Machine Learning-Based Customer Segmentation and Personalised Marketing in Financial Services

Customer segmentation is a way of dividing a clientele into groups, or “the consumer segments,” with the intention of assembling customers with similar characteristics in each section. Gender, age, interests, and different spending patterns are just a few of the marketing-relevant characteristics that serve as the foundation for segmentation. Segmentation solutions enable banks to provide more individualized products and services. Marketers can select the best channels of advertising for their intended consumer base, establish profitable new market niches, launch novel products and services, and decide what promotional content to offer by getting a deeper grasp of the characteristics of their target audience. Machine learning can help people make more informed choices in the future is used in this situation. Different algorithms are used to uncover concealed trends in the data. Applying segmentation makes it clear which of the eluded segments to target. Machine learning can help people make more informed choices in the future is used in this situation. Different algorithms are used to uncover undetected trends in the data. Applying segmentation makes it clear which of the eluded segments to target. This study segmented the data regarding cardholders of different banks using silhouette approaches, elbow approaches, and K-means cluster analysis.