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Summary:
Traditional personas often focus on demographics, but they don’t always provide useful insights for product design.
In this experiment, I explore Functional Personas—focusing on user goals, behaviours, pain points, objections, and needs. I also explore how AI can combine research data and create personas from different perspectives to support better design decisions.
The benfits of functional personas
Light the load
They are easier to update without large research cycle
Stay current
They are easy to produce and mantain
Outcome oriented
Focus on what users are trying to achieve and the evidence that supports those needs. This creates a clear link between user insights and actual product decisions
Actions 1:
I created a dedicated project in Claude and uploaded publicly available information and research. I instructed Claude to act as a UX researcher and create realistic, functional personas based on the research. The personas are segmented by needs, tasks, questions, pain points, and goals, with the reasoning behind each segment.
I also asked Claude to run a deep research on user feed back on popular fitness app like Strava and Apple fitness.
Actions 2:
I instructed AI to conduct deep research on popular fitness apps, including Apple Fitness, Google Health, PUSH, Runna, Strong, Hevy, Runkeeper, and Gymverse.
The research focused on user feedback, pain points, goals, needs, sentiment, tasks, and questions, with particular attention to people with obesity and diabetes. I then cross-referenced these findings with the project data to identify common patterns and insights.
Actions 3:
Generate personas based on user needs and pain points for Nutrition-Tracking, not demographics. Segment users by their goals, needs, behaviours, tasks, and challenges. Use the project research to identify and create the most meaningful persona segments.
Claude and Figma can generate an informative Venn diagram showing two key personas: Precision Tracker and Simplifier.
The project can also generate additional personas based on different features, user needs, or product perspectives.
Conclusion:
AI can process large amounts of data quickly, handle data entry, cross-reference different sources, and organise information based on specific directions. However, data quality and AI hallucinations are still a concern.
The personas generated by AI are best used for idea generation and reference, not as a replacement for real user research. Product decisions should still be validated with real users input.
The main benefit of quickly generating these design artefacts is that they help teams explore ideas, rule out weaker directions, and compare different approaches quickly