A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case

Yıl 2025, Cilt: 9 Sayı: 1, 55 - 73, 30.06.2025

Muhammet Sinan Başarslan , Aslıhan Ünal , Fatih Kayaalp

https://doi.org/10.26650/acin.1584030

Öz

Customer churn is an important issue in increasing both the long- and short-term revenues. If companies identify customers’ churn behavior, they can prevent churn, ensure customer loyalty, and, in turn, gain better financial returns. The telecommunications sector is a customer-oriented sector that requires customer retention to survive in the market. In this sector, customer churn is observed at a high level. In recent years, artificial intelligence-based customer churn analysis has been widely used to predict customer churn behavior. In this study, a customer churn analysis was conducted using publicly shared Telco telecommunications data. Predictive models were constructed using machine learning (LR, KNN, SVM, DT, RF, ANN), ensemble learning (XGBoost, Majority Voting), and deep learning (LSTM) methods. In addition, a 3-layered LSTM model was proposed. Accuracy (Acc), F1-score (F1), Precision (Prec), and Recall (Rec) rates were used to evaluate the models. As a result, the novel 3-layered LSTM model achieved 91.90% Acc, 91.49% Prec, 92.31% Rec, and 91.90% F1 values. The proposed model is competitive with the existing models.

Anahtar Kelimeler

Customer Churn Analysis , Ensemble Learning , Machine Learning , Deep Learning , Telecommunication

