Skip to main content
SpringerLink
Log in
Book cover

Advances in Machine Learning and Computational Intelligence pp 137–156Cite as

Churn Prediction and Retention in Banking, Telecom and IT Sectors Using Machine Learning Techniques

Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

Customer churn prevention is one of the deciding factors when it comes to maximizing the revenues of any organization. Also known as customer attrition, it occurs when customers stop using the products or services of a company. Through our paper, we are predicting customer churn beforehand so that proper customer retention steps can be taken with the help of exploratory data analysis and to make customized offers for the targets. For the churn prediction, our implementation consists of comparative analysis of four algorithmic models, namely logistic regression, random forest, SVM and XGBoost, on three different domains, namely banking, telecom and IT. The purpose of doing this comparative analysis is that there are not many research works which compare the performance of various algorithms in different domains. We also develop various retention strategies with the help of exploratory data analysis.

Keywords

  • Churn
  • Retention
  • Prediction
  • Logistic regression
  • Random forest
  • SVM
  • XGBoost
  • Telecom
  • Banking
  • IT

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Z. Can, E. Albey, Churn prediction for mobile prepaid subscribers, in DATA (2017), pp. 67–74

    Google Scholar 

  2. S. Höppner, E. Stripling, B. Baesens, S. vanden Broucke, T. Verdonck, Profit driven decision trees for churn prediction. Eur. J. Oper. Res. (2018)

    Google Scholar 

  3. H. Faris, A hybrid swarm intelligent neural network model for customer churn prediction and identifying the influencing factors. Information 9(11), 288 (2018)

    CrossRef  Google Scholar 

  4. A. Cotter, H. Jiang, S. Wang, T. Narayan, M. Gupta, S. You, K. Sridharan, Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals (2018). arXiv:1809.04198

  5. P. Spanoudes, T. Nguyen, Deep learning in customer churn prediction: unsupervised feature learning on abstract company independent feature vectors (2017). arXiv:1703.03869

  6. Y. Yang, Z. Liu, C. Tan, F. Wu, Y. Zhuang, Y. Li, To stay or to leave: churn prediction for urban migrants in the initial period, in Proceedings of the 2018 World Wide Web Conference on World Wide Web (International World Wide Web Conferences Steering Committee, 2018), pp. 967–976

    Google Scholar 

  7. B. Lengyel, R. Di Clemente, J. Kertész, M.C. González, Spatial diffusion and churn of social media (2018). arXiv:1804.01349

  8. Z. Zhang, R. Wang, W. Zheng, S. Lan, D. Liang, H. Jin, Profit maximization analysis based on data mining and the exponential retention model assumption with respect to customer churn problems, in 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (IEEE, 2015), pp. 1093–1097

    Google Scholar 

  9. J. Semrl, A. Matei, Churn prediction model for effective gym customer retention, in 2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) (IEEE, 2017), pp. 1–3

    Google Scholar 

  10. D.F. Benoit, D. Van den Poel, Improving customer retention in financial services using kinship network information. Expert Syst. Appl. 39(13), 11435–11442 (2012)

    CrossRef  Google Scholar 

  11. G. Nie, G. Wang, P. Zhang, Y. Tian, Y. Shi, Finding the hidden pattern of credit card holder’s churn: a case of china, in International Conference on Computational Science (Springer, Berlin, Heidelberg, 2009), pp. 561–569

    Google Scholar 

  12. J. Zhao, X.H. Dang, Bank customer churn prediction based on support vector machine: taking a commercial bank’s VIP customer churn as the example, in 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (IEEE, 2008), pp. 1–4

    Google Scholar 

  13. M. Szmydt, Predicting customer churn in electronic banking, in International Conference on Business Information Systems (Springer, Cham, 2018), pp. 687–696)

    Google Scholar 

  14. Y. Chen, Y.R. Gel, V. Lyubchich, T. Winship, Deep ensemble classifiers and peer effects analysis for churn forecasting in retail banking, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (Springer, Cham, 2018), pp. 373–385)

    Google Scholar 

  15. D.A. Kumar, V. Ravi, Predicting credit card customer churn in banks using data mining. Int. J. Data Anal. Tech. Strat. 1(1), 4–28 (2008)

    CrossRef  Google Scholar 

  16. C. Abbet, M. M’hamdi, A. Giannakopoulos, R. West, A. Hossmann, M. Baeriswyl, C. Musat, Churn intent detection in multilingual chatbot conversations and social media (2018). arXiv:1808.08432

Download references

Author information

Authors and Affiliations

  1. VIT University, Vellore, Tamil Nadu, India

    Himani Jain, Garima Yadav & R. Manoov

Authors
  1. Himani Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Garima Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Manoov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Manoov .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Prof. Srikanta Patnaik

  2. Simulation and Modelling, School of Science and Technology, Middlesex University, London, UK

    Prof. Xin-She Yang

  3. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Prof. Ishwar K. Sethi

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jain, H., Yadav, G., Manoov, R. (2021). Churn Prediction and Retention in Banking, Telecom and IT Sectors Using Machine Learning Techniques. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5243-4_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5242-7

  • Online ISBN: 978-981-15-5243-4

  • eBook Packages: EngineeringEngineering (R0)

search

Navigation