A Temporal Approach to Customer Churn Prediction: A Case Study for Financial Services

• Marcus Almeida UFOP
• Mariana Mota UFOP
• Wellington Souza Gerencianet S.A
• Marcos Nicolau Gerencianet S.A
• Eduardo Luz UFOP
• Gladston Moreira UFOP

DOI: https://doi.org/10.5753/eniac.2022.227571

Abstract

Customer churn prediction models aim to detect customers with a high probability of canceling the contract with the company, based on the use of the offered services. We propose a temporal approach to the labeling stage, based on the frequency reduction of the use of services, through each customer’s behavior. We also propose a temporal neural network architecture for the task. The approach was evaluated on a real dataset provided by a Brazilian company in the financial sector. The temporal convolutional neural network achieved an accuracy of 82.63%, a sensitivity of 61.5%, and a precision of 41.58%, outperforming other traditional classifiers (XG-Boost and Random Forest).

References

Ahn, D., Lee, D., and Hosanagar, K. (2020). Interpretable deep learning approach to churn management. Available at SSRN 3981160.

Amin, A., Khan, C., Ali, I., and Anwar, S. (2014). Customer churn prediction in telecommunication industry: With and without counter-example. In 2014 European Network Intelligence Conference, pages 134-137.

Bai, S., Kolter, J. Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271.

Caigny, A. D., Coussement, K., and Bock, K. W. D. (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.

Caigny, A. D., Coussement, K., Bock, K. W. D., and Lessmann, S. (2020). Incorporating textual information in customer churn prediction models based on a convolutional neural network. International Journal of Forecasting, 36(4):1563-1578.

Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321-357.

Chen, Z.-Y., Fan, Z.-P., and Sun, M. (2012). A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of Operational Research, 223(2):461-472.

Chouiekh, A. et al. (2020). Deep convolutional neural networks for customer churn prediction analysis. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(1):1-16.

Coussement, K. and De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66(9):1629-1636.

Devriendt, F., Berrevoets, J., and Verbeke, W. (2021). Why you should stop predicting customer churn and start using uplift models. Information Sciences, 548:497-515.

Gregory, B. (2018). Predicting customer churn: Extreme gradient boosting with temporal data. arXiv:1802.03396.

Hadden, J., Tiwari, A., Roy, R., and Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 34(10):2902-2917.

Lima, E., Mues, C., and Baesens, B. (2009). Domain knowledge integration in data mining using decision tables: case studies in churn prediction. Journal of the Operational Research Society, 60(8):1096-1106.

Mena, C. G., De Caigny, A., Coussement, K., De Bock, K. W., and Lessmann, S. (2019). Churn prediction with sequential data and deep neural networks. a comparative analysis. arXiv:1909.11114.

Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., and Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2):204-211.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.

Remy, P. (2020). Temporal convolutional networks for keras. https://github.com/philipperemy/keras-tcn.

Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V. (2019). Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2820-2828.

Ullah, I., Raza, B., Malik, A. K., Imran, M., Islam, S. U., and 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.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Å., and Polosukhin, I. (2017). Attention is all you need. arXiv:1706.03762.

Verbeke, W., Martens, D., Mues, C., and Baesens, B. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38(3):2354-2364.

Zhong, J. and Li, W. (2019). Predicting customer churn in the telecommunication industry by analyzing phone call transcripts with convolutional neural networks. In Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence, pages 55-59.

Zhuang, Y. (2018). Research on e-commerce customer churn prediction based on improved value model and xg-boost algorithm. Management Science and Engineering, 12(3):51-56.