This research paper presents a comprehensive analysis of hierarchical clustering methods with a focus on customer segmentation. It explores various linkage criteria, including single linkage, average linkage, complete linkage, and Ward’s method, detailing their processes, advantages, and limitations. Through a step-by-step implementation of a sample dataset, the paper demonstrates how these methods can effectively identify distinct customer groups based on attributes such as age, gender, annual income, and spending score. A dendrogram visualization is used to interpret the hierarchical relationships among data points, highlighting the strengths and challenges of each linkage approach. The study concludes by emphasizing the practical applications of hierarchical clustering in marketing strategies and the necessity for algorithmic improvements to address computational intensity and sensitivity to outliers.