Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data
Abstract
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.
Keywords
Cite
@article{arxiv.2510.17253,
title = {Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data},
author = {Özkan Canay and {Ü}mit Kocabıcak},
journal= {arXiv preprint arXiv:2510.17253},
year = {2025}
}
Comments
19 pages, 5 figures. Published in International Journal of Human-Computer Interaction (Taylor & Francis, 2025)