Articles
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BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES
Md Amran Hossen Pabel , Master’s of Science in Business Analytics Wright State University, Ohio, USA Biswanath Bhattacharjee , Department of Management Science and Quantitative Methods, Gannon University, USA Sonjoy Kumar Dey , McComish Department of Electrical Engineering and Computer Science, South Dakota State University, USA Sakib Salam Jamee , Department of Management Information Systems, University of Pittsburgh, PA, USA Md Omar Obaid , Department of Business Analytics, California State Polytechnic University Pomona, CA, USA Md Sakib Mia , MSc in Business Analytics, Trine University, USA Sajidul Islam Khan , MSc in Business Analytics, Trine University, USA Mohammad Kawsur Sharif , Department of Business Administration and Management, Washington University of Virginia, USAAbstract
This study evaluates three machine learning clustering algorithms—K-Means, DBSCAN, and Hierarchical Clustering—for customer segmentation in the banking sector. Using a dataset of customer demographic, financial, and transactional data, we compare the algorithms based on the Silhouette score and Davies-Bouldin index. Hierarchical Clustering performed best, achieving the highest Silhouette score (0.68) and the lowest Davies-Bouldin index (1.15), indicating well-defined and compact clusters. K-Means showed reliable performance with a Silhouette score of 0.62 but required predefined clusters. DBSCAN identified noise effectively but resulted in lower cluster compactness, with a Silhouette score of 0.55 and a Davies-Bouldin index of 1.50. The findings highlight Hierarchical Clustering as the most effective method for customer segmentation in banking, with flexibility depending on the data and objectives.
Keywords
Customer segmentation, machine learning, K-Means, DBSCAN
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Copyright (c) 2025 Md Amran Hossen Pabel, Biswanath Bhattacharjee, Sonjoy Kumar Dey, Sakib Salam Jamee, Md Omar Obaid, Md Sakib Mia, Sajidul Islam Khan, Mohammad Kawsur Sharif
This work is licensed under a Creative Commons Attribution 4.0 International License.