A Review On Machine Learning Methods For Over-The-Top (OTT) Platforms Customer Churn Prediction

Authors

  • Bathula Prasanna Kumar
  • Edara Sreenivasa Reddy

Keywords:

Churn Prediction, Machine Learning, Decision Making, Customer Defection, Marketing Analytics

Abstract

Market deregulation and globalization have significantly heightened competition across industries, resulting in evolving market dynamics and an increase in customer churn. For OTT service providers, accurately predicting and addressing churn is crucial for retaining subscribers and ensuring sustainable growth. This study examines 185 published articles from 2018 to 2024, focusing on customer churn prediction through machine learning techniques. In contrast to previous reviews that often explore isolated aspects of churn prediction, this research places particular emphasis on OTT-related sectors, including telecommunications, finance, and online streaming platforms. It highlights the importance of creating new datasets and developing predictive models tailored to these industries. The findings reveal a notable research gap regarding the profitability aspect of churn prediction models. To address this, the study advocates for the integration of profit-based evaluation metrics, enabling better decision-making, improved subscriber retention strategies, and enhanced profitability. This comprehensive approach underscores the importance of aligning predictive techniques with actionable business outcomes in the OTT churn landscape.

Downloads

Download data is not yet available.

References

K. Khadka and S. Maharjan, ‘‘Customer satisfaction and customer loyalty,’’ Centria Univ. Appl. Sci. Pietarsaari, vol. 1, no. 10, pp. 58–64,2017.

J. Ahn, J. Hwang, D. Kim, H. Choi, and S. Kang, ‘‘A survey on churn analysis in various Bus. Domains,’’ IEEE Access, vol. 8, pp. 220816–220839, 2020.

J. N. Sheth and C. Uslay, ‘‘Creating enduring customer value,’’ J. Creating Value, vol. 8, no. 2, pp. 241–252, Nov. 2022.

M.A.H. Farquad, Vadlamani Ravi, S. Bapi Raju “Churn prediction using comprehensible support vector machine: An analytical CRM application”, Applied Soft Computing 19 (2014) 31–40.

M. Alizadeh, D. S. Zadeh, B. Moshiri, and A. Montazeri, ‘‘Development of a customer churn model for banking industry based on hard and soft data fusion,’’ IEEE Access, vol. 11, pp. 29759–29768, 2023.

N. Edwine, W. Wang, W. Song, and D. Ssebuggwawo, ‘‘Detecting the risk of customer churn in telecom sector: A comparative study,’’ Math. Problems Eng., vol. 2022, pp. 1–16, Jul. 2022.

L. C. Cheng, C.-C. Wu, and C.-Y. Chen, ‘‘Behavior analysis of customer churn for a customer relationship system: An empirical case study,’’ J. Global Inf. Manage., vol. 27, no. 1, pp. 111–127, Jan. 2019.

A. Somosi, A. Stiassny, K. Kolos, and L. Warlop, ‘‘Customer defection due to service elimination and post-elimination customer behavior: An empirical investigation in telecommunications,’’ Int. J. Res. Marketing, vol. 38, no. 4, pp. 915–934, Dec. 2021.

W. Soliman and T. Rinta-Kahila, ‘‘Toward a refined conceptualization of is discontinuance: Reflection on the past and a way forward,’’ Inf. Manage., vol. 57, no. 2, 2020, Art. no. 103167.

H. Sebastian and R. Wagh, ‘‘Churn analysis in telecommunication using logistic regression,’’ Oriental J. Comput. Sci. Technol., vol. 10, no. 1, pp. 207–212, Mar. 2017.

H. Lee, Y. Lee, H. Cho, K. Im, Y.S. Kim, “Mining churning behaviors and developing retention strategies based on a partial least squares (PLS) model”, Decision Support System 52 (2011) 207–216.

H. Jain, A. Khunteta, and S. Srivastava, ‘‘Telecom churn prediction and used techniques, datasets and performance measures: A review,’’ Telecommun. Syst., vol. 76, no. 4, pp. 613–630, Apr. 2021.

Kriti, “Customer churn: A study of factors affecting customer churn using machine learning” Iowa State University Capstones, Theses and Dissertations, March 2019.

Essam Abou el Kassem, Shereen Ali Hussein, Alaa Mostafa Abdelrahman, Fahad Kamal Alsheref. “Customer Churn Prediction Model and Identifying Features to Increase Customer Retention based on User Generated Content” IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, November 5, 2020.

