The study examines customer segmentation through two frameworks: Recency, Frequency, Monetary (RFM) and Customer Lifetime Value (CLV). It evaluates five clustering algorithms (K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models, and Fuzzy C-Means) on a UK E-Commerce data. Using internal metrics like Silhouette Score and Calinski–Harabasz Index, the study highlights the strengths and limitations of each algorithm, offering insights on aligning segmentation methods with specific business objectives.