Research Context

This master's thesis explored how UX-driven ad ranking can improve search result relevance in online marketplaces. The research focused on fashion and accessories search results for Austria's largest C2C platform, addressing the lack of an empirically validated ranking model from a buyer's perspective.

Research & Methodology

I conducted a literature review of more than 20 scientific publications, benchmarked competing marketplaces, developed a weighted ad scoring framework, and built an interactive prototype. The final concept was evaluated in a controlled A/B study with 241 participants using relevance ratings, trust ratings, behavioral metrics, heatmaps and the UEQ-S questionnaire.

Ad Scoring Model

Based on literature findings and expert workshops, I identified and weighted relevant ad-quality factors including information completeness, condition clarity, transaction security, pricing and temporal signals. These factors were transformed into a structured scoring model and integrated into the prototype's search ranking.

Results

The UX-driven ranking increased perceived relevance and showed improvements in trust, particularly among frequent marketplace users and second-hand buyers. Behavioral analysis, heatmaps and questionnaire data provided quantitative evidence for the effectiveness of the proposed ranking approach.

Research Contribution

The project resulted in an empirically validated ad scoring model together with a methodological framework that can support future search ranking improvements for digital marketplaces. The findings contribute to UX research on search relevance and evidence-based product decision making.