I Praveen Lalwani, Manas Kumar Mishra, Jasroop Singh Chadha, Pratyush Sethi. “Customer churn prediction system: a machine learning approach” in Springer.

Saran Kumar A., Chandrakala D. “A Survey on Customer Churn Prediction using Machine Learning Techniques” International Journal of Computer Applications (0975 – 8887) Volume 154 – No.10, November 2016.

Pradeep B, Sushmitha Vishwanath Rao, Swati M Puranik, Akshay Hegde “Analysis of Customer Churn prediction in Logistic Industry using Machine Learning” International Journal of Scientific and Research Publications, Volume 7, Issue 11, November 2017 ISSN 2250-3153.

Koen W. De Bock, Dirk Van den Poel, “Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models”, Expert Systems with Applications 39 (2012) 6816–6826.

S. H. Iranmanesh, M. Hamid, M. Bastan, G. Hamed Shakouri, and M. M. Nasiri, ‘‘Customer churn prediction using artificial neural network: An analytical CRM application,’’ in Proc. Int. Conf. Ind. Eng. Oper. Manag., 2019, pp. 23–26.

J. Bojei, C. C. Julian, C. A. B. C.Wel, and Z. U. Ahmed, ‘‘The empirical link between relationship marketing tools and consumer retention in retail marketing,’’ J. Consum. Behaviour, vol. 12, no. 3, pp. 171–181, May 2013.

K. Ljubičić, A. Merćep, and Z. Kostanjčar, ‘‘Churn prediction methods based on mutual customer interdependence,’’ J. Comput. Sci., vol. 67, Mar. 2023, Art. no. 101940.

D. L. García, À. Nebot, and A. Vellido, ‘‘Intelligent data analysis approaches to churn as a business problem: A survey,’’ Knowl. Inf. Syst., vol. 51, no. 3, pp. 719–774, Jun. 2017.

J. Kandampully, T. Zhang, and A. Bilgihan, ‘‘Customer loyalty: A review and future directions with a special focus on the hospitality industry,’’ Int. J. Contemp. Hospitality Manage., vol. 27, no. 3, pp. 379–414, Apr. 2015.

H.Wetzel, C. Haenel, and A. C. Hess, ‘‘Handle with care! Service contract termination as a service delivery task,’’ Eur. J. Marketing, vol. 56, no. 12, pp. 3169–3196, Nov. 2022.

D. L. García, A. V. Alcacena, and M. À. Castells, ‘‘Customer continuity management as a foundation for churn data mining,’’ 2007.

D. Ismanova, ‘‘Students’ loyalty in higher education: The mediating effect of satisfaction, trust, commitment on student loyalty to alma mater,’’ Manage. Sci. Lett., vol. 9, no. 8, pp. 1161–1168, 2019.

H. Ribeiro, B. Barbosa, A. C. Moreira, and R. G. Rodrigues, ‘‘Determinants of churn in telecommunication services: A systematic literature review,’’ Manage. Rev. Quart., vol. 1, pp. 1–38, Feb. 2023.

A. Ben, ‘‘Enhanced churn prediction in the telecommunication industry,’’ SSRN Electron. J., vol. 8, no. 2, pp. 6–15, 2020.

P. S. H. Tan, Y. O. Choong, and I.-C. Chen, ‘‘The effect of service quality on behavioral intention: The mediating role of student satisfaction and switching barriers in private universities,’’ J. Appl. Res. Higher Educ., vol. 14, no. 4, pp. 1394–1413, Dec. 2022.

E. Ghazali, B. Nguyen, D. S. Mutum, and A. A. Mohd-Any, ‘‘Constructing online switching barriers: Examining the effects of switching costs and alternative attractiveness on e-store loyalty in online pure-play retailers,’’ Electron. Markets, vol. 26, no. 2, pp. 157–171, May 2016.

Amiya, Ranjan, Panda., Manoj, Kumar, Mishra., Dvij, Kalsi., Kaushik, Jyoti, Bhuyan., Soumyadeep, Saha., Kaustubh, Jyoti, Bhuyan. (2024). 1. Classification of Customer Churning based on OTT platform data. doi: 10.1109/esic60604.2024.10481640.

