Analyzes user card sorting data to generate data-driven information architecture and navigation recommendations.
This skill transforms raw card sorting results into actionable information architecture insights for UX researchers and designers. It processes data from both open and closed sorting studies to identify common grouping patterns, naming conventions, and user mental models. By generating similarity matrices and flagging ambiguous items that participants struggled to categorize, the skill helps teams build intuitive navigation structures backed by empirical evidence, significantly reducing the manual effort required to synthesize research findings.
Key Features
01Identifies dominant category patterns and naming conventions
02Generates similarity matrices to visualize item relationships
03699 GitHub stars
04Proposes hierarchical navigation and IA structures
05Analyzes both open and closed card sorting methodologies
06Flags inconsistent or ambiguous items for further research
Use Cases
01Synthesizing results from user research tools like spreadsheets or CSV exports
02Designing navigation menus for complex websites or applications
03Restructuring existing content based on actual user mental models
