Qualitative Research

Verbatim

Verbatim

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Conveo automates video interviews to speed up decision-making.

Definition:

Verbatim, in qualitative research, means capturing participant language exactly as spoken or written, without editing, paraphrasing, or interpretation. Verbatim quotes serve as the evidentiary foundation for thematic analysis, allowing researchers to trace every insight back to a real person who actually said it. In stakeholder reporting, verbatim excerpts carry a credibility that summarised findings cannot replicate, because they preserve the participant's own words, tone, and framing. Across depth interviews, focus groups, and ethnographic sessions, verbatim data is what separates grounded qualitative analysis from researcher inference, and it is what gives findings the authority to inform consequential business decisions.

How Conveo Does It

Every Conveo AI-moderated video interview is transcribed automatically the moment a session closes, giving researchers searchable verbatim records tied to timestamped video clips within days of launch. Studies can go live in under 30 minutes, and verbatim quotes surface directly inside stakeholder-ready reports, each one traceable to a real participant, not a synthetic respondent or AI-generated avatar. At enterprise scale, across hundreds of simultaneous conversations, verbatim data compounds inside the Conveo insight library, making every quote searchable and reusable across future studies.

Frequently asked questions.
Verbatim means recording and using a participant's exact words, without editing or paraphrasing. In qualitative research, verbatim quotes are the primary unit of evidence. They preserve the participant's own phrasing, which often carries meaning that a summary would flatten or lose. Researchers use verbatim data to ground thematic analysis, support findings in stakeholder reports, and demonstrate that conclusions trace back to what real people actually said.
Verbatim quotes give qualitative findings their credibility. When a stakeholder reads a summary claim, they are trusting the researcher's interpretation. When they read a verbatim quote, they are reading the participant's own words, which is a fundamentally different kind of evidence. Verbatim excerpts also reveal nuance that paraphrase strips away: hesitation, emphasis, specific vocabulary, and the way a participant frames a problem often carry as much meaning as the content itself.
Verbatim preserves the participant's exact language. Paraphrase restates the meaning in the researcher's own words. Both have a role in qualitative work, but they serve different functions. Verbatim is the evidence; paraphrase is the interpretation. Strong qualitative analysis keeps the two clearly separated, using verbatim quotes to anchor claims and paraphrase to synthesise patterns across participants. Conflating them, or substituting paraphrase where verbatim is expected, weakens the traceability and credibility of the findings.
AI transcription has made verbatim capture faster and more consistent, removing the manual effort that once made full transcription impractical at scale. More significantly, AI analysis can now surface verbatim quotes that illustrate a theme, rather than requiring a researcher to read every transcript to find them. The risk is that AI-generated summaries can blur the line between verbatim and paraphrase. Platforms built with research rigor keep verbatim quotes linked to their source, so every claim remains traceable to a real participant.
Enterprise teams use verbatim quotes at two stages: during analysis, to ground thematic coding in actual participant language, and during reporting, to give stakeholders direct access to the voice of the customer. A well-chosen verbatim quote in an executive presentation can shift a room in a way that a percentage or a summary cannot. Teams that maintain a searchable library of verbatim data across studies can also retrieve relevant quotes when a new question arises, without commissioning a new study from scratch.
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