We'll apply unsupervised learning to discover natural groupings within the customer base through behavioral clustering. Our process begins with feature scaling to ensure all variables contribute equally to the distance-based calculations required for the k-means algorithm. We'll utilize the elbow method to mathematically determine the optimal number of clusters and then execute the clustering process to assign every customer to a specific segment. To interpret these high-dimensional groups, we'll apply Principal Component Analysis for 2D visualization and profile each segment to create actionable customer personas for personalized engagement strategies.