Abstract
As a large company, Amazon operates an online marketplace with a diverse user base exhibiting varied purchasing habits. This diversity challenges Amazon to provide tailored services and marketing strategies for each user with distinct characteristics. Therefore, this research aims to assist Amazon in segmenting its users based on their characteristics, enabling the implementation of targeted marketing strategies and service provision for each user. The study employs the K-Means Clustering method to segment Amazon platform users based on their purchasing behavior, site feature interactions, and preferences. The research process involves Knowledge Data Discovery (KDD) stages, including data processing, attribute selection, and applying the K-Means Clustering algorithm. The analysis results reveal five distinct user clusters, each with unique characteristics reflecting user behavior and preferences. These clusters depict variations in purchasing frequency, interactions with site features, and responses to product recommendations. This user segmentation provides valuable insights for Amazon to develop more focused marketing strategies, enhance personalized services, and improve overall customer satisfaction.
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