Key takeaways
- AI is good at grouping large volumes of text into themes.
- Remove personal data before analysis where possible.
- Spot-check themes against the original feedback.
A simple workflow
- Export feedback (survey answers, reviews, tickets) to a spreadsheet.
- Remove names, emails and other identifiers.
- Ask an AI assistant to group responses into themes with counts and example quotes.
- Spot-check: read a sample of original responses for each theme.
- Map themes to funnel stages - which are objections, which are onboarding issues?
- Choose one improvement per theme and track the result.
Below are [number] anonymised customer comments. Group them into themes. For each theme give: - a short name - how many comments fit it - 2-3 representative quotes (verbatim) - whether it relates to: attracting, converting, onboarding, or retaining customers Do not invent comments or quotes. If a comment fits no theme, list it under 'Other'. [paste comments]
FAQ
How much feedback do I need?
Even a few dozen responses can reveal useful themes. Larger volumes make AI grouping more valuable because manual reading becomes impractical.