<![CDATA[This study aims to analyze consumer segmentation on e-commerce platforms by employing the K-Means algorithm as the primary clustering method. Using the Online Retail (UK) dataset, which contains comprehensive transaction records from a UK-based online retail company, the research focuses on identifying behavioral patterns among consumers. Several key variables, including purchase frequency, total transaction value, and recency or visit time, are processed to create meaningful clusters that represent different types of consumer behavior. The K-Means algorithm is applied through a series of preprocessing steps, such as data cleaning, feature selection, and normalization to ensure accurate clustering results. Once the clusters are formed, each consumer group is analyzed to determine its characteristics, purchasing tendencies, and potential value to the business. The segmentation results provide valuable insights for businesses in developing targeted marketing strategies and personalized service offerings. By understanding the unique preferences and behaviors within each cluster, companies can optimize promotional efforts, improve customer retention, and enhance overall user experience. The findings indicate that data-driven segmentation using the K-Means algorithm is a highly effective approach for gaining deeper, actionable insights into consumer behavior, thereby supporting more strategic decision-making in the e-commerce environment.]]>
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