CUSTOMER SEGMENTATION USING K-MEANS CLUSTERING FOR TARGETED MARKETING IN BANKING
Keywords:
Customer Segmentation, Machine Learning, K-Means Clustering, Banking, Targeted Marketing, Customer EngagementAbstract
Customer segmentation plays a pivotal role in the banking industry, allowing institutions to customize marketing strategies and offers for specific customer segments. This paper investigates the use of the K-Means clustering algorithm, a machine learning technique, to group customers based on crucial financial attributes including account balance, balance checking frequency, purchase patterns, cash advances, and purchase frequency. The objective is to form well-defined clusters that reveal distinct customer profiles. By harnessing these insights, banks can design targeted marketing campaigns and personalized offers, enhancing customer engagement, fostering loyalty, and ultimately driving profitability.
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Copyright (c) 2024 Karthika Gopalakrishna (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
