What it is
Qualitative conversations with representative users about their needs, mental models, behaviors, and context. They reveal the "why" behind what people do, which usage data cannot.
Why use it
Direct contact surfaces needs, frustrations, and context teams cannot reliably predict, and prevents building for a problem users do not have.
Use this when
- The problem or user needs are unclear or contested
- You need to test assumptions about user behavior
- Usage data raises questions needing qualitative explanation
- You are designing for users the team rarely reaches
You may not need this when
- Recent research already answers the question
- The change is minor and does not affect user workflows
- Strong proxy evidence exists (support tickets, analytics, prior studies)
Dependencies — what you need
- Access to target personas or segments
- Recruitment support: lists, incentives, outreach
- Clear interview objectives and guiding questions
- Interview tools such as Hubble, Microsoft Teams, or in person
People involved
- Design
- Research
- Product
Typical outputs
- Recordings or transcripts
- Themes, with the number of participants behind each
- Verbatim quotes illustrating key insights
- Assumptions supported, contradicted, or not tested
Template
User Interviews template
A fill-in structure for running this method: what to prepare, what to do, what to record, and how to read the result.
How AI can help
AI as augmentation — not replacement
- Draft an interview guide from the objectives
- Generate hypotheses to test
- Summarize transcripts for themes, for the researcher to check
- Find patterns across transcripts, citing quotes
- Draft screener criteria for recruiting
AI prompt
A prompt for any AI assistant. It works only from material you paste in, keeps assumptions apart from findings, and leaves the decisions to you.