Kaynakça

• Ahmed, R., Bibi, M., & Syed, S. (2023a). Improving Heart Disease Prediction Accuracy Using a Hybrid Machine Learning Approach: A Comparative study of SVM and KNN ALgorithms. International Journal of Computations, Information and Manufacturing (IJCIM), 3 (i), 49-54. https://doi.org/l0.54489/ijcim.v3i1.223 google scholar
• Ahmed, R., Bibi, M., & Syed, S. (2023b). Improving Heart Disease Prediction Accuracy Using a Hybrid Machine Learning Approach: A Comparative study of SVM and KNN ALgorithms. International Journal of Computations, Information and Manufacturing (IJCIM), 3(1), 49-54. https://doi.org/10.54489/ijcim.v3i1.223 google scholar
• Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020). A Survey on Churn AnaLysis in Various Business Domains. IEEE Access, 8, 220816220839. https://doi.org/10.1109/ACCESS.2020.3042657 google scholar
• Amin, A., AL-Obeidat, F., Shah, B., Adnan, A., Loo, J., & Anwar, S. (2019). Customer churn prediction in teLecommunication industry using data certainty. Journal of Business Research, 94, 290-301. https://doi.org/10.1016/j.jbusres.2018.03.003 google scholar
• Arifin, S., & Samopa, F. (2018). AnaLysis of Churn Rate SignificantLy Factors in TeLecommunication Industry Using Support Vector Machines Method. Journal of Physics: Conference Series, 1108, 012018. https://doi.org/10.1088/1742-6596/1108/1/012018 google scholar
• Azeem, M., Usman, M., & Fong, A. C. M. (2017). A churn prediction modeL for prepaid customers in teLecom using fuzzy cLassifiers. Telecommunication Systems, 66(4), 603-614. https://doi.org/10.1007/s11235-017-0310-7 google scholar
• BaLtsou, G., TsichLas, K., & VakaLi, A. (2022). LocaL community detection with hints. Applied Intelligence, 52(9), 9599-9620. https://doi. org/10.1007/s10489-021-02946-7 google scholar
• BaşarsLan, M. S., & KayaaLp, F. (2023). Sentiment anaLysis with ensembLe and machine Learning methods in muLti-domain datasets. Turkish Journal of Engineering, 7(2), 141-148. https://doi.org/10.31127/tuje.1079698 google scholar
• Bayrak, A. T., Aktas, A. A., TunaLi, O., Susuz, O., & Abbak, N. (2021). Personalized Customer Churn Analysis with Long Short-Term Memory. 2021 IEEE InternationaL Conference on Big Data and Smart Computing (BigComp), 79-82. https://doi.org/10.1109/BigComp51126. 2021.00024 google scholar
• Bhattacharyya, J., & Dash, M. K. (2022). What Do We Know About Customer Churn Behaviour in the TeLecommunication Industry? A BibLio-metric AnaLysis of Research Trends, 1985-2019. FIIB Business Review, 11(3), 280-302. https://doi.org/10.1177/23197145211062687 google scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324 google scholar
• Cao, J., Lin, Z., Huang, G.-B., & Liu, N. (2012). Voting based extreme Learning machine. Information Sciences, 185(i), 66-77. https://doi.org/ 10.1016/j.ins.2011.09.015 google scholar
• Cao, S., Liu, W., Chen, Y., & Zhu, X. (2019). Deep Learning Based Customer Churn Analysis. 2019 11th InternationaL Conference on WireLess Communications and SignaL Processing (WCSP), 1-6. https://doi.org/10.1109/WCSP.2019.8927877 google scholar
• Chen, T., & Guestrin, C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD InternationaL Conference on KnowLedge Discovery and Data Mining, 785-794. https://doi.org/10.1145/2939672.2939785 google scholar
• Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative anaLysis of data preparation aLgorithms for customer churn prediction: A case study in the teLecommunication industry. Decision Support Systems, 95, 27-36. https://doi.org/10.1016/j.dss. 2016.11.007 google scholar
• ÇeLebi, S. B., & Fidan, Ş. (2024). RNN-Based Time Series AnaLysis for Wind Turbine Energy Forecasting. International Journal of Engineering and Innovative Research, 6(1), 15-28. https://doi.org/10.47933/ijeir.1387314 google scholar
• ÇeLebi, S. B., & Karaman, Ö. A. (2023). MuLtiLayer LSTM ModeL for Wind Power Estimation in the Scada System. European Journal of Technique (EJT), 13(2), 116-122. https://doi.org/10.36222/ejt.1382837 google scholar
• 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, 269(2), 760-772. https://doi.org/10.1016/j.ejor. 2018.02.009 google scholar
• Ecevit, A., Öztürk, İ., Dağ, M., & Özcan, T. (2023). Short-Term SaLes Forecasting Using LSTM and Prophet Based ModeLs in E-Commerce. Acta Infologica, 7(1), 59-70, 2023. https://doi.org/10.26650/acin.1259067 google scholar
