Is ChatGPT enough to analyze a focus group?
ChatGPT is useful to summarize a transcript, group themes and write a first draft. It is not enough for a report a board will question: it works only from the text you give it, cannot know who spoke if the transcript does not say, may return a paraphrased quote and does not hear the recording.
What does general AI do well with a transcript?
A tool like ChatGPT is good at working with a lot of text. Give it a focus group transcript and it can:
- Summarize what was discussed in each block of the guide.
- Group similar opinions into themes and suggest a name for each.
- Propose hypotheses or questions worth checking.
- Write a first draft of findings, or translate it.
To explore a session, or to organize your own notes, it is real help. The problem starts when that draft becomes the report without any checks.
Can it know who said each sentence?
Only if the transcript already says so. ChatGPT works from the text you give it: if the lines do not carry the name or profile of the speaker, it has no way of knowing, and if they carry the wrong one, it will repeat the mistake. That has to be settled earlier, from the recording.
And settling it is the hard part. In a room, every microphone picks up everyone. In a real focus group we measured that a microphone's wearer was the loudest voice on their own microphone only 11 of 120 seconds. In a report built by hand from a single room microphone, only 1 in 10 quoted people was attributed. A qualitative study compares segments, so a sentence attributed to the wrong person can move a finding to the wrong side.
Are its quotes literal?
Not always. A language model generates text: when you ask it for a quote, it may return the exact words or a version that sounds the same but is not. ChatGPT's own interface warns that it can make mistakes and suggests checking important information. In a report, a reworded quote is a serious problem, because readers assume someone said it that way.
The practical rule: every quote an AI proposes should be found word for word in the transcript, and the transcript should be checked against the recording.
Does it know the minute something was said?
Only if the transcript has timestamps and the quote is matched against them. Without that, nobody can go back to the exact moment to hear it, and a finding nobody can listen to is hard to defend when someone asks "who said that?".
Also, ChatGPT does not hear the recording behind a transcript you paste. If the transcript got a word wrong, the analysis inherits the error.
Quick comparison
| Task | ChatGPT with a transcript | What a checkable report needs |
|---|---|---|
| Summarize and group themes | Does it well | Useful as a starting point |
| Know who spoke | Only if the transcript says so | One microphone per person, and review of anything unclear |
| Literal quotes | May paraphrase | Every quote checked word for word against the transcript |
| Exact minute | Only if the transcript has timestamps | Every quote with its minute in the recording |
| Faithful to the audio | Does not hear the recording | A person listens to every quote before the report |
How do we do it at WAV?
We use AI, but behind checks that do not depend on it. Every participant wears their own microphone and the room is recorded in 360. WR3, our software, separates who is speaking with 90.4% agreement with human review, measured on 141 moments of a real session, and whatever it cannot decide it flags for a person to settle by listening.
When the AI proposes a quote, the system checks that its words appear complete and in order in the transcript of a single turn, spoken by one of the people the idea is attributed to. The text kept is the transcript's, never the AI's wording. Then a person listens to every quote in the recording and marks it verified or rejected. The detail is in how we measure.
And we do not hand you software to operate: we run the complete study, from design to report. You can see what a verified quote looks like, with its profile and minute, in the sample report, built from a fictional study.
So, is it enough?
To explore, yes. For a report that has to hold up a decision in front of a board, no: you still need to know who spoke, check every quote against the recording and be able to return to the exact minute. That part of the work is not something a language model replaces.