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Empathy Map From Interview Data

Table output Has example

Capture what users say, think, do, feel.

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Create empathy maps. Interview notes: [INTERVIEW_NOTES]. User segment: [SEGMENT].

For each segment fill four quadrants plus pains and gains: Says (direct quotes), Thinks (inferred beliefs, labeled as inference), Does (observed or reported behavior), Feels (emotions with evidence), Pains (frustrations, risks), Gains (desired outcomes, success measures). Cite participant IDs. Highlight contradictions between what they say and what they do.

Return the map as a table, then 5 insights in the form "[User] needs [need] because [insight]" and three design questions ("How might we..."). Add a quality check: every statement is supported by data, quotes are verbatim, and inferences are labeled. Provide a method to compare maps across segments.

Give one filled example map for the most common segment in my data, cite the interview ID for each entry, and add the questions we should still ask to fill the empty quadrants.

Write each quadrant entry as a short phrase of at most 15 words, with the interview ID in brackets after it.

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Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.

See an example result (fictional data)
Segment: new managers (7 interviews).
Says: "I do not know what to ask in one-to-ones" (P2). "I do not want to look weak" (P5).
Thinks (inference): "If I ask for help, they will think I'm not ready."
Does: copies questions from articles; skips meetings when behind (P3, P6).
Feels: anxious before reviews (P2, P4, P7).
Pains: no time for preparation (6 of 7); conflicting advice (4 of 7).
Gains: confident conversations; team trust.
Contradiction: they say they value feedback but avoid asking for it.
Insights: New managers need ready-to-use questions, because they avoid asking for help rather than lacking motivation.
How might we give them a safe first script?
Compare maps by segment (new versus experienced).

Illustrative only, made-up data. Your results will differ and must be reviewed.

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