Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data

TL;DR

This paper presents AWUM, a novel web usage mining methodology that enriches interaction data from CAWAL to better understand user behavior and optimize user experience on large-scale web platforms.

Contribution

It introduces AWUM, a new approach that enhances web usage data analysis by integrating enriched datasets from CAWAL for improved UX insights.

Findings

87.16% of sessions involved multiple pages

40% of users accessed various services

50% of users opted for secure exits

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.