<![CDATA[Customer segmentation is the process of grouping customers based on similar characteristics. It plays a critical role in marketing strategy, enabling businesses to understand consumer behavior and enhance the effectiveness of campaigns. The Recency, Frequency, Monetary (RFM) model and the K-Means Clustering algorithm have proven to be effective in identifying different customer segments. This study uses online retail transaction data from the period 2010-2011. The RFM model is employed to calculate customer value based on recency, frequency, and monetary metrics. Subsequently, the K-Means Clustering algorithm is applied to normalized data to group customers into several segments. The results reveal the existence of three distinct customer segments. These segments, characterized by varying traits, provide a clearer understanding of consumer behavior. By understanding the characteristics of each segment, companies can design more effective and personalized marketing strategies. Furthermore, this study contributes to the advancement of knowledge in the field of data analysis.]]>
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