Müşteri Kayıp Tahmini için Sinir Ağı Tabanlı Karşılaştırmalı Analiz

Öz

Müşteri kaybı, müşterinin bir işletmeyle bağlantısının kesilmesi anlamına gelir. Müşteri kaybıyla ilgili gider, hem kaybedilen geliri hem de yeni müşteriler kazanmak için gereken pazarlama harcamalarını kapsar. Müşteri kaybının azaltılması her işletmenin en önemli hedefidir. Müşteri kayıp tahmini, işletmelerin yüksek kayıp riski olan müşterileri belirleyerek bu müşterileri ellerinde tutmalarını sağlayan stratejiler geliştirmelerine katkıda bulunacaktır. Dijital dünyada müşteri kayıp tahmini modellerinin geliştirilmesinin önemi her geçen gün artmaktadır. Bu çalışmada, anonim bir telekomünikasyon şirketinden elde edilen müşteri verileri kullanılarak müşteri kayıp tahmini için MLP tabanlı yapay sinir ağı modeli geliştirilmiştir. Geliştirilen model kNN, LR, NB, RF ve SVM ile karşılaştırılmıştır. Uygulanan modellerin tahmin sonuçları tartışılmış ve deneysel sonuçlar, karşılaştırılan tüm modellerin %70'in üzerinde doğruluğa sahip olduğunu göstermiştir. Deneysel sonuçlar, geliştirilen MLP tabanlı yapay sinir ağı modelinin diğer modellere göre yaklaşık %95 doğrulukla en başarılı sınıflandırma performansına sahip olduğunu göstermiştir.

Anahtar Kelimeler

• Yapay Sinir Ağları
• Makine Öğrenmesi
• MLP
• Müşteri Kayıp Tahmini

Neural Network Based a Comparative Analysis for Customer Churn Prediction

Öz

Customer churn refers to a customer's disconnection from a business. The expense associated with customer churn encompasses both the forfeited revenue and the marketing expenditures required to acquire new customers. Mitigating customer churn stands as the foremost objective for every business. Customer churn prediction will contribute to developing strategies enabling businesses to retain these customers by identifying customers with a high risk of loss. In the digital world, the importance of developing customer churn prediction models is increasing daily. In this study, MLP based artificial neural network model was developed for customer churn prediction using customer data from an anonymous telecommunications company. The developed model was compared with kNN, LR, NB, RF, and SVM. The prediction results of the applied models were discussed, and the experimental results showed that all the models compared had over 70% accuracy. Experimental results showed that the developed MLP-based artificial neural network model has the most successful classification performance compared to other models with approximately 95% accuracy.

Anahtar Kelimeler

• Artificial Neural Network
• Machine learning
• MLP
• Customer Churn Prediction

Kaynakça

1. [1] Pondel, M., Wuczyński, M., Gryncewicz, W., Łysik, Ł., Hernes, M., Rot, A., Kozina, A. Deep learning for customer churn prediction in e-commerce decision support. In Business Information Systems. 3-12, 2021.
2. [2] Cenggoro, T. W., Wirastari, R. A., Rudianto, E., Mohadi, M. I., Ratj, D., Pardamean, B. Deep Learning as a Vector Embedding Model for Customer Churn. Procedia Computer Science. 179, 624-631, 2021.
3. [3] Pustokhina, I. V., Pustokhin, D. A., Nguyen, P. T., Elhoseny, M., Shankar, K. Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector. Complex & Intelligent Systems. 1-13, 2021.
4. [4] Khodabandehlou, S., Rahman, M. Z. Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior. Journal of Systems and Information Technology, 2017.
5. [5] Asthana, P. A comparison of machine learning techniques for customer churn prediction. International Journal of Pure and Applied Mathematics. 119(10), 1149-1169, 2018.
6. [6] Agrawal, S., Das, A., Gaikwad, A., Dhage, S. Customer churn prediction modelling based on behavioural patterns analysis using deep learning. In 2018 International conference on smart computing and electronic enterprise (ICSCEE). 1-6, 2018.
7. [7] Gaur, A., Dubey, R. Predicting Customer Churn Prediction In Telecom Sector Using Various Machine Learning Techniques. In 2018 International Conference on Advanced Computation and Telecommunication (ICACAT). 1-5, 2018.
8. [8] Halibas, A. S., Matthew, A. C., Pillai, I. G., Reazol, J. H., Delvo, E. G., Reazol, L. B. Determining the intervening effects of exploratory data analysis and feature engineering in telecoms customer churn modelling. In 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC). 1-7, 2019.

9. [9] Kavitha, V., Kumar, G. H., Kumar, S. M., Harish, M. Churn prediction of customer in telecom industry using machine learning algorithms. International Journal of Engineering Research and Technology (IJERT). 9(5), 181-184, 2020.
10. [10] Lalwani, P., Mishra, M. K., Chadha, J. S., Sethi, P. Customer churn prediction system: a machine learning approach. Computing, 1-24, 2021.
11. [11] Chabumba, D. R., Jadhav, A., Ajoodha, R. Predicting telecommunication customer churn using machine learning techniques. In Interdisciplinary Research in Technology and Management. 625-636, 2021.
12. [12] Telco Customer Churn Dataset, 2018, Available: https://www.kaggle.com/blastchar/telco-customer-churn.
13. [13] Domingos, E., Ojeme, B., Daramola, O. Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector. Computation. 9(3), 34, 2021.
14. [14] De, S., Prabu, P., Paulose, J. Effective ML Techniques to Predict Customer Churn. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). 895-902, 2021.
15. [15] Vo, N. N., Liu, S., Li, X., Xu, G. Leveraging unstructured call log data for customer churn prediction. Knowledge-Based Systems, 212, 106586, 2021.
16. [16] Karamollaoğlu, H., Yücedağ, İ., Doğru, İ. A. Customer Churn Prediction Using Machine Learning Methods: A Comparative Analysis. In 2021 6th International Conference on Computer Science and Engineering (UBMK). 139-144, 2021.
17. [17] Veningston, K., Rao, P. V., Selvan, C., Ronalda, M. (2022). Investigation on Customer Churn Prediction Using Machine Learning Techniques. In Proceedings of International Conference on Data Science and Applications. 109-119, 2022.
18. [18] Dingli, A., Marmara, V., Fournier, N. S. Comparison of deep learning algorithms to predict customer churn within a local retail industry. International journal of machine learning and computing. 7(5), 128-132, 2017.
19. [19] Khattak, A., Mehak, Z., Ahmad, H., Asghar, M. U., Asghar, M. Z., Khan, A. Customer churn prediction using composite deep learning technique. Scientific Reports. 13(1), 17294, 2023.
20. [20] Saha, L., Tripathy, H. K., Gaber, T., El-Gohary, H., El-kenawy, E. S. M. Deep churn prediction method for telecommunication industry. Sustainability. 15(5), 4543, 2023.
21. [21] Amin, A., Adnan, A., Anwar, S. An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes. Applied Soft Computing. 137, 110103, 2023.
22. [22] Prabadevi, B., Shalini, R., Kavitha, B. R. Customer churning analysis using machine learning algorithms. International Journal of Intelligent Networks. 4, 145-154, 2023.