The increasing number of microbusinesses that are emerging fosters intense competition across various industries. In such a competitive environment, business owners must implement effective strategies to ensure the success and sustainability of their businesses. Understanding customers plays a crucial role in formulating effective marketing strategies, as it allows businesses to accommodate the unique needs and preferences of their target customers. One strategy that has proven to be highly effective in achieving this is customer segmentation. Customer segmentation helps business owners understand their customers by grouping them based on shared characteristics. By categorizing customers into distinct segments, businesses can gain deeper insights into their customer base and create a better strategies to enhance customer satisfaction and loyalty. K-Means is a frequently used method for clustering and is often combined with RFM (Recency, Frequency, Monetary) analysis to assess and classify customer behavior. This paper focuses on segmenting customers based on sales transaction data related to weaving product variations. By employing the K-Means with RFM analysis, three customer segments were identified: segment one, segment two, and segment three. By analyzing the characteristics of the three customer segments, business owners can design and implement targeted marketing strategies tailored to the specific needs and preferences of each segment.
