As part of the Quant UX course at Télécom Paris, I collaborated with a team to evaluate the usability of Goodreads using a mix of quantitative and qualitative research methods.
Our goal was to assess how intuitive, efficient, and satisfying it is for users to discover book recommendations on the platform.
We conducted structured usability testing with 16 participants and collected data through click tracking, cursor trajectory, task completion time, and post-task surveys. Participants performed three key tasks: freely exploring the site, completing mini-challenges across core features (like ‘Similar Books’ and ‘Explore’), and finding a book for a friend using the profile feature.
Using Python (pandas, numpy, matplotlib, seaborn), we analyzed cursor movement data and generated heatmaps to visualize user interaction. We also performed thematic analysis on qualitative feedback to identify key usability issues. Our findings revealed pain points in navigation, personalization, and the outdated design of the community section, while highlighting strengths in book discovery and friend-based recommendations.
My contributions included:
- Designing and conducting user testing protocols
- Collecting and processing cursor-tracking and survey data
- Creating visualizations to support findings (heatmaps, bar charts, correlation matrices)
- Drafting actionable UX recommendations based on user feedback and data
This project strengthened my skills in UX research, data analysis, and the application of human-centered design principles to real-world digital platforms.
