Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data
This work addresses user experience optimization for web analytics practitioners, but it appears incremental as it builds on existing web usage mining with enriched data.
This study tackled the problem of understanding user behavior on the web to optimize user experience by introducing Augmented Web Usage Mining (AWUM), which enriched data from CAWAL, resulting in findings such as 87.16% of sessions involving multiple pages and 40% of users accessing various services.
Understanding user behavior on the web is increasingly critical for optimizing user experience (UX). This study introduces Augmented Web Usage Mining (AWUM), a methodology designed to enhance web usage mining and improve UX by enriching the interaction data provided by CAWAL (Combined Application Log and Web Analytics), a framework for advanced web analytics. Over 1.2 million session records collected in one month (~8.5GB of data) were processed and transformed into enriched datasets. AWUM analyzes session structures, page requests, service interactions, and exit methods. Results show that 87.16% of sessions involved multiple pages, contributing 98.05% of total pageviews; 40% of users accessed various services and 50% opted for secure exits. Association rule mining revealed patterns of frequently accessed services, highlighting CAWAL's precision and efficiency over conventional methods. AWUM offers a comprehensive understanding of user behavior and strong potential for large-scale UX optimization.