I conduct user research for a product design studio. In a typical month, I run 12 to 18 moderated user interviews — each one recorded as a video file. Before I found a workflow that actually worked, I spent more time watching recordings than conducting them. That ratio has flipped.
The Volume Problem in User Research
User research is only as useful as your ability to synthesize it. You can run 50 interviews, but if you can't get to the insights quickly, the findings land too late to influence decisions — or they don't land at all.
The bottleneck for me was always the recordings. A 60-minute usability session produces an hour of video that contains maybe 10 minutes of genuinely insight-dense material. Finding those 10 minutes meant watching or fast-forwarding through everything else.
Across 15 interviews a month, that's 15 hours of footage — for a researcher who also needs to do synthesis, write reports, and present findings. The math doesn't work unless you have a better system.
Converting Video Files to Transcripts
My recording setup is simple: Zoom for remote sessions, which exports as MP4; occasionally a screen recorder with audio for unmoderated sessions.
I upload the video files to sipsip.ai's video transcriber. I don't need to extract audio first — it handles video files directly. The workflow is upload, wait a few minutes, and come back to a full transcript.
What I get:
- Full transcript — everything the participant said, in sequence
- AI summary — the 3–5 things that happened in the session, distilled
- Key points — the standout moments flagged by the model
"Fifteen interviews a month used to mean 15 hours of footage. Now it means 15 transcripts I can actually search."
— Lucas Park
How Transcripts Change Research Synthesis
The critical shift is moving from time-based to text-based analysis.
Video is indexed by time. To find a quote, you need to remember roughly when it happened and scrub to that point. Across 15 sessions, this is impractical. You're working from imperfect memory or incomplete notes.
Text is indexed by content. I can search all 15 transcripts simultaneously for a word, a phrase, a theme. When I'm doing affinity mapping, I'm not trying to remember which session something came from — I'm searching for it.
This changes how fast I can move from data collection to synthesis. I can finish the last interview on a Friday and have a preliminary themes document by Monday morning. Before, that would have taken most of the week.
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Using the AI Summary for Quick Triangulation
I use the AI summary for two things:
1. Rapid triangulation during data collection. After each session, I read the summary before the next. It takes 60 seconds and tells me what that session added to the picture I'm building. If three summaries in a row mention the same friction point, I know I have signal.
2. Screener for what to read closely. Not every transcript deserves equal attention. The summary tells me which sessions were data-rich and which were more routine. I read the rich ones in full; for the others, I focus on the key points and search for specific themes when needed.
File Types I Work With
Different research methods produce different file formats:
- MP4 (Zoom, Google Meet) — my main format for remote moderated sessions
- MOV (QuickTime, screen recordings on Mac) — unmoderated sessions and think-aloud recordings
- MP3 — audio-only recordings from phone interviews and guerrilla research sessions
sipsip.ai handles all of these. I upload whichever format comes out of the recording setup without converting.
Quote Extraction for Reports
Stakeholder reports need direct participant quotes. Previously, finding the right quote meant rewinding to a timestamp I'd written down, hoping I'd written it down accurately.
Now: I search the transcript for the topic I'm writing about, identify the candidate quotes visually, and copy them directly. The transcript is the quote library.
For presentation slides, the key points output often contains a line I can pull directly — the model is reasonably good at identifying the most quotable moments from an interview.
What Doesn't Work Perfectly
Background noise degrades accuracy. Remote sessions where a participant is in a coffee shop, or has a noisy household, produce lower-quality transcripts. My practice is to flag these and do a light manual cleanup before I use them for synthesis.
Technical vocabulary specific to a product or domain sometimes gets transcribed oddly. Industry terms, product names, and acronyms are the most common errors. A find-and-replace pass after the transcript arrives takes care of the recurring ones.
Multi-speaker attribution isn't always clean, especially in sessions where both the moderator and participant talk over each other. For moderated interviews, I generally know which voice is which and can read through any ambiguous sections.
Frequently Asked Questions
Does it work for moderated sessions where there are two speakers?
Yes. The transcript captures both voices. Speaker attribution from audio alone isn't always perfect, but for research synthesis purposes — identifying what a participant said and in what context — the transcript is accurate enough to use directly.
How long does it take to transcribe a 60-minute user interview video?
A 60-minute MP4 file typically takes 6–10 minutes to process. Fast enough to upload at the end of a session and come back to the transcript after a short break.
Can I use the transcripts in research reports?
Yes. The transcripts are yours to use in deliverables, reports, and presentations. For studies with IRB or privacy requirements, treat the transcripts with the same handling protocols as the recordings themselves.
Is there a length limit for video files?
sipsip.ai handles standard research session lengths (30–90 minutes) without issue. For very long recordings, check the pricing page for current file size and duration limits.
Does it work in languages other than English?
Yes — 50+ languages are supported. I've used it for sessions in Korean and the accuracy was comparable to English. Mixed-language sessions (where participants code-switch) work reasonably well depending on the language pair.
As a UX researcher, I record every user session on video. sipsip.ai converts them to searchable transcripts with summaries — so I spend my time on insights, not rewatching footage.