Analyze Survey CSV with One Claude Prompt

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You ran the survey. You got the responses. The CSV has been sitting in your Downloads folder for three weeks. This is where most survey programs die. The collection works. The analysis never happens. Manual tagging, spreadsheet pivots, slide assembly by hand. 10+ hours per batch. The PM moves on. The data decays. Next quarter someone asks "what did users say?" and nobody has the answer. AI compresses that 10+ hours into 30 minutes. Export the CSV. Upload to Claude. Run one prompt: "I have N responses from an exit survey. Give me distributions per question, themes from open-ended responses with exact quotes, user segments, and three product recommendations ranked by evidence." You get a 1-page action brief with cited user quotes and ranked actions. The workflow below has seven steps. Steps 1-4 take 20 minutes. Steps 5-6 take 10. Step 7 is a Claude Code script that automates the whole thing so you never do it manually again. The gap between teams that run surveys and teams that get value from surveys is the analysis layer. The framework below closes that gap. Screenshot it.

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Turn a raw survey export into a 1-page action brief with themes, quotes, and product recommendations in 30 minutes.

Prompt

I have N responses from an exit survey. Give me distributions per question, themes from open-ended responses with exact quotes, user segments, and three product recommendations ranked by evidence.

Why it works

The prompt bundles four distinct analysis tasks — quantitative distributions, qualitative theme extraction, segmentation, and prioritized recommendations — into a single request. This forces the model to synthesize across all response types at once rather than treating them in isolation, which surfaces connections between numeric patterns and verbatim user language. Asking for 'exact quotes' as part of the theme output anchors the results in evidence rather than paraphrase. This makes the brief auditable and usable in stakeholder presentations without a separate step to retrieve supporting quotes. Ranking recommendations 'by evidence' constrains the model to tie each suggestion back to frequency or sentiment signals in the data rather than generating generic product advice. This keeps the output grounded in what respondents actually said.

When to use

•After collecting exit surveys, onboarding surveys, or NPS follow-ups that have been sitting unanalyzed
•When a PM or researcher needs a shareable brief quickly but lacks bandwidth for manual coding and slide assembly
•As the first pass before deeper qualitative analysis — to identify which themes warrant further investigation

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