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Review mining: turn a thousand reviews into a product and copy fix list
Ratings are slipping and the team is guessing at why from the last ten reviews they happened to read.
The prompt — copy and run it
You are a consumer insights analyst mining product reviews for actionable product and content fixes. I will paste review text. Produce: A) A THEME TABLE — each recurring theme with its approximate share of the reviews it appears in, sentiment, star-rating association, and whether it is a PRODUCT issue, a PACK/DELIVERY issue, an EXPECTATION-SETTING issue (content promised something the product does not do), or a competitor comparison. B) A FIX LIST split by owner — what R&D/ops must change versus what the PDP copy or imagery can fix this week, ranked by frequency × rating impact. C) FIVE VERBATIMS worth quoting internally, chosen because they are representative, not because they are dramatic — each with the theme it evidences. Input: [PASTE REVIEW TEXT WITH STAR RATINGS AND DATES; NOTE THE RETAILER AND ITEM] Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in the data I give you, write "not provided" and flag it. Mark every claim I should verify against my syndicated data or internal reporting before using it externally. Never include retailer-confidential terms or personally identifiable shopper data. Do not fabricate or paraphrase a verbatim into something the reviewer did not write, and remove any name or personal detail before quoting.
How to use this prompt
- Copy the full prompt above with the Copy button.
- Add your context. This prompt runs as-is — paste it, then add the specific details, data, or files it should reason over.
- Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.
Why this prompt works
The most valuable review signal is usually expectation-setting, not product failure — and it is fixable in an afternoon by rewriting the PDP. Splitting the fix list by owner keeps the fast fixes from being queued behind an R&D roadmap.
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When should I use this prompt?
Ratings are slipping and the team is guessing at why from the last ten reviews they happened to read.
Why does this prompt work?
The most valuable review signal is usually expectation-setting, not product failure — and it is fixable in an afternoon by rewriting the PDP. Splitting the fix list by owner keeps the fast fixes from being queued behind an R&D roadmap.
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