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Synthesise support tickets, interviews and app reviews into prioritised product themes with ChatGPT or Claude

A product-management guide to cleaning feedback from tickets, interviews, surveys and reviews, then using AI prompts to find cross-source themes and rank them by impact, frequency and feasibility.

Evidence: The author reports this. We have not checked it beyond reading the source.

The business problem

Founders collect customer feedback in several places and rarely turn it into prioritised product decisions.

What was tried

Step one (about two to three hours) builds a feedback inventory, such as 90 days of tickets, interview transcripts, survey exports and 200 to 500 app reviews, then removes personal data, standardises dates, tags sources and splits files into chunks of 50 to 100 items. Step two (one to two hours) runs source-specific prompts and then a cross-source prompt that looks for themes in two or more sources, contradictions, segment differences, trends and root causes, ending with an impact, frequency and feasibility priority matrix. Later parts, including a hallucination check and stakeholder validation, are paywalled.

What was reported (not reported)

No results from a real project are shown. The guide supplies a hypothetical sample dataset, and quotes statistics about analysis time and AI-driven speed-ups without sources.

Limitations

Most of the guide is paywalled, so only the first steps were readable. The statistics it cites have no source, there is no measured accuracy for the prompts, and privacy is covered only by removing personal data. Prompts assume manual pasting of batches, so very large volumes need more tooling.

What you need

Exports of your own feedback data and a ChatGPT, Claude or Gemini account. Newsletter and tool prices are not stated.

Sources

Source published: September 21, 2025. Last reviewed here: October 11, 2026. Spot a mistake? Tell us.

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