Analyzing Interview Data

Turn raw notes into insights, find patterns across interviews, and create actionable recommendations

From Notes to Insights

The interview is over. You have pages of notes. Now what? The magic happens in analysis. This is where you transform raw observations into insights that drive decisions. Most people skip this step or do it poorly. Don't be most people.

The Analysis Process

1. Review Immediately (Same Day)

While memories are fresh, fill in gaps in your notes. Add context you remember but didn't write down. The longer you wait, the more you forget.

2. Highlight Quotes (Within 1 Day)

Pull out the gold: direct quotes that illustrate pain points, desires, or workflows. These become evidence for your insights.

3. Tag Themes (After 3-5 Interviews)

Start looking for patterns. What keeps coming up? What problems appear in multiple interviews? Create tags like "tool-switching-pain" or "onboarding-confusion".

4. Create Insights (After All Interviews)

Synthesize patterns into clear insight statements: "Users with X background struggle with Y because Z." These drive product decisions.

Process Your Notes

Try analyzing sample interview notes to identify themes and create insights:

Your Interview Notes

Prioritize Your Insights

Not all insights are equal. Use this matrix to prioritize by impact and frequency:

Fix Immediately

High Impact Γ— High Frequency

Users struggle with multiple tools

Impact: 8/10β€’Frequency: 9/10

Fix Eventually

High Impact Γ— Low Frequency

Onboarding takes too long

Impact: 6/10β€’Frequency: 4/10

Needs mobile access

Impact: 7/10β€’Frequency: 3/10

Quick Wins

Low Impact Γ— High Frequency

Price is a concern

Impact: 5/10β€’Frequency: 6/10

Backlog

Low Impact Γ— Low Frequency

πŸ“Š Prioritization Strategy

  • Fix Immediately: High impact on users, happens frequently - top priority
  • Quick Wins: Easy to fix, happens often - good for momentum
  • Fix Eventually: Important but rare - schedule for later
  • Backlog: Low priority - revisit quarterly
🎯

Good Insights Are Actionable

Bad insight: "Users want better performance." Too vague. What does that mean? What should we do?

Good insight: "Users with slow internet (3+ participants) abandon the signup flow at step 3 because the form validation requires server round-trips. Local validation would let them proceed."

Key Takeaways

  • β€’Review notes same day while memories are fresh. Fill in gaps and add context you remember.
  • β€’Look for patterns after 3-5 interviews. What themes keep appearing? What quotes support them?
  • β€’Prioritize insights by impact and frequency. Fix high-impact, high-frequency problems first.
  • β€’Make insights actionable: Include who, what problem, why it happens, and what to do about it.