Abstract
Customer churn is the event when customers using the products or services of an organization decide to no longer do so by either switching to another organization or by stopping using those products/services. It takes much more effort to attract new customers than to retain the existing clientele, which makes predicting customer churn and imposing retention strategies an essential task for many organizations. Though methodologies that deliver accurate predictions are available, there exists no general and transparent pipeline designed for churn prediction on different domains with high interpretability. In our work, we propose two domain-agnostic, inference-friendly, and easy-interpreting novelties: 1) a general and interpretable clustering-based churn prediction pipeline, ClusPred, which incorporates both demographics and transaction records; 2) an Inhomogeneous Poisson Process (IHPP) specifically designed to model customer behaviors to predict churners. We evaluated the prediction performance of ClusPred with IHPP using datasets from banking, retails, and mobile application fields. The experiments illustrated the ClusPred pipeline with IHPP have increased the performance while ensuring domain independence, making it a more standardized churner prediction process. With competitive accuracy and running time, our proposed models are also suitable for big data applications, which is rarely addressed by customer churn methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Huang, Y., et al.: Telco churn prediction with big data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery, pp. 607–618 (2015)
Zhang, Z., Wang, R., Zheng, W., Lan, S., Liang, D., Jin, H.: Profit maximization analysis based on data mining and the exponential retention model assumption with respect to customer churn problems. In: Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. Institute of Electrical and Electronics Engineers Inc., pp 1093–1097 (2016)
De Caigny, A., Coussement, K., De Bock, K.W.: A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur. J. Oper. Res. 269, 760–772 (2018). https://doi.org/10.1016/j.ejor.2018.02.009
Amin, A., et al.: Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing 237, 242–254 (2017). https://doi.org/10.1016/j.neucom.2016.12.009
Bi, W., Cai, M., Liu, M., Li, G.: A big data clustering algorithm for mitigating the risk of customer churn. IEEE Trans. Ind. Inform. 12, 1270–1281 (2016). https://doi.org/10.1109/TII.2016.2547584
Li, Y., Wang, B.: A study on customer churn of commercial banks based on learning from label proportions. In: IEEE International Conference on Data Mining Workshops, ICDMW. IEEE Computer Society, pp. 1241–1247 (2019)
Yang, Y., Liu, Z., Tan, C., Wu, F., Zhuang, Y., Li, Y.: To stay or to leave: Churn prediction for urban migrants in the initial period. In: The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018. Association for Computing Machinery, Inc., pp. 967–976 (2018)
Das, M., Elsner, M., Nandi, A., Ramnath, R.: TopChurn: maximum entropy churn prediction using topic models over heterogeneous signals. In: WWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web. Association for Computing Machinery, Inc., pp 291–297 (2015)
Lu, Y., et al.: Uncovering the co-driven mechanism of social and content links in user churn phenomena. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp. 3093–3101 (2019)
Shirazi, F., Mohammadi, M.: A big data analytics model for customer churn prediction in the retiree segment. Int. J. Inf. Manage. 48, 238–253 (2019). https://doi.org/10.1016/j.ijinfomgt.2018.10.005
Liu, X., et al.: A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games (2018)
Yang, C., Shi, X., Luo, J., Han, J.: I know you’ll be back: interpretable new user clustering and churn prediction on a mobile social application. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp. 914–922 (2018)
De Caigny, A., Coussement, K., De Bock, K.W., Lessmann, S.: Incorporating textual information in customer churn prediction models based on a convolutional neural network. Int. J. Forecast. 36, 1563–1578 (2020). https://doi.org/10.1016/j.ijforecast.2019.03.029
Mitrović, S., Baesens, B., Lemahieu, W., Weerdt, J.: Churn prediction using dynamic RFM-augmented Node2vec. In: Guidotti, R., Monreale, A., Pedreschi, D., Abiteboul, S. (eds.) PAP 2017. LNCS, vol. 10708, pp. 122–138. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71970-2_11
Luo, L., Li, B., Koprinska, I., Berkovsky, S., Chen, F.: Discovering temporal purchase patterns with different responses to promotions. In: International Conference on Information and Knowledge Management, Proceedings (2016)
Ke, G., et al.: LightGBM: A highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, H., Luo, L., Ristanoski, G. (2021). A Clustering-Prediction Pipeline for Customer Churn Analysis. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-82153-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82152-4
Online ISBN: 978-3-030-82153-1
eBook Packages: Computer ScienceComputer Science (R0)