Enhancing Customer Churn Prediction: Advanced Models and Resampling Techniques in Dynamic Business Environments

Customer churn analysis is critical for businesses looking to hold onto market share in today’s dynamic business environment. The development of e-Finance presents additional difficulties for the traditional banking sector as the digital marketplace grows. Banks face several challenges, including fintech competition, dwindling client loyalty, and digital transformation. Bank managers can identify problems, identify potential churn customers early on, and develop effective retention strategies based on client traits and preferences by analyzing probable causes of bank customer turnover from multiple perspectives and building models for predicting churn. Not only banks but also large corporate sectors like telecommunication, and over-the-top (OTT) platforms do face customer churn. This study proposes the Random Leaf Model (RLM) and also explores the Logit Leaf Model (LLM), and Neural Network Ensemble Model, three sophisticated predictive modeling methodologies. Proactive strategies are necessary in today's marketplaces due to their competitive nature. The primary problem with current automatic churn prediction algorithms is the substantial gap between majority and minority class proportions in the datasets, which might lead to model bias in favor of the dominant class. The shortcomings of conventional churn analysis techniques underscore the necessity of implementing advanced cutting-edge algorithms to achieve precise forecasts.