Pain-Point Persona Summarizer (Feedback → JTBD/Pain → Insight Cards)
Turn feedback into persona-specific JTBD pain summaries and ready-to-share insight cards that guide design and copy.
Prompt Overview
Tips For You
- Limit to 3–4 personas to preserve signal. - Add a column for ‘evidence count’ next to each pain to avoid opinion creep. - Use Insight Cards as inputs to prioritization workshops.
From Operations TeamNexusAi TechnologyProblem It Solves
Teams lack crisp, persona-level pain statements. This creates generic features and bland messaging.
Behavior-based personas
Grounded in tasks, not vague demographics.
JTBD statements
Clear problem framing for product decisions.
Evidence counts
Quantifies how often each pain appears.
Shareable cards
Ready for design and roadmap reviews.
AI Prompt Instructions
Act as: A UX researcher and product strategist specialized in JTBD and persona synthesis.
Why this task matters: Strong personas anchor product and UX decisions. We need crisp pain statements that reflect real tasks, struggles, and outcomes.
Important boundaries:
- No archetype stereotypes. Base personas on actual behaviors present in the text.
- Focus on task failures, friction, and desired outcomes—not demographics.
User inputs:
- Raw feedback lines or interview snippets.
- Known customer segments (e.g., SMB admin, enterprise security lead) if available.
Objectives:
1) Extract up to 4 personas grounded in behaviors.
2) For each, craft JTBD statements (When..., I want to..., so I can...).
3) Summarize top pains, triggers, and success metrics for each persona.
4) Produce shareable Insight Cards.
Analysis workflow:
1) Scan text for repeated tasks, contexts, and constraints.
2) Group by behavioral patterns. Name personas with functional labels (e.g., Billing Admin, Onboarding New Hire).
3) For each persona, list pains ranked by frequency and severity; include direct quotes.
4) Draft JTBD statements and define measurable outcomes (time saved, errors reduced, revenue protected).
Required output format:
- Overview: personas detected and confidence.
- Insight Cards per persona: Name, JTBD, Top Pains, Triggers, Desired Outcomes, Quotes (2–3), Product Opportunities.
- Appendix: methodology, assumptions, and data gaps.
Quality controls:
- Avoid overfitting: do not create more than 4 personas from thin data.
- Keep quotes short and attributable to the pain they illustrate.
Verification checklist:
- Do pains map directly to observed tasks?
- Are proposed opportunities testable and narrow?
Final instruction:
Return the complete set of Insight Cards with concise, actionable language suitable for design reviews and roadmap discussions.
Expected Outcome
Persona: Billing Admin (Confidence: High) JTBD: When reconciling monthly invoices, I want to match payments quickly so I can close the books without chasing missing data. Top Pains: Payment status mismatch (Severity 4), export errors (3) Quotes: "CSV export drops last column." "Status shows paid but ledger says pending." Opportunities: Add audit trail, fix CSV schema, surface reconciliation checklist.
Implementation Journey
Synthesize in Claude
Open Claude and paste interview or feedback snippets with any known segments. Request persona extraction with JTBD and pain ranking using the prompt. Expect concise Insight Cards.
10-15 minOrganize in Google Sheets
Copy the Insight Cards into a sheet with columns for Persona, JTBD, Pain, Evidence Count, Severity. Filter by severity to flag top issues for discovery sprints.
10 minPublish to Notion
Create a Notion page per persona. Link related tickets and research notes so PMs and designers can reference quotes during planning.
10 min
