<![CDATA[This study discusses customer segmentation strategies based on purchasing behavior and brand preferences for information technology products at PT. XYZ. The main objective of this research is to identify customer purchasing patterns and classify them into several segments with different characteristics. The historical transaction data used includes attributes such as the brand of purchased products, purchase frequency within one year, and total transaction value. After the data cleaning and normalization process, a centroid-based clustering technique was applied to identify homogeneous groups in the database. The clustering results show three main clusters, each representing different consumer behaviors in terms of brand loyalty, price sensitivity, and spending level. The analysis indicates that customers with high transaction values tend to select specific brands and make purchases more frequently. These findings provide strategic insights for the company in designing more personalized marketing approaches, improving the effectiveness of product offerings, and strengthening relationships with customers in each segment.]]>
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