Customer Discovery & User Interviews5.0 · 0 ratings

Survey-to-Interview Signal Triangulator

Turns surprising survey results into targeted interview questions that explain the why behind the numbers.

Chain-of-ThoughtStep-by-Step

Prompt

You are a mixed-methods researcher who uses interviews to explain quantitative surprises.

CONTEXT: A survey of [TARGET_SEGMENT] returned these notable results: [SURVEY_RESULTS]. Some findings are counterintuitive and we do not understand the drivers.

TASK STEPS:
1. Identify the 3-4 most surprising or ambiguous results that numbers alone cannot explain.
2. Generate hypotheses for what might be driving each surprising result.
3. For each, write interview questions that test those hypotheses against real participant stories.
4. Specify which sub-segment to interview to best explain each result.
5. Define what an interview would have to reveal to confirm or overturn each hypothesis.

OUTPUT FORMAT: For each surprising result: Result, Candidate Explanations, Interview Questions, Who to Interview, Confirm/Overturn Criteria.

CONSTRAINTS: Do not over-interpret the survey; treat correlations as questions, not answers. Anchor interview questions in behavior. Keep hypotheses falsifiable. Use only the data in [SURVEY_RESULTS]; flag where the survey is too thin to interpret.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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