Tasneem, Qaraeen., Nora, Qaqour., Sameh, Taqatqa. (2024). 8. Predictive Customer Analytics: Machine Learning for Churn Prediction and Retention. doi: 10.59994/ajbtme.2024.1.11.

Victor, Chang., Karl, Hall., Qianwen, Xu., Folakemi, Ololade, Amao., Meghana, Ashok, Ganatra., Vladlena, Benson. (2024). 6. Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models. Algorithms, doi: 10.3390/a17060231.

Muteb, Alotaibi., Mohd, Anul, Haq. (2024). 10. Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods. Engineering, Technology & Applied Science Research, doi: 10.48084/etasr.7480

Opara, John, Ogbonna., Gilbert, I.O., Aimufua., Muhammad, Umar, Abdullahi., Suleiman, Abubakar. (2024). 17. Churn Prediction in Telecommunication Industry: A Comparative Analysis of Boosting Algorithms. doi: 10.4314/dujopas.v10i1b.33.

Xiayu, Li., Tianyi, Yang., Xu, Zhan., Yanxin, Shi., Huixiang, Li. (2024). 18. Utilizing Data Science and AI for Customer Churn Prediction in Marketing. Journal of Theory and Practice of Engineering Science, doi: 10.53469/jtpes.2024.04(05).10.

Muhammad, Maulana, Sidiq., Dyah, Anggraini. (2023). 9. Analysis and Classification of Customer Churn Using Machine Learning Models. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), doi: 10.29207/resti.v7i6.4933.

Manal, Loukili., F., Messaoudi., Raouya, El, Youbi. (2023). 7. Implementation of Machine Learning Algorithms for Customer Churn Prediction. doi: 10.61186/jist.34208.11.43.196.

Daisy, Reneilwe, Chabumba., Ashwini, Jadhav., Ritesh, Ajoodha. (2021). 34. Predicting Telecommunication Customer Churn using Machine Learning Techniques. doi: 10.1201/9781003202240-98

Joydeb, Kumar, Sana., Mohammad, Zoynul, Abedin., M., Sohel, Rahman., Md., Saidur, Rahman. (2022). 37. A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection. PLOS ONE, doi: 10.1371/journal.pone.0278095

Teuku, Rizky, Noviandy., Ghalieb, Mutig, Idroes., Irsan, Hardi., Mohd, Afjal., Samrat, Ray. (2024). 44. A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry. Infolitika Journal of Data Science, doi: 10.60084/ijds.v2i1.199.

Wee, How, Khoh., Ying, Han, Pang., Shih, Yin, Ooi. (2023). 48. Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning. Sustainability, doi: 10.3390/su15118631

Liwei, Chen. (2023). 16. Machine Learning-based Analysis and Prediction of Telecoms Customer Churn. doi: 10.1109/icaml60083.2023.00032.

Navienkumar, R., Lalithamani, N. (2023). 25. Machine Learning Methods for Predictive Customer Churn Analysis in the Telecom Industry. doi: 10.1109/icccnt56998.2023.10306395.

Angela, Yi, Wen, Chong., Khai, Wah, Khaw., Wai, Chung, Yeong., Wen, Xu, Chuah. (2023). 47. Customer Churn Prediction of Telecom Company Using Machine Learning Algorithms. JOURNAL OF SOFT COMPUTING AND DATA MINING, doi: 10.30880/jscdm.2023.04.02.001.

Abdelrahim, Kasem, Ahmad., Assef, Jafar., Kadan, Aljoumaa. (2019) Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, doi: 10.1186/S40537-019-0191-6.

Alae, Chouiekh., El, Hassane, Ibn, El, Haj. (2020). 66. Deep Convolutional Neural Networks for Customer Churn Prediction Analysis. International Journal of Cognitive Informatics and Natural Intelligence, doi: 10.4018/IJCINI.2020010101.

Shreyas, Rajesh, Labhsetwar. (2020). 74. Predictive analysis of customer churn in telecom industry using supervised learning. doi: 10.21917/IJSC.2020.0291.

Sharmila, Kishor, Wagh., Aishwarya, A., Andhale., Kishor, S., Wagh., J., Pansare., Sarita, P., Ambadekar., S., H., Gawande. (2023). 76. Customer Churn Prediction in Telecom Sector using Machine Learning Techniques. Results in control and optimization, doi: 10.1016/j.rico.2023.100342.

