Analysis of Customer Segmentation Using Unsupervised Learning

In today’s data-driven world, understanding customers better is key to delivering personalized experiences and staying competitive. This project dives into how unsupervised machine learning, especially clustering techniques, can be used to group customers based on their behavior and predicted future actions. We focus on behavioral and predictive segmentation—two powerful approaches that help uncover what customers do and what they’re likely to do next. The process starts with collecting rich customer data, including their interactions, purchases, demographics, and preferences. After cleaning and preparing the data through feature engineering, we apply clustering algorithms like K-Means, Hierarchical Clustering, and DBSCAN to uncover natural patterns and customer groups—without needing any predefined labels. The outcome is a set of meaningful customer segments that can be used dynamically or statically across various business functions. These insights help companies tailor their marketing, boost customer engagement, and make smarter business decisions. By combining behavioral signals with predictive models, this approach offers a scalable way to segment customers more intelligently and effectively