecom industry: Datasets, methods and metrics. International Research Journal of Engineering and Technology (IRJET), 3(04), 1065-1070.

[8]. Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society, 18(6), 275-285.

[9]. Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31-39.

[10]. Webb, G. I., Keogh, E., & Miikkulainen, R. (2010). Naïve Bayes. Encyclopedia of machine learning, 15, 713-714.

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