This study examines the dynamics of online consumer behavior on the Shopee platform, focusing on Dezhaf Hijab, a Muslim fashion brand in Indonesia. With the growth of e-commerce, understanding consumer behavior through a data-driven approach becomes crucial for optimizing marketing strategies and enhancing purchase intention. Utilizing a dataset from December 9, 2023, to January 8, 2024, this research employs CRISP-DM methodology, RFM-D analysis, K-means clustering, Market Basket Analysis (MBA), and Multiple Linear Regression to provide insights into customer segmentation, product bundling strategies, and factors influencing purchase intention. The findings reveal that the cluster analysis identifies four distinct customer segments: Superstar Customers at 50.37%, Golden Customers at 31.92%, Typical Customers at 17.65%, and Dormant Customers at 0.06%, targeted with personalized marketing strategies based on their purchasing behavior. Market Basket Analysis offers additional insights into product associations, facilitating effective cross-selling strategies with 11 product recommendations. The results from Multiple Linear Regression indicate that only UnitPrice (X1) significantly affects Total (Y). This study underscores the effectiveness of integrating various data mining techniques to understand and predict consumer behavior, offering valuable insights into targeted marketing strategies and ecommerce management.