CUSTOMER SEGMENTATION USING MACHINE LEARNING

Businesses need to identify and segment their consumer base in order to effectively customize their strategies and improve customer satisfaction in the highly competitive market landscape of today. The goal of this study is to employ machine learning techniques to create a strong consumer segmentation model that will classify customers according to their demographics, behaviors, and purchase histories. Through the use of multiple clustering methods, including K-means, DBSCAN, and Hierarchical Clustering, the model seeks to find unique customer segments with shared attributes. To accomplish optimal segmentation, the segmentation process entails three steps: feature selection to identify the most significant features, model training, and data preprocessing to manage missing values and outliers. In-depth segment analysis is also included in the report to offer practical insights for better client retention tactics, tailored recommendations, and focused marketing efforts. The results of the study illustrate how machine learning may be used to find hidden patterns in consumer data, giving organizations the ability to make data- driven decisions. Organizations may improve their marketing efforts, allocate resources more efficiently, and eventually increase customer engagement and profitability by putting this customer segmentation strategy into practice. Key Words: K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Cluster Plotting, Heatmaps, customer relationship management (CRM) system.