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Forecasting Customer Churn using Machine Learning and Deep Learning Approaches

Yıl 2025, Cilt: 8 Sayı: 1, 60 - 70, 31.05.2025

Ceren Aksoy , Ayhan Küçükmanisa , Zeynep Hilal Kilimci

https://doi.org/10.34088/kojose.1526621

Öz

Customer churn forecasting is a challenging task recommended for churn prevention for companies operating in various industries such as banking, telecommunications, and insurance. Forecasting customer churn is very important for many companies because gaining potential customers usually costs more than retaining present ones. That is why companies, analysts, and researchers are center on analyzing the dynamics behind customer churn behaviors. In this study, we present a comparative study for the purpose of forecasting customer churn employing publicly available datasets, namely, IBM Watson and Call-Detailed Record (CDR). For this purpose, logistic regression, random forest, decision tree, k-nearest neighbor, extreme gradient boosting, and naive Bayes techniques are evaluated as machine learning approaches while artificial neural networks and convolutional neural networks are assessed as deep learning models. Experiment results indicate that the random forest method exhibits superior performance with 79.94% accuracy for the IBM Watson dataset and 96.34% accuracy for the Call Detailed Report (CDR) dataset. To demonstrate the effectiveness of the suggested framework, a comparison with the state-of-the-art studies is performed.

Anahtar Kelimeler

Customer churn prediction Random forest Deep learning Convolutional neural networks