S. Mitrović and J. De Weerdt, ‘‘Churn modeling with probabilistic meta paths-based representation learning,’’ Inf. Process. Manage., vol. 57, no. 2, Mar. 2020, Art. no. 102052.

A. Dursun and M. Caber, ‘‘Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis,’’ Tourism Manage. Perspect., vol. 18, pp. 153–160, Apr. 2016.

K. Georgiou and A. Chasapis, ‘‘Novel time slicing approach for customer defection models in e-commerce: A case study,’’ Data Sci. Manage., vol. 5, no. 3, pp. 149–162, Sep. 2022.

G. Mena, K. Coussement, K. W. De Bock, A. De Caigny, and S. Lessmann, ‘‘Exploiting time-varying RFM measures for customer churn prediction with deep neural networks,’’ Ann. Operations Res., vol. 1, pp. 1–23, Mar. 2023.

T. Gattermann-Itschert and U. W. Thonemann, ‘‘Proactive customer retention management in a non-contractual B2B setting based on churn prediction with random forests,’’ Ind. Marketing Manage., vol. 107, pp. 134–147, Nov. 2022.

M. Mirkovic, T. Lolic, D. Stefanovic, A. Anderla, and D. Gracanin, ‘‘Customer churn prediction in B2B non-contractual Bus. Settings using invoice data,’’ Appl. Sci., vol. 12, no. 10, p. 5001, May 2022.

B. Janssens, M. Bogaert, A. Bagué, and D. Van den Poel, ‘‘B2Boost: Instance-dependent profit-driven modelling of B2B churn,’’ Ann. Operations Res., vol. 1, pp. 1–27, Mar. 2022.

S. Maldonado, G. Domínguez, D. Olaya, andW. Verbeke, ‘‘Profit-driven churn prediction for the mutual fund industry: A multi segment approach,’’Omega, vol. 100, Apr. 2021, Art. no. 102380.

S. Khodabandehlou and M. Zivari Rahman, ‘‘Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior,’’ J. Syst. Inf. Technol., vol. 19, no. 1, pp. 65–93, Mar. 2017.

M. Y. Smaili and H. Hachimi, ‘‘New RFM-D classification model for improving customer analysis and response prediction,’’ Ain Shams Eng. J., vol. 14, no. 12, Dec. 2023, Art. no. 102254.

K. Chen, Y.-H. Hu, and Y.-C. Hsieh, ‘‘Predicting customer churn from valuable B2B customers in the logistics industry: A case study,’’ Inf. Syst. E-Business Manage., vol. 13, no. 3, pp. 475–494, Aug. 2015.

S. Peker, A. Kocyigit, and P. E. Eren, ‘‘LRFMP model for customer segmentation in the grocery retail industry: A case study,’’ Marketing Intell. Planning, vol. 35, no. 4, pp. 544–559, May 2017.

T. Gattermann-Itschert and U.W. Thonemann, ‘‘How training on multiple time slices improves performance in churn prediction,’’ Eur. J. Oper. Res., vol. 295, no. 2, pp. 664–674, Dec. 2021.

A. Perišic and M. Pahor, ‘‘RFM-LIR feature framework for churn prediction in the mobile games market,’’ IEEE Trans. Games, vol. 14, no. 2, pp. 126–137, Jun. 2022.

S. Mitrović, B. Baesens, W. Lemahieu, and J. De Weerdt, ‘‘On the operational efficiency of different feature types for Telco churn prediction,’’ Eur. J. Oper. Res., vol. 267, no. 3, pp. 1141–1155, Jun. 2018.

O. M. Mirza, G. Jose Moses, R. Rajender, E. Laxmi Lydia, S. Kadry, C. Me-Ead, and O. Thinnukool, ‘‘Optimal deep canonically correlated autoencoder-enabled prediction model for customer churn prediction,’’ Comput., Mater. Continua, vol. 73, no. 2, pp. 3757–3769, 2022.

Downloads

Published

2025-05-15

How to Cite

1.
Kumar BP, Reddy ES. A Review On Machine Learning Methods For Over-The-Top (OTT) Platforms Customer Churn Prediction. J Neonatal Surg [Internet]. 2025May15 [cited 2025Sep.21];14(24S):111-23. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/5905

Issue

Vol. 14 No. 24S (2025): Journal of Neonatal Surgery

Section

Original Article

License

Creative Commons License

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

You are free to:

  • Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.