Augmented Web Usage Mining: Optimizing User Experience with Enriched Data

Article & Journal: Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data Published in: International Journal of Human–Computer Interaction

Study Summary: This research introduces the concept of "Augmented Web Usage Mining." We leveraged the rich data attributes provided by CAWAL (such as scroll depth, interaction timing, and device-specific metrics) to perform a granular analysis of user experience. Unlike traditional clickstream analysis, this study maps the "why" behind user actions. We identified UX bottlenecks and proposed layout optimizations that measurably improved user engagement and navigation efficiency.

Behind the Research: Knowing that a user left a page is easy; knowing why they left is hard. By enriching standard web mining with behavioral metrics, we turned raw data into a narrative of user experience. This paper bridges the gap between technical data mining and human-computer interaction (HCI). It proves that when you have granular, owned data, you can optimize your web applications not just for performance, but for genuine human satisfaction.

Citation & DOI: Canay, O., & Kocabıçak, Ü. (2025). Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data. International Journal of Human–Computer Interaction, 41(11), 7152–7171. DOI: 10.1080/10447318.2025.2495839