• Edwine, N., Wang, W., Song, W., & Ssebuggwawo, D. (2022). Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study. google scholar
• MathematicaL ProbLems in Engineering, 2022, 1-16. https://doi.org/10.1155/2022/8534739 google scholar
• Fu, W. (2022). Research on the construction of earLy warning modeL of customer churn on an e-commerce pLatform. Applied Mathematics and Nonlinear Sciences, 0(0). https://doi.org/10.2478/amns.2022.1.00016 google scholar
• Gary Schneider. (2016). Electronic Commerce (12th Edition). Cengage Learning. google scholar
• Gaur, A., & Dubey, R. (2018). Predicting Customer Churn Prediction In Telecom Sector Using Various Machine Learning Techniques. 2018 InternationaL Conference on Advanced Computation and TeLecommunication (ICACAT), 1-5. https://doi.org/10.1109/ICACAT.2018. 8933783 google scholar
• Guo, F., & Qin, H. (2017). Data Mining Techniques for Customer ReLationship Management. Journal of Physics: Conference Series, 910, 012021. https://doi.org/10.1088/1742-6596/910/1/012021 google scholar
• Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers and Operations Research, 34(10), 2902-2917. https://doi.org/10.1016/j.cor.2005.11.007 google scholar
• Hanif, A., & Azhar, N. (2017, December). Resolving class imbalance and feature selection in customer churn dataset. In 2017 InternationaL Conference on Frontiers of Information TechnoLogy (FIT) (pp. 82-86). IEEE. google scholar
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. google scholar
• InduLkar, Y., & PatiL, A. (2021). Comparative Study of Machine Learning Algorithms for Twitter Sentiment Analysis. 2021 InternationaL Conference on Emerging Smart Computing and Informatics (ESCI), 295-299. https://doi.org/10.1109/ESCI50559.2021.9396925 google scholar
• Jeyaprakaash, P., & Sashirekha, K. (2022). Accuracy Measure of Customer Churn Prediction in TeLecom Industry using Adaboost-Decision Tree ALgorithm. Journal of Pharmaceutical Negative Results, 13(SO4). https://doi.org/10.47750/pnr.2022.13.S04.179 google scholar
• Karvana, K. G. M., Yazid, S., SyaLim, A., & Mursanto, P. (2019). Customer Churn Analysis and Prediction Using Data Mining Models in Banking Industry. 2019 InternationaL Workshop on Big Data and Information Security (IWBIS), 33-38. https://doi.org/10.1109/IWBIS.2019. 8935884 google scholar
• Kozak, J., Kania, K., Juszczuk, P., & Mitrşga, M. (2021). Swarm inteLLigence goal-oriented approach to data-driven innovation for customer churn management. International Journal of Information Management, 60, 102357. https://doi.org/10.1016/j.ijinfomgt.2021.102357 google scholar
• Kurt, K. K., Atay, H. T., Cicek, M. A., Karaca, S. B., & TurkeLi, S. (2019). The Importance of Multidisciplinary in Data Science: Application of the Method in Health Sector to Telecommunication Sector. 2019 InternationaL ArtificiaL InteLLigence and Data Processing Symposium (IDAP), 1-4. https://doi.org/10.1109/IDAP.2019.8875917 google scholar
• LaLwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2022). Customer churn prediction system: a machine Learning approach. Computing, 104(2), 271-294. https://doi.org/10.1007/s00607-021-00908-y google scholar
• LaVaLLey, M. P. (2008). Logistic Regression. Circulation, 117(18), 2395-2399, doi:10.1161/CIRCULATIONAHA.106.682658 google scholar
• MaLik, S., BansaL, P., Sharma, P., Jain, R., & Vashisht, A. (2022). Image Retrieval Using Multilayer Bİ-LSTM (pp. 745-755). https://doi.org/10. 1007/978-981-16-2597-8_64 google scholar
• Mishra, A., & Reddy, U. S. (2017). A Novel Approach for Churn Prediction Using Deep Learning. 2017 IEEE InternationaL Conference on ComputationaL InteLLigence and Computing Research (ICCIC), 1-4. https://doi.org/10.1109/ICCIC.2017.8524551 google scholar
• Mohammadifar, A., GhoLami, H., & GoLzari, S. (2022). Stacking- and voting-based ensembLe deep Learning modeLs (SEDL and VEDL) and active Learning (AL) for mapping Land subsidence. Environmental Science and Pollution Research, 30(10), 26580-26595. https:// doi.org/10.1007/s11356-022-24065-7 google scholar
• Mokhairi, M., Nordin, A.R. & KhaLid, A. (2016). Predictive modeLing for teLco customer churn using rough set theory. ARPN Journal of Engineering and Applied Sciences, 11(5), 3203-3207. google scholar
• OsmanogLu, O. O. (2019). Comparing to Techniques Used in Customer Churn Analysis. In JournaL of MuLtidiscipLinary DeveLopments (VoL. 4, Issue 1). google scholar
