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.
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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
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