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.
Mine support tickets, chat logs or inbound emails for recurring problems, confusion and unmet needs that should shape your persona and your marketing.
The full prompt is included in the pack.
Get the pack — $14Why Gemini: Gemini and Claude handle very large ticket exports well; for smaller batches any model works.
3 tips for this prompt come with the pack.
Preview of the example output. The full example comes with the pack.
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.
You are a customer research analyst. I want to understand who our customers are from what they write in reviews. Business: [business description] Reviews: [reviews] 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. Analyze the reviews and give me: 1. Customer segments: 2-4 distinct groups you can see in the reviews (based on situation, goal or experience level, not invented demographics). For each, quote 2 reviews that show it and estimate how many reviews fit it (count them, do not guess percentages). 2. Triggers: what was happening in their life or business that made them look for this. 3. Desired outcomes: what "success" looks like in their words. Quote phrases verbatim. 4. Delighters: what exceeded expectations. 5. Frustrations and deal-breakers: what disappointed people, especially in 1-3 star reviews. 6. Language bank: 15 exact phrases customers use that would work in marketing copy. 7. What the reviews cannot tell us: important persona questions this data does not answer. Keep it skimmable. Do not write a polished persona yet; this is raw material.
Yes. It is plain-language and written to work in all three. Best fit: Gemini. Gemini and Claude handle very large ticket exports well; for smaller batches any model works.
Replace each bracket with your own details: [business description], [support tickets]. The more specific your input, the more useful the result.
It is part of the Customer persona builder pack ($14, one-time), which includes 8 prompts. The $59 All-Access Bundle includes every pack.
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.
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.
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.
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.
Map the functional, emotional and social jobs your customer is "hiring" you to do, including what they would use instead, so your persona explains why people buy, not just who they are.
Map how your persona goes from first noticing the problem to buying, with the questions, objections and information they need at each stage.