• PwC consumer Experience Series. (n.d.). https://www.pwc.de/de/consuLting/pwc-consumer-inteLLigence-series-customer-experience. pdf. https://www.pwc.de/de/consuLting/pwc-consumer-inteLLigence-series-customer-experience.pdf google scholar
• Qureshi, S. A., A. S. Rehman, A. M., & Qamar, A. (2013). Telecommunication subscribers’ churn prediction model using machine learning. Eighth InternationaL Conference on DigitaL Information Management (ICDIM 2013), 131-136. https://doi.org/10.1109/ICDIM.2013. 6693977 google scholar
• Routh, P., Roy, A., & Meyer, J. (2021). Estimating customer churn under competing risks. Journal of the Operational Research Society, 72(5), 1138-1155. https://doi.org/10.1080/01605682.2020.1776166 google scholar
• Seymen, O. F., ÖLmez, E., Doğan, O., Er, O., & HızıroğLu, K. (2023). Customer Churn Prediction Using Ordinary ArtificiaL NeuraL Network and ConvoLutionaL NeuraL Network ALgorithms: A Comparative Performance Assessment. Gazi University Journal of Science, 36(2), 720733. https://doi.org/10.35378/gujs.992738 google scholar
• Shi Long Zhuang. (n.d.). Telecom Churn Dataset. 2022. https://www.kaggLe.com/datasets/shiLongzhuang/teLecom-customer-churn-by-maven-anaLytics google scholar
• Sinan BasarsLan, M., & KayaaLp, F. (2021). Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease (pp. 27-35). https://doi.org/10.1007/978-981-15-6321-8_2 google scholar
• Su, C., Wei, L., & Xie, X. (2022). Churn Prediction in Telecommunications Industry Based on Conditional Wasserstein GAN. 2022 IEEE 29th InternationaL Conference on High Performance Computing, Data, and AnaLytics (HiPC), 186-191. https://doi.org/10.1109/HiPC56025. 2022.00034 google scholar
• Şeker, S. E. (2016). Müşteri Kayıp AnaLizi (Customer Churn AnaLysis). YBS Ansiklopedi, 3(1). google scholar
• Şentürk, A., & Şentürk, Z. K. (2016). Yapay Sinir AğLarı İLe Göğüs Kanseri Tahmini. El-Cezeri Fen ve Mühendislik Dergisi, 3(2). https://doi. org/10.31202/ecjse.264199 google scholar
• Tan, K. L., Lee, C. P., Anbananthen, K. S. M., & Lim, K. M. (2022). RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network. IEEE Access, 10, 21517-21525. https://doi.org/10.1109/ACCESS.2022.3152828 google scholar
• ULLah, I., Raza, B., MaLik, A. K., Imran, M., IsLam, S. U., & Kim, S. W. (2019). A Churn Prediction ModeL Using Random Forest: AnaLysis of Machine Learning Techniques for Churn Prediction and Factor Identification in TeLecom Sector. IEEE Access, 7, 60134-60149. https://doi.org/10.1109/ACCESS.2019.2914999 google scholar
• Ustebay, S., Turgut, Z., & Aydin, M. A. (2019). Cyber Attack Detection by Using Neural Network Approaches: Shallow Neural Network, Deep Neural Network and AutoEncoder (pp. 144-155). https://doi.org/10.1007/978-3-030-21952-9_11 google scholar
• Uzun, E., Agun, H. V., & YerLikaya, T. (2012). Web content extraction by using decision tree learning. 2012 20th SignaL Processing and Communications AppLications Conference (SIU), 1-4. https://doi.org/10.1109/SIU.2012.6204476 google scholar
• Varan, S., & Behara, R. S. (2014). Customer churn analysis in the wireless industry: A data mining approach. https://www.researchgate. net/pubLication/228411974 google scholar
• Wanchai, P. (2017). Customer churn analysis : A case study on the telecommunication industry of Thailand. 2017 12th InternationaL Conference for Internet TechnoLogy and Secured Transactions (ICITST), 325-331. https://doi.org/10.23919/ICITST.2017.8356410 google scholar
• Zatonatska, T., Fareniuk, Y., & Shpyrko, V. (2023). Churn Rate ModeLing for TeLecommunication Operators Using Data Science Methods. Marketing and Management of Innovations, 14(2), 163-173. https://doi.org/10.21272/mmi.2023.2-15 google scholar
• Zavrak, S., & IskefiyeLi, M. (2023). FLow-based intrusion detection on software-defined networks: a muLtivariate time series anomaLy detection approach. Neural Computing and Applications, 35(16), 12175-12193. https://doi.org/10.1007/s00521-023-08376-5 google scholar
• Zavrak, S., & YiLmaz, S. (2023). EmaiL spam detection using hierarchicaL attention hybrid deep Learning method. Expert Systems with Applications, 233, 120977. https://doi.org/10.1016/j.eswa.2023.120977 google scholar
• Zhang, X., Liu, Z., Yang, X., Shi, W., & Wang, Q. (2010). Predicting customer churn by integrating the effect of the customer contact network. google scholar
• Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, 392-397. https://doi.org/ 10.1109/SOLI.2010.5551545 google scholar