This study aims to enhance PT Maspion's marketing strategy effectiveness by clustering customers using the K-Means Clustering algorithm. By leveraging customer transaction data, this research successfully grouped customers into four clusters based on their purchasing patterns. Each cluster was analyzed to identify key characteristics and provide relevant marketing strategy recommendations, such as volume-based discounts, personalized services, and loyalty programs. The results indicate that implementing K-Means Clustering helps PT Maspion better understand customer needs, increase loyalty, and optimize company revenue. This study offers practical contributions to the company and enriches academic literature on the application of machine learning in marketing.
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