Persona signals from customer reviews
Paste reviews (yours or a competitor's) and extract who is buying, what triggered the purchase, what they value and what disappointed them, with every insight tied to a quote.
Free AI prompt · ChatGPT · Claude · Gemini
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Synthesize customer interviews into persona insights
Turn messy interview transcripts or call notes into a structured synthesis: patterns across customers, notable differences, and the quotes that matter, ready to build personas from.
What you'll fill in
- [business description] What you sell and to whom e.g. “Northwind Bookkeeping: monthly bookkeeping for Shopify stores”
- [research question] What you want to learn from the interviews e.g. “Why do store owners finally decide to hire a bookkeeper, and what almost stops them?”
- [interview notes] Transcripts or notes from 3-15 customer interviews or sales calls. Label each interview (Customer A, B, C...). Remove personal details you do not need. e.g. “Customer A (candle store, 2 yrs): "I didn't know if we were profitable until our accountant told us in April..."”
Copy the prompt
Step 1 — Per-interview summaries
You are a qualitative researcher. I ran customer interviews to answer this question: [research question] Business: [business description] Interview notes (labeled by customer): [interview notes] Evidence rule: ground every claim in the data I pasted. Label each insight as one of: (evidence) when it is directly supported, with a short quote or reference; (inference) when it is a reasonable reading of the data; (assumption) when the data does not cover it. Never invent quotes, demographics, percentages or customer names. If the data is too thin to support a conclusion, say so. For each interview, write a compact summary: - Context: who they are and their situation (only what they said) - Trigger: what made them act now - Before: how they handled the problem previously - Desired outcome: in their words - Hesitations: what almost stopped them - Decision factors: what tipped them over - Best 2 quotes (verbatim) Keep each summary under 120 words. Do not merge interviews yet.
Step 2 — Patterns across interviews
Now synthesize across all interviews: 1. Patterns: themes that appear in 3 or more interviews. For each, list which customers mentioned it and one representative quote. 2. Differences: where customers split into meaningfully different groups (by situation, motivation or decision process). Describe each potential persona group in 2 sentences and list which customers belong to it. 3. Surprises: anything that contradicts what a business like this would normally assume. 4. Answer to the research question in 4-6 sentences, labeled with evidence strength. 5. Gaps: what we still do not know and who we should talk to next (for example, people who churned or chose a competitor). Stay strictly within the interview data.
How to use it
- Open a new chat in Claude (the others work too).
- Paste step 1 and replace every [bracket] with your details.
- When it replies, paste the next step in the same chat.
- Review the output, check any facts, and edit to taste.
Why Claude: Long transcripts fit comfortably in Claude or Gemini; in ChatGPT you may need to paste one interview per message.
Tips
- Five good interviews reveal most of the major patterns; you do not need 50 to start.
- Strip names and personal details before pasting, especially in shared workspaces.
- Interviews with people who chose a competitor or canceled are often the most useful.
Example output
Customer A — candle store, 2 years
Context: sells on Shopify, ~400 orders/month, did own books in spreadsheets.
Trigger: accountant flagged a $9k discrepancy in April.
Before: monthly CSV exports, "catch up when I panic."
Desired outcome: "Know if we're profitable without waiting for April."
Hesitations: cost; worried about giving access to bank accounts.
Decision factors: read-only access; flat monthly price.
Quotes: "I didn't know if we were profitable until April." "I was scared of handing over the keys."
(Customers B-F summarized similarly.)
Patterns across 6 interviews:
- Tax-season shock as trigger (A, C, D, F) — "The accountant's list of questions was the last straw." (evidence)
- Fear of losing control of money (A, B, E) — "Who else can see my bank?" (evidence)
- Price compared to their own time, not to other bookkeepers (C, D, E) (evidence)
Potential persona groups:
1. Reactive fixers (A, C, D, F): act after a painful tax season; want catch-up help fast.
2. Planners scaling up (B, E): growing quickly; want monthly numbers to make inventory decisions.
Surprise: nobody compared us to software like QuickBooks alone; they compared us to doing it themselves.
Gaps: no interviews with people who chose a competitor or stopped after onboarding.
FAQ
Does this prompt work in ChatGPT, Claude and Gemini?
Yes. It is plain-language and written to work in all three. Best fit: Claude. Long transcripts fit comfortably in Claude or Gemini; in ChatGPT you may need to paste one interview per message.
What do I need to fill in?
Replace each bracket with your own details: [business description], [research question], [interview notes]. The more specific your input, the more useful the result.
Is this prompt free?
Yes. Copy it above and use it as often as you like. It is one of the free samples from the Customer persona builder pack.
Will I get the same result as the example?
No. The example was made for a fictional business to show the shape and quality of the output. Your result depends on your input and the model, so review and edit before you use it.
Do I need a paid AI plan?
Usually not. Free plans handle most prompts. Paid plans help when you paste in long documents or run a long multi-step sequence in one chat.
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