In the contemporary digital landscape, e-commerce has rapidly evolved, reshaping consumer behaviour and the retail industry. This study explores the dynamic factors affecting customer satisfaction and profitability in e-commerce. Utilising a comprehensive methodological framework that includes fuzzy Delphi, fuzzy BWM, C-means clustering, and fuzzy COPRAS methods, the research identifies and prioritises critical success factors influencing online consumer behaviour. An analysis of 602 customer records classifies purchasing behaviours and demographic characteristics into distinct clusters. Key findings underscore the significance of customer reviews, recommendation systems, shopping satisfaction, and personalised shopping experiences in driving e-commerce success. Methodologically, the study demonstrates the effectiveness of advanced analytical techniques in managing uncertainty and complexity in e-commerce data. Despite its contributions, the research acknowledges limitations, including the cross-sectional nature of the data, sample size constraints, and the need for broader generalisability. Future research directions include integrating emerging technologies, conducting longitudinal studies, and developing cross-channel strategies to enhance customer engagement and profitability.