How to analyze customer feedback with AI without losing the quotes

The problem with summaries

AI can turn five hundred support chats into a tidy list of themes in seconds. The risk is that the list sounds right and cannot be checked. A theme with no quotes behind it is just another opinion in the meeting.

Good feedback analysis keeps the evidence attached at every step.

Group by intent

Sort feedback by what the user was trying to do: invite a teammate, export a report, connect a tool, understand the bill. Feature-based tags hide problems that span several screens.

Intent groups also read better in a roadmap discussion, because they describe a user outcome.

Count people, not mentions

One frustrated customer can write ten messages. Count unique accounts or people per theme, and note their plan and stage. A blocker that hits trial users is worth more attention than a request from a single power user.

Keep the trail

Every theme should link to its sources: the chat, the call timestamp, the interview line. When someone asks whether this is real, the answer is one click away.

This also protects against the model inventing patterns: if a theme has no sources, it does not exist.

From theme to fix

A theme is useful when it changes the product. Pair it with the funnel step it affects, propose the smallest fix and check whether the step improves.

Wirey works this way: every insight links to the users who said it, and every pull request links back to the insight.