The era of behavioural big data has created new avenues for data science research, with many new contributions stemming from academic researchers. Yet data controlled by …
Z Liu, P Jiang, KW De Bock, J Wang, L Zhang… - … Forecasting and Social …, 2024 - Elsevier
Customer retention campaigns increasingly rely on predictive analytics to identify potential churners in a customer base. Traditionally, customer churn prediction was dependent on …
Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one …
Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the …
In order to improve the performance of any machine learning model, it is important to focus more on the data itself instead of continuously developing new algorithms. This is exactly the …
Customer churn is a critical and challenging problem affecting business and industry, in particular, the rapidly growing, highly competitive telecommunication sector. It is of …
J Ahn, J Hwang, D Kim, H Choi, S Kang - IEEE Access, 2020 - ieeexplore.ieee.org
In this paper, we present churn prediction techniques that have been released so far. Churn prediction is used in the fields of Internet services, games, insurance, and management …
Class imbalance brings significant challenges to customer churn prediction. Many solutions have been developed to address this issue. In this paper, we comprehensively compare the …
To survive in today's telecommunication business it is imperative to distinguish customers who are not reluctant to move toward a competitor. Therefore, customer churn prediction has …