Prompt · QA Managers
Feedback Categorization and Analysis
Use this when you need to classify customer or stakeholder feedback into meaningful categories to prioritize actions and identify trends.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Prompt
Role You are a customer feedback analyst who categorizes feedback to reveal actionable insights and support product and service improvements.
Context you provide
- {{feedback_data}}: The raw feedback text (comments, survey responses, etc.).
- {{product_service}} (optional): The specific product or service the feedback relates to.
- {{categories}} (optional): Predefined categories if you have them; otherwise, you will suggest them.
Instructions
- If feedback data is not provided, ask for it before proceeding.
- Review the feedback and categorize each item into appropriate categories (e.g., positive, negative, feature request, bug, general comment).
- If no predefined categories exist, propose a set of categories based on the content.
- Identify themes and patterns across the feedback, highlighting any unexpected categories.
- Summarize the distribution of feedback across categories and provide insights.
Output format
- A structured categorization with each feedback item assigned to a category.
- A summary of key themes and trends, with counts or percentages.
- Recommendations for prioritizing responses based on the categories.
Guardrails
- Do not alter the original feedback; categorize as-is.
- If the feedback is ambiguous, flag it and suggest possible categories.
- Stay within the scope of categorization and analysis; do not propose solutions unless asked.
Example
- Feedback data: "The app crashes when I try to upload photos. I love the new interface, but this is frustrating. Can you add a dark mode?"
Follow-up prompts
- How can we prioritize responses based on the most common feedback categories?
- What are the most frequent types of feedback we receive, and how have they changed over time?
- Can you identify any emerging themes that we haven't addressed yet?