Persona
What did we learn about them?
Overview
We often use abstract labels like “citizen”, “user”, or “resident”. But these terms mask the wide diversity of needs, capacities, and lived realities that shape how people engage with services. Without a clear understanding of who we are designing for, it's easy to develop services that don’t meet real needs—especially for those most at risk of exclusion. Persona helps teams build realistic, human-centred reference profiles based on actual interviews and observations. These are not fictional characters, but representative summaries of different types of people you have encountered during your discovery research. Personas ensure your decisions are guided by real users, not assumptions.
⏱️ Time: 45–60 minutes
👫 Participants: Team members who conducted or reviewed the research
🛠️ Materials: Persona template, interview notes, markers/post-its
Input
Before completing this canvas, ensure you have completed the following steps:
User InterviewUser ObservationContext
Use this tool once you’ve gathered qualitative insights (e.g. interviews, observations) and begun to notice patterns among different types of users. It is especially helpful before ideation or prototyping, to ensure solutions are grounded in lived experience and include the perspectives of diverse or underserved groups. You may have many types of users and stakeholders. Choose one key user groups first to represent with a persona. You can repeat the exercise later for other user groups if needed.
Recipe
Identify patterns in your research
Review your notes from the interview and observation, and cluster people with similar situations, needs, or behaviours.
Results
A small set of clear, grounded user profiles that represent the range of people affected by your service. These can be used to:
Align your team on who you’re designing for
Test ideas against real-life needs
Avoid designing for a hypothetical “average” user
Ensure inclusion of less visible or underserved groups
Tips
Base your personas on real evidence—not just what “seems typical”.
Avoid stereotypes or over-simplification. Focus on meaningful differences that impact the service experience.
Don’t wait for perfect data—start with what you know, and refine as you learn more.
Include perspectives that might otherwise be ignored: low literacy, disability, rural users, etc.
Use them actively—refer back to them during idea generation, testing, and decision-making.
We'd love to hear how you're using this tool! Please share your examples and feedback to inspire others and help improve the Booster. Submit your example today and be part of our community.
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