continuous-interview-synthesis

