WPIS: Machine Learning-Driven E-Commerce Churn Prediction Integrating Technical Metrics and Information Quality for Strategic MIS Decision Support

Abstract

E-commerce website performance—encompassing both technical delivery quality and information quality—is associated with customer retention: platforms exhibiting poor technical metrics (session friction, high cart abandonment) or poor information quality (low engagement, weak content relevance) tend to show elevated churn. This study operationalizes website performance prediction by mapping behavioural and transactional signals to a Technical Metrics sub-vector Ti and an Information Quality sub-vector Qi (theoretically assigned proxy indicators), constructing a Website Performance Influence Score (WPIS), and training an XGBoost prediction engine that achieves 92.1±0.3% accuracy across five stratified train–test splits, with all preprocessing parameters fit exclusively on each split’s training fold. Using a publicly available E-Commerce Customer Behavior dataset of 50,000 records, the model attains F1-scores of 0.95 (low-risk) and 0.86 (high-risk) and an AUC-ROC of 0.93 for the churned class. Feature importance, cross-validated against SHAP attributions, identifies customer service interactions, customer lifetime value, discount usage rate, and cart abandonment rate as the dominant performance-failure signals, with the engineered Information Quality Degradation Index (IQDi) and Technical Performance Failure Score (TPFi) ranking sixth and ninth by gain. A Strategic MIS Decision Support Layer stratifies customers into risk tiers and maps each tier to illustrative, scenario-based intervention strategies. These results support the hypothesis that ensemble learning captures non-linear interactions between technical and information-quality dimensions, offering a reproducible, theoretically grounded foundation for data-driven MIS decision-making in digital commerce.