Synthesize Raw User Feedback
Turn a pile of reviews, support tickets and survey answers into clear themes, patterns and concrete next steps.
Last updated: July 2026 · Collective Brain
Good for
- Condense app-store reviews and support tickets into a few clear core themes
- Sort open survey answers by satisfaction, friction and feature requests
- Prepare interview findings for a team update or roadmap discussion
The prompt
You are an experienced UX researcher who synthesizes raw user feedback in a structured way.
Context:
- Product: [your product and what it does]
- Feedback source: [e.g. app-store reviews, support tickets, survey]
- What I want to decide with the results: [e.g. Q3 roadmap, prioritization]
Here is the raw feedback (each line or paragraph is one response):
[paste your collected feedback here]
Task:
1. Read all the feedback and cluster it into 4 to 7 clearly named themes.
2. For each theme, describe: the core point, how often it appears (roughly, as a percentage or count), and whether it is positive, negative or mixed.
3. Back each theme with 1 to 2 verbatim quotes from the original feedback.
4. Name overarching patterns and contradictions across themes.
5. Derive one concrete recommendation per theme and prioritize them by impact and effort.
Output format:
- Short summary in 3 sentences
- Table: Theme, Frequency, Sentiment, Quote, Recommendation
- Section on patterns and contradictions
- Prioritized top 3 actions
If you lack the data to support a claim, say so openly instead of guessing. Do not invent numbers.Replace the bracketed placeholders with your own details.
Frequently asked
How much feedback can I paste at once?
ChatGPT can only process a limited amount of text in one pass. For very large volumes, split the feedback into blocks, cluster each block, and then merge the partial results at the end using the same prompt.
Can I trust the numbers and percentages?
The frequencies are estimates from the pasted text, not exact statistics. The prompt tells ChatGPT to flag missing data and avoid inventing numbers. For reliable figures, verify the clusters against the original source.
Related
Rather have it done?
Prompts are a start.
Results are our job.
When the prompt should turn into real work that holds up consistently across every channel, we take over. Start free, finish professionally.