Leveraging Machine Learning for Customer Segmentation and Targeted Marketing in BFSI
Abstract
Banks can enhance their product and service personalization through segmentation solutions. By gaining a deeper understanding of client characteristics, marketers can select the appropriate promotional content, choose the proper marketing channels for the target market, identify new and profitable market sectors, and introduce innovative products and services. Artificial intelligence marketing leverages AI concepts and models like machine learning and Bayesian networks. Cluster analysis, a machine learning method, classifies entities with similar observable characteristics. This study employed K-means cluster analysis and the Elbow and silhouette methods to segment data for cardholders from various banks. Results from the Elbow and silhouette methods indicated that the optimal number of clusters is five. Based on income and shopping frequency, considered the most significant attributes for customer segmentation, this research identified five distinct consumer segments: Savers, General, Targets, and Big Spenders. The study's findings have direct implications for the industry, recommending the use of machine learning techniques to develop various marketing strategies and policies. These strategies can enhance the bank’s efficiency, customer satisfaction, and service quality, making the research highly actionable for banking professionals, marketers, and researchers.
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