Visualizing Clickstream Prediction Logic and Utility

Understanding user behavior patterns from clickstream data is critical for improving e-commerce decision-making. In this study, we propose a novel visualization framework called SankeyX that connects user interaction sequences, model predictions, SHAP-based feature attributions, and business utility into a unified Sankey-style diagram. It enables users to trace how behavioral patterns contribute to model outcomes and assess their financial impact by a utility matrix. In the case study, we also demonstrate its ability to reveal dominant purchasing patterns by using a real world dataset. This method bridges the gap between explainable AI and decision-making for clickstream prediction.

Authors

• Danny Yu-Chung Wang
• Lars Arne Jordanger
• Jerry Chun-Wei Lin

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