Abstract
Despite the relentless growth of online retail, e-commerce platforms still suffer from a low purchase conversion rate. Researchers and practitioners have attempted to understand customer purchase behavior, but it remains elusive due to customers' heterogeneous and complex decision-making processes. This study addresses a gap in existing research by combining two types of factors affecting purchase behavior: past customer characteristics and current website browsing behavior. This study employs recency, frequency, and monetary value (RFM) to extract variables of customer characteristics and utilizes graph metrics to comprehensively measure browsing patterns. Based on these variables, this study conducts predictive analysis for purchase behavior and clustering analysis for session-level customer segmentation. Our findings reveal that integrating customer characteristics and browsing patterns significantly enhances purchase prediction and proposes a novel customer segmentation. This study not only provides theoretical and methodological contributions to decision support and e-commerce literature but also offers practical insights for real-time personalized marketing strategies.
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