Personas: AI-generated user profiles that represent different types of people from your interviews. Think of them as fictional characters based on real data - each persona captures the behaviors, motivations, and characteristics of a group of similar participants. This helps you understand your audience in digestible segments rather than trying to process hundreds of individual responses.Classification: The process where AI automatically sorts your interview participants into personas based on their responses. Instead of manually reading through every transcript to find patterns, the system identifies common traits and groups similar people together under each persona.Persona Chat: An interactive feature that lets you have conversations with your personas as if they were real people. The responses come from actual interview data, so when “Lucy” answers your questions, she’s drawing from real quotes and insights from participants who match her profile.
Start with quality data: The more detailed and varied your interview transcripts, the richer and more accurate your personas will be. Aim for at least 10-15 interviews before generating personas.
Use both approaches strategically: If you have existing personas from other research, start with those to see how your current data maps to known user types. Use auto-generation when exploring new markets or validating assumptions.
Leverage the chat feature for presentations: Instead of showing stakeholders raw data, have a conversation with a persona during meetings. Ask “Lucy” about feature preferences or pain points to make insights more memorable and relatable.
Cross-reference with quotes: Always click through to the actual transcript quotes that support persona characteristics. This helps you understand the “why” behind each trait and adds credibility to your findings.