Kaynakça

  • [1] Ahmad A. K., Jafar A., Aljoumaa K., 2019. Customer Churn Prediction in Telecom Using Machine Learning in Big Data Platform. Journal of Big Data, 6(1).
  • [2] Koçoğlu F. Ö., Özcan T. Ş., Baray A., 2016. Veri madenciliğinde ayrılan müşteri üzerine literatür araştırması. Uluslarası Katılımlı 16. Üretim Araştırmaları Sempozyumu, İstanbul, Türkiye, 12-14 October.
  • [3] LeCun Y., Bengio Y., Hinton G., 2015. Deep learning. Nature, 521(7553), pp. 436–444.
  • [4] Agrawal S., Das A., Gaikwad A., Dhage S., 2018. Customer churn prediction modelling based on behavioural patterns analysis using Deep Learning. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Selangor, Malaysia, 11-12 July.
  • [5] Putu O. H. N., Setyo A., 2020. Telecommunication service subscriber churn likelihood prediction analysis using diverse machine learning model. 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), Medan, Indonesia, 25-27 June.
  • [6] Jay Shen T., Bin Shibghatullah A., 2022. Developing machine learning and deep learning models for customer churn prediction in telecommunication industry. Proceedings of International Conference on Artificial Life and Robotics, 27, pp. 533–539.
  • [7] Nalatissifa H., Pardede H. F., 2021. Customer decision prediction using deep neural network on Telco Customer Churn Data. Jurnal Elektronika Dan Telekomunikasi, 21(2), pp. 122.
  • [8] Tang P., 2020. Telecom customer churn prediction model combining K-means and XGBoost algorithm. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 25-27 December.
  • [9] Siddika A., Faruque A., Masum A. K., 2021. Comparative analysis of churn predictive models and factor identification in telecom industry. 2021 24th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 18-20 December.
  • [10] Tamuka N., Sibanda K., 2020. Real time customer churn scoring model for the telecommunications Industry. 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Kimberley, South Africa, 25-27 November.
  • [11] Parmar P., Serasiya S., 2021. Telecom Churn Prediction Model using XgBoost Classifier and Logistic Regression Algorithm. International Research Journal of Engineering and Technology (IRJET), 8(5), pp. 1100-1105.
  • [12] Makruf M., Bramantoro A., Alyamani H. J., Alesawi S., Alturki R., 2021. Classification methods comparison for customer churn prediction in the telecommunication industry. International Journal of Advanced and Applied Sciences, 8(12), pp. 1–8.
  • [13] Raja J., Pandian S., 2019. An Optimal Ensemble Classification for Predicting Churn in Telecommunication. Journal of Engineering Science and Technology Review, 13(2), pp. 44-49.
  • [14] Hargreaves C. A., 2019. A machine learning algorithm for Churn Reduction & Revenue Maximization: An application in the telecommunication industry. International Journal of Future Computer and Communication, 8(4), pp. 109–113.
  • [15] Wagh Sharmila K., Andhale Aishwarya A., Wagh Kishor S., Pansare Jayshree R., Ambadekar Sarita P., Gawande S.H., 2024. Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization Volume 14, March 2024, 100342.
  • [16] Brownlee J., 2023. 3 Ways to Encode Categorical Variables for Deep Learning, https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python, 03.03.2023.
  • [17] Wibowo V. V. P., Rustam Z., Laeli A. R., Said A. A., 2021. Logistic regression and logistic regression-genetic algorithm for classification of Liver Cancer Data. 2021 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain, 07-08 December.
  • [18] Kotsiantis S. B., 2021. Decision trees: A recent overview. Artificial Intelligence Review, 39(4), pp. 261–283.
  • [19] He S., Wu J., Wang D., He X., 2022. Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest. Chemosphere, 290, pp. 133388.
  • [20] Cunningham P., Delany S. J., 2021. K-nearest neighbour classifiers - a tutorial. ACM Computing Surveys, 54(6), pp. 1–25.
  • [21] Pamina J., Raja B., SathyaBama S., Soundarya S., Sruthi M. S., Kiruthika S., Aiswaryadevi V. J., 2019. An Effective Classifier for Predicting Churn in Telecommunication. Jour of Adv Research in Dynamical & Control Systems, 11, pp. 221-229.
  • [22] Dong T., Shang W., Zhu H., 2011. Naive bayesian classifier based on the improved feature weighting algorithm. Communications in Computer and Information Science, 152, 142–147.
  • [23] Gupta N., 2013. Artificial Neural Network. Network and Complex Systems, 3(1), pp. 24-28.
  • [24] Bock S., Goppold J., Weiß M., 2018. An improvement of the convergence proof of the ADAM-Optimizer. arXiv:1804.10587.
  • [25] Nair V., Hinton G. E., 2010. Rectified linear units improve restricted boltzmann machines. ICML, Haifa, Israel, 807-814.
  • [26] Adem K., 2022. Impact of activation functions and number of layers on detection of exudates using circular hough transform and convolutional neural networks. Expert Systems with Applications, 203, pp. 117583.
  • [27] Surekcigil Pesch I., Bestelink E., de Sagazan O., Mehonic A., Sporea R. A., 2022. Multimodal transistors as ReLU activation functions in physical neural network classifiers. Scientific Reports, 12(1), 670.
  • [28] Gustineli M., 2022. A survey on recently proposed activation functions for deep learning. arXiv:2204.02921.
  • [29] Abbas S., Alhwaiti Y., Fatima A., Khan M. A., Adnan Khan M., Ghazal T. M., Kanwal A., Ahmad M., Sabri Elmitwally, N., 2022. Convolutional neural network based intelligent handwritten document recognition, Computers, Materials & Continua, 70(3), pp. 4563-4581.
  • [30] Prilianti K. R., Brotosudarmo T. H., Anam S., Suryanto A., 2019. Performance comparison of the convolutional neural network optimizer for photosynthetic pigments prediction on plant digital image. AIP Conference Proceedings, 020020, pp. 1-8.
  • [31] Haji S. H., Abdulazeez A. M., 2021. Comparison of Optimization Techniques Based on Gradient Descent Algorithm: A Review. Palarch’s Journal of Archaeology of Egypt/Egyptology, 18(4), 2021.
  • [32] Štifanić J., Štifanić D., Zulijani A., Car Z., 2021. Multiclass Classification of Oral Squamous Cell Carcinoma.Student Scientific Conference RiSTEM 2021, Rijeka, Croatia, 10 June.
  • [33] Bansal K., Bathla R. K., Kumar Y., 2022. Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancer. Soft Computing, 26, pp. 11153-11184.

