Interview Insight Synthesizer

Pro v1.0.0 1 view

Synthesize 10-50 interview transcripts into comprehensive research reports with themes, patterns, and prioritized recommendations.

What You Get

Reduce interview analysis time from 4-8 weeks to under 1 hour while extracting themes, patterns, and actionable insights across large transcript datasets.

The Problem

Product managers and UX researchers face a bottleneck when analyzing interview transcripts. With 10-50 interviews generating hundreds of pages, manual analysis takes 4-8 weeks. Critical insights get buried, patterns are missed, and decisions are delayed while transcripts pile up.

The Solution

Uses TF-IDF vectorization and K-means clustering to automatically identify 8-15 major themes across transcripts. Tracks theme frequency, identifies contradictions and rare insights, performs segment analysis when metadata is available, and generates prioritized recommendations. The Python script handles deterministic clustering while Claude interprets results, labels themes meaningfully, and produces narrative reports.

How It Works

  1. 1 Collect interview transcript files (TXT format, 10-50 files) and optional research question or participant metadata
  2. 2 Run clustering script to vectorize transcripts and identify theme clusters via TF-IDF and K-means
  3. 3 Interpret clustering results: label themes descriptively, calculate frequencies, assess severity
  4. 4 Select 2-5 representative quotes per theme with proper attribution
  5. 5 Identify cross-interview patterns: universal pain points, segment-specific themes, contradictions, rare insights
  6. 6 Perform segment analysis if participant metadata was provided
  7. 7 Generate prioritized recommendations grouped by timeframe (Immediate, Short-term, Long-term, Deprioritize)
  8. 8 Format comprehensive markdown report with executive summary, themes, quotes, and methodology notes

What You'll Need

  • Interview transcripts in TXT format (minimum 10, maximum 50)
  • Python 3.8+ with scikit-learn and numpy installed
  • Optional: Research question or hypothesis for focused analysis
  • Optional: Participant metadata (demographics, user type, segment) for cross-segment analysis

Get This Skill

Requires Pro subscription ($9/month)

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Examples

Email Management Pain Point Analysis

Baseline example analyzing 12 customer interviews about email workflows to identify the primary pain point. Demonstrates theme extraction, frequency tracking, representative quotes, and prioritized recommendations for a focused research question.

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Multi-Region Product Feedback with Role Segmentation

Advanced analysis of 50 interviews across three geographic regions (EMEA, APAC, Americas) and three departments (Engineering, Product, Sales). Demonstrates cross-dimensional segment analysis, regional priority differences, role-specific pain points, and strategic recommendations with quarterly timelines.

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Remote Collaboration Tools - Multi-Segment Analysis

Complex analysis of 35 interviews across three business segments (SMB, Enterprise, Startup) examining adoption blockers and coordination challenges. Demonstrates segment-specific pattern analysis, contradiction identification, and prioritized recommendations with resource estimates.

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