Customer Segmentation And Churn Prediction Using K Means Fuzzy And Multi-Layer Perceptron Algorithm

Given the competitive environment in the banking industry, creating long-term relationships is quite necessary in the banking industry, therefore, insights from bank churn prediction can be really beneficial to compete and maintain profitability. Customers are segmented using complex clustering algorithms based on their purchasing sequences, population trends, and bank activities. This segmentation allow banks to identify various customer groups with varying needs and preferences, leading in more specific advertising techniques as well as targeted product offerings. Churn prediction helps us in identifying which customers are nearer to churn or exiting the company. This helps us to reduce customer retention. Customer segmentation and churn prediction allow businesses to construct concentrated advertising and loyalty strategies. Understanding the behaviors and personality traits of separate client groups shall assist companies in reducing churn while enhancing their client satisfaction. By implementing this help a lot in a company’s success and helps them in understanding what a customer is really looking for from their company. Banks can improve relationships with customers and achieve profitable growth in the dynamic sector of banking by successfully integrating customer segmentation and churn prediction. In this research or paper, we are going to perform customer segmentation for bank clients using clustering algorithms and then predict the chance of these customers leaving the bank in the future with the system of churn prediction.