Due to market deregulation and globalisation, competitive environments in various sectors
continuously evolve, leading to increased customer churn. Effectively anticipating and …

Churn prediction gained attention across different application fields, both in the business
and academic world, and a variety of sophisticated churn prediction models have already …

Competitive markets and customers' changing needs in the bank industry necessitate
accurately predicting customers who may leave the firm in the near future. Consequently …

Alongside natural language processing and computer vision, large learning models have
found their way into e-commerce. Especially, for recommender systems and click-through …

Marketers have long since understood the importance of customer segmentation and
customer churn prediction modelling. However, linking these processes remains a …

Predicting events and behavior in games is a crucial task of game analytics, both during
gameplay, such as predicting wins, and after, like forecasting player churn. Game metrics …

Purpose The new customer churn prediction (CCP) utilizing deep learning is developed in
this work. Initially, the data are collected from the WSDM-KKBox's churn prediction challenge …

Players' purchases in free-to-play online games often serve as crucial indicators of user
engagement and behavior. Understanding these purchases not only enhances the …

Although some machine learning methods have been widely applied to customer churn
prediction across various fields, few studies have focused on customer churn in card and …

Customer churn can be defined as the phenomenon of customers who discontinue their
relationship with a company. This problem is transversal to many industries, including the …