User Feedback Analysis Framework
Create a structured framework to analyze this user feedback data: [paste feedback or describe feedback source]. The analysis should: 1) Identify and categorize common themes using affinity mapping, 2) Quantify frequency and sentiment for each theme, 3) Map issues to specific product areas and user journey stages, 4) Prioritize findings based on user impact, business alignment, and implementation effort, 5) Identify potential quick wins vs. strategic initiatives, 6) Recommend 3-5 specific product improvements with rationale, and 7) Suggest follow-up research to validate findings.
How to Use This Prompt
- Copy the prompt using the button above
- Replace placeholders in [brackets] with your specific details
- Paste into your AI assistant (ChatGPT, Claude, Gemini, etc.)
- Iterate as needed - ask follow-up questions to refine the output
Why This Prompt Works
This prompt transforms raw, unstructured user feedback into a prioritized, actionable analysis. By combining affinity mapping with sentiment quantification and a prioritization matrix (impact, alignment, effort), it produces output that goes beyond theme identification and delivers specific product improvement recommendations with clear rationale.
When to Use This Prompt
- After collecting a batch of NPS responses, support tickets, or app store reviews that need synthesis
- When preparing for a product review meeting and you need data-backed recommendations from user feedback
- Before a roadmap planning session to ensure user voice is represented with structured evidence
Tips for Better Results
- Paste actual feedback snippets directly into the prompt rather than summarizing them yourself for more accurate theme detection
- Specify the feedback source (in-app survey, support tickets, G2 reviews) so the AI adjusts its analysis approach accordingly
- Include the number of responses and time period so the AI can gauge statistical significance of the patterns it identifies