Use Cases/AI User Research Analysis

AI User Research Analysis

Analyze 100 interviews in the time it takes to read one.

Outcome

Research synthesis time reduced by 83% (days → minutes)

Outcome

Cross-study patterns automatically surfaced

Outcome

Entire org can query insights on-demand

The Problem

Analysis is the bottleneck.

User research is supposed to inform product decisions. In practice, it often doesn't because the bottleneck isn't conducting research, it's analyzing it.

A single 60-minute interview generates roughly 8,000 words of transcript. Manually transcribing takes 4-5 hours. Coding that transcript, which involves reading through, highlighting key quotes, tagging themes, organizing insights, takes another 2-3 hours. Multiply that by the 15-20 interviews in a typical study, and a researcher can spend an entire month just processing data before any synthesis begins.

The result is predictable: research gets rushed, insights stay superficial, and studies sit in folders that nobody ever references again. Researchers become bottlenecks, fielding ad-hoc requests for "what did users say about X?" while trying to keep up with new projects. Patterns across studies, the meta-insights that inform product strategy, never get surfaced because nobody has time to look.

Worse, research stays siloed. The designer who conducted usability tests last quarter has insights that would help the PM planning next quarter's roadmap, but there's no efficient way to connect them. Each study becomes a one-time deliverable instead of a building block in organizational knowledge.

The Solution

How Magic Insights Solves It

01

Transcribe with near-human accuracy

Upload audio or video recordings and get 95%+ accurate transcripts in minutes, not hours. Our transcription engine handles multiple speakers, technical jargon, and accents in 22+ languages. Speaker labels are automatically detected, and timestamps let you jump to any moment in the recording.

02

Surface insights automatically

Once transcribed, our AI analyzes every interview to extract key themes, notable quotes, emotional moments, pain points, and feature requests. You don't need to read every transcript; the AI highlights what matters so you can dive deep where it counts.

03

Run thematic analysis at scale

Magic Insights conducts thematic analysis across all your interviews simultaneously. Instead of manually coding transcripts one by one, see patterns emerge across your entire dataset: "12 of 18 participants mentioned frustration with onboarding" or "Enterprise users consistently request bulk export features."

04

Build a research repository

Every study, interview, and insight feeds into a searchable knowledge base. Six months from now, when someone asks "what do we know about how customers think about pricing?" you can query your entire research history and get cited answers in seconds.

05

Share insights that stick

Generate shareable highlight reels, quote collections, and insight reports that stakeholders actually read. Link directly to video clips so decision-makers can hear customer voices firsthand.

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