Customer Feedback Synthesis prompts for Product Manager
Ready-to-use AI prompts for customer feedback synthesis — written for Product Manager and easy to paste into ChatGPT, Claude or Gemini.
Synthesize raw feedback into themes
You are a user researcher synthesizing feedback. Here is the raw data:
<feedback>
[feedback]
</feedback>
Product: [product]. Segment: [segment]. Question I am trying to answer: [question].
Cluster into themes by the underlying problem, not the surface wording. For each theme: a name in the customers' own language, how many mentions, representative verbatim quotes, the user segment it concentrates in, whether it is a bug, a usability failure, a missing capability, or an expectation mismatch, and the severity of its impact.
Rank themes by frequency times severity. Then state clearly what this data cannot tell us — sampling bias, who is not represented, and which themes rest on one or two comments.
Analyze user interview transcripts
Act as a research analyst. Here is an interview transcript:
<transcript>
[transcript]
</transcript>
Research goal: [research goal]. Participant: [participant].
Extract: the participant's actual workflow step by step, where they hesitated or worked around something, the moments of visible frustration or delight with the quote, their stated needs versus their demonstrated behavior (call out where these differ), the vocabulary they use for the domain, and anything that contradicts our current assumptions.
Separate what they said from what I might be tempted to conclude. Flag leading questions in the transcript that may have shaped answers. Finish with the three follow-up questions I should ask the next participant to test the strongest emerging pattern.
Turn feedback into a prioritized action list
You are a product manager translating research into decisions. Synthesized findings:
<findings>
[findings]
</findings>
Business goal: [business goal]. Capacity: [engineering capacity]. Current roadmap: [current roadmap].
For each finding, decide: fix now, schedule, investigate further, or accept and document. Justify each with the affected user volume, the severity, and the cost of the fix.
Separately identify: quick fixes worth doing this sprint, findings that indicate a deeper structural problem rather than a surface bug, and findings that conflict with our current roadmap (say which should win). End with what we should tell the customers who raised these, and the metric that would show whether our response worked.