Bank Customer Segmentation and Marketing Strategies Based on Improved DBSCAN Algorithm

This study conducts a case study on the characteristics of fixed deposit businesses in a Portuguese bank, analyzing the current customer data features and the limitations of marketing strategies. It also highlights the limitations of the traditional DBSCAN algorithm, including issues with parameter selection and a lack of diverse clustering metrics. Using machine learning techniques, the study explores the relationship between customer attribute features and fixed deposits. The proposed KM-DBSCAN algorithm, which combines K-means and DBSCAN, is used for customer segmentation. This method integrates both implicit and explicit customer indicators, incorporates weight factors, constructs a distance distribution matrix, and optimizes the process of selecting the neighborhood radius and density threshold parameters. As a result, the clustering accuracy of customer segmentation is improved by 15%. Based on the clustering results, customers are divided into four distinct groups, and personalized marketing strategies for customer deposits are proposed. Differentiated marketing plans are implemented, with a focus on customer relationship management and feedback. The model’s performance is evaluated using silhouette coefficients, accuracy, and F1 score. The model is then applied in a real-world scenario, leading to an average business revenue growth rate of 16.08% and a 4.5% increase in customer engagement.