Forecasting Customer Churn using Machine Learning and Deep Learning Approaches

Yıl 2025, Cilt: 8 Sayı: 1, 60 - 70, 31.05.2025

Ceren Aksoy , Ayhan Küçükmanisa , Zeynep Hilal Kilimci

https://doi.org/10.34088/kojose.1526621

Öz

Customer churn forecasting is a challenging task recommended for churn prevention for companies operating in various industries such as banking, telecommunications, and insurance. Forecasting customer churn is very important for many companies because gaining potential customers usually costs more than retaining present ones. That is why companies, analysts, and researchers are center on analyzing the dynamics behind customer churn behaviors. In this study, we present a comparative study for the purpose of forecasting customer churn employing publicly available datasets, namely, IBM Watson and Call-Detailed Record (CDR). For this purpose, logistic regression, random forest, decision tree, k-nearest neighbor, extreme gradient boosting, and naive Bayes techniques are evaluated as machine learning approaches while artificial neural networks and convolutional neural networks are assessed as deep learning models. Experiment results indicate that the random forest method exhibits superior performance with 79.94% accuracy for the IBM Watson dataset and 96.34% accuracy for the Call Detailed Report (CDR) dataset. To demonstrate the effectiveness of the suggested framework, a comparison with the state-of-the-art studies is performed.

Anahtar Kelimeler

Customer churn prediction Random forest Deep learning Convolutional neural networks

