Interview
☻Qualitative Method:
Explore how players feel and behave through interviews, observations, and open-ended feedback.
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Best Stage: Early Concept → Mid Development
Primary Goal: Understanding player needs, motivations, expectations, and pain points
Effort: Moderate
Overview
An interview for game testing purposes is a structured or semi-structured conversation conducted with players before, during, or after a playtest. It helps uncover usability issues, emotional responses, motivations, and expectations that aren’t always visible through analytics. Interviews work best when paired with other methods, offering a balance of qualitative and quantitative data.
How to Run Interviews
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Prepare a Question Guide
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Mix open-ended questions (e.g., “Tell me about...”) and specific ones (e.g., “Was the map easy to read?”).
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Stay neutral and avoid leading phrasing like “Did you like that part?” Instead, ask “What did you think about that part?” (See Build Your Own Survey for more question tips.)
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Record
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Recording interviews (audio or video) allows you to review tone, emotion, and language later.
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Encourage Honest Feedback
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Remind players they’re not being tested and that negative or critical feedback is valuable.
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Be aware of social desirability bias, a cognitive bias where participants give answers they believe are more polite or acceptable rather than their true opinions.
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Use Probes
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Follow up with questions like “What made you say that?” or “Can you show me where you got confused?”
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Take Notes
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Identify patterns in what players liked, found confusing, ignored, or requested improvements on.
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What Interviews Can Reveal That Data Can't
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Why players ignore a feature
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Emotional responses to characters or story beats
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Mental models (e.g., “I thought I had to fight him, but I was supposed to run”)
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Frustration or joy not reflected in telemetry or survey data
Analyzing Interviews
To analyze qualitative data, I usually record the interview, create a transcript, and import it into software like MAXQDA or Dovetail. From there, I code or tag sections of the transcript to mark important insights.
Common Tags I Use:
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“Confusion”
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“Frustration”
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“Liked mechanic”
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“UI issue”
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“Fun moment”
After coding, I use filters to identify which tags appear most often and look for patterns. I then group related tags into themes such as:
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“Onboarding issues”
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“Positive feedback on combat”
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“Misunderstood mechanics”
This helps translate raw feedback into clear, actionable insights for designers and developers.