ABSTRACT

E-commerce has become a vital force in global economic growth and consumer behavior transformation, driven by advancements in AI, big data, and 5G technology. Traditional marketing strategies are increasingly inadequate in meeting rapidly evolving market demands. This study aims to address these challenges by predicting consumer repurchase behavior, thus enhancing marketing precision and customer retention. The research employs a combination of machine learning models and decision support systems to analyze e-commerce data, identify purchasing patterns, and provide actionable insights. The findings reveal that the system significantly improves e-commerce operations, customer satisfaction, and market competitiveness by offering data-driven insights and optimized marketing strategies. The conclusion highlights the system’s potential to transform e-commerce practices. It suggests areas for future research, including the continuous refinement of predictive models and the exploration of broader market applications. This study contributes to the field by providing a practical solution for understanding and enhancing consumer repurchase behavior in e-commerce.

Keywords: E-commerce, predictive analytics, decision support system, machine learning, consumer repurchase behavior.
https://doi.org/10.57180/fkcq5426