Optimizing Motorcycle Sales: Enhancing Customer Segmentation with K-Means Clustering and Data Mining Techniques
Abstract
Information plays a crucial role in the sustainability of company operations. The development of information technology, especially in the industry 4.0 era, affects various fields including economics, social, and education. The company faces challenges in declining motorcycle sales due to intense competition and ineffective customer segmentation. To address these issues, this study proposes the use of the K-Means algorithm with Python tools for better customer segmentation. The study aims to identify diverse customer groups and tailor marketing strategies accordingly. By utilizing the Elbow method and Silhouette score, the analysis of customer data is simplified. This study also employs data mining techniques to uncover hidden patterns in motorcycle sales data, aiding companies in improving operational efficiency and decision-making.
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