QUALITY OVER QUANTITY IN GLOBAL E-COMMERCE: DECODING PERFORMANCE THROUGH EXPLAINABLE MACHINE LEARNING AND STRATEGIC SIMULATIONS
Description
This dataset contains the performance and user interaction metrics for the 50 most visited e-commerce platforms globally as of June 2025. It was compiled to support the research article titled "Quality Over Quantity in Global E-Commerce: Decoding Performance Through Explainable Machine Learning and Strategic Simulations". The data includes nine key performance indicators: Average Visit Duration, Page Visits, Bounce Rate, Performance Coefficient, Speed Index, Unique Visitors, Monthly Visits, Traffic Share, and Month-over-Month (MoM) Traffic Change. Data Collection Methodology: The raw data was collected from reputable digital analytics tools, including SimilarWeb, GTmetrix, and Google PageSpeed Insights. The dataset allows for the replication of the hybrid methodology involving CRITIC-based weighting, multiple MCDM methods (TOPSIS, VIKOR, etc.), and machine learning predictions using SHAP analysis.
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Institutions
- Firat Universitesi