Bank Churn Prediction: Leveraging Advanced Analytics for Customer Retention in the Banking Sector

Consumers are more likely to switch to better alternatives in today’s fiercely competitive market if they believe they are available. If consumers have easy access to information, incur minimal switching costs, and are convinced that better options are available, they are more likely to switch. Particularly in markets with an abundance of information, this is true. As a result, businesses must keep a close eye on their clients in order to identify any potential problems. As a result of significant advancements in business intelligence, numerous information discovery and predictive analytics techniques have been developed. This study proposes a novel monitoring and forecasting paradigm for client attrition using data from ABC Bank’s website. Historical churning behavior patterns were analyzed using data mining techniques to generate predictor variables for machine learning-based predictive models. To validate the efficacy of the classifiers, a sample of consumers who were about to depart was utilized. Businesses must plan for customer churn because it allows them to figure out customers who might stop using their services. Getting more customers is comparatively more expensive then retaining the old customers. As a result, it is critical to understand the precise marketing strategies that will increase the likelihood of retaining a customer who is about to leave. Businesses must consider a variety of customer habits, preferences, and cancellation factors in order to retain customers. Maintaining constant communication with each customer and determining when and how to run campaigns that will be most effective for them is critical. In order to develop effective marketing retention strategies, this study will introduce a novel method for identifying prospective customers who might leave anytime soon.