Kaynakça

  • [1] Ahmad A. K., Jafar A., Aljoumaa K., 2019. Customer Churn Prediction in Telecom Using Machine Learning in Big Data Platform. Journal of Big Data, 6(1).
  • [2] Koçoğlu F. Ö., Özcan T. Ş., Baray A., 2016. Veri madenciliğinde ayrılan müşteri üzerine literatür araştırması. Uluslarası Katılımlı 16. Üretim Araştırmaları Sempozyumu, İstanbul, Türkiye, 12-14 October.
  • [3] LeCun Y., Bengio Y., Hinton G., 2015. Deep learning. Nature, 521(7553), pp. 436–444.
  • [4] Agrawal S., Das A., Gaikwad A., Dhage S., 2018. Customer churn prediction modelling based on behavioural patterns analysis using Deep Learning. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Selangor, Malaysia, 11-12 July.
  • [5] Putu O. H. N., Setyo A., 2020. Telecommunication service subscriber churn likelihood prediction analysis using diverse machine learning model. 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), Medan, Indonesia, 25-27 June.
  • [6] Jay Shen T., Bin Shibghatullah A., 2022. Developing machine learning and deep learning models for customer churn prediction in telecommunication industry. Proceedings of International Conference on Artificial Life and Robotics, 27, pp. 533–539.
  • [7] Nalatissifa H., Pardede H. F., 2021. Customer decision prediction using deep neural network on Telco Customer Churn Data. Jurnal Elektronika Dan Telekomunikasi, 21(2), pp. 122.
  • [8] Tang P., 2020. Telecom customer churn prediction model combining K-means and XGBoost algorithm. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 25-27 December.
  • [9] Siddika A., Faruque A., Masum A. K., 2021. Comparative analysis of churn predictive models and factor identification in telecom industry. 2021 24th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 18-20 December.
  • [10] Tamuka N., Sibanda K., 2020. Real time customer churn scoring model for the telecommunications Industry. 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Kimberley, South Africa, 25-27 November.
  • [11] Parmar P., Serasiya S., 2021. Telecom Churn Prediction Model using XgBoost Classifier and Logistic Regression Algorithm. International Research Journal of Engineering and Technology (IRJET), 8(5), pp. 1100-1105.
  • [12] Makruf M., Bramantoro A., Alyamani H. J., Alesawi S., Alturki R., 2021. Classification methods comparison for customer churn prediction in the telecommunication industry. International Journal of Advanced and Applied Sciences, 8(12), pp. 1–8.
  • [13] Raja J., Pandian S., 2019. An Optimal Ensemble Classification for Predicting Churn in Telecommunication. Journal of Engineering Science and Technology Review, 13(2), pp. 44-49.
  • [14] Hargreaves C. A., 2019. A machine learning algorithm for Churn Reduction & Revenue Maximization: An application in the telecommunication industry. International Journal of Future Computer and Communication, 8(4), pp. 109–113.
  • [15] Wagh Sharmila K., Andhale Aishwarya A., Wagh Kishor S., Pansare Jayshree R., Ambadekar Sarita P., Gawande S.H., 2024. Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization Volume 14, March 2024, 100342.
  • [16] Brownlee J., 2023. 3 Ways to Encode Categorical Variables for Deep Learning, https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python, 03.03.2023.
  • [17] Wibowo V. V. P., Rustam Z., Laeli A. R., Said A. A., 2021. Logistic regression and logistic regression-genetic algorithm for classification of Liver Cancer Data. 2021 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain, 07-08 December.
  • [18] Kotsiantis S. B., 2021. Decision trees: A recent overview. Artificial Intelligence Review, 39(4), pp. 261–283.
  • [19] He S., Wu J., Wang D., He X., 2022. Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest. Chemosphere, 290, pp. 133388.
  • [20] Cunningham P., Delany S. J., 2021. K-nearest neighbour classifiers - a tutorial. ACM Computing Surveys, 54(6), pp. 1–25.
  • [21] Pamina J., Raja B., SathyaBama S., Soundarya S., Sruthi M. S., Kiruthika S., Aiswaryadevi V. J., 2019. An Effective Classifier for Predicting Churn in Telecommunication. Jour of Adv Research in Dynamical & Control Systems, 11, pp. 221-229.
  • [22] Dong T., Shang W., Zhu H., 2011. Naive bayesian classifier based on the improved feature weighting algorithm. Communications in Computer and Information Science, 152, 142–147.
  • [23] Gupta N., 2013. Artificial Neural Network. Network and Complex Systems, 3(1), pp. 24-28.
  • [24] Bock S., Goppold J., Weiß M., 2018. An improvement of the convergence proof of the ADAM-Optimizer. arXiv:1804.10587.
  • [25] Nair V., Hinton G. E., 2010. Rectified linear units improve restricted boltzmann machines. ICML, Haifa, Israel, 807-814.
  • [26] Adem K., 2022. Impact of activation functions and number of layers on detection of exudates using circular hough transform and convolutional neural networks. Expert Systems with Applications, 203, pp. 117583.
  • [27] Surekcigil Pesch I., Bestelink E., de Sagazan O., Mehonic A., Sporea R. A., 2022. Multimodal transistors as ReLU activation functions in physical neural network classifiers. Scientific Reports, 12(1), 670.
  • [28] Gustineli M., 2022. A survey on recently proposed activation functions for deep learning. arXiv:2204.02921.
  • [29] Abbas S., Alhwaiti Y., Fatima A., Khan M. A., Adnan Khan M., Ghazal T. M., Kanwal A., Ahmad M., Sabri Elmitwally, N., 2022. Convolutional neural network based intelligent handwritten document recognition, Computers, Materials & Continua, 70(3), pp. 4563-4581.
  • [30] Prilianti K. R., Brotosudarmo T. H., Anam S., Suryanto A., 2019. Performance comparison of the convolutional neural network optimizer for photosynthetic pigments prediction on plant digital image. AIP Conference Proceedings, 020020, pp. 1-8.
  • [31] Haji S. H., Abdulazeez A. M., 2021. Comparison of Optimization Techniques Based on Gradient Descent Algorithm: A Review. Palarch’s Journal of Archaeology of Egypt/Egyptology, 18(4), 2021.
  • [32] Štifanić J., Štifanić D., Zulijani A., Car Z., 2021. Multiclass Classification of Oral Squamous Cell Carcinoma.Student Scientific Conference RiSTEM 2021, Rijeka, Croatia, 10 June.
  • [33] Bansal K., Bathla R. K., Kumar Y., 2022. Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancer. Soft Computing, 26, pp. 11153-11184.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Ceren Aksoy 0000-0002-7349-4967

Ayhan Küçükmanisa 0000-0002-1886-1250

Zeynep Hilal Kilimci 0000-0003-1497-305X

Yayımlanma Tarihi 31 Mayıs 2025
Gönderilme Tarihi 1 Ağustos 2024
Kabul Tarihi 23 Aralık 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 1

Kaynak Göster

APA Aksoy, C., Küçükmanisa, A., & Kilimci, Z. H. (2025). Forecasting Customer Churn using Machine Learning and Deep Learning Approaches. Kocaeli Journal of Science and Engineering, 8(1), 60-70. https://doi.org/10.34088/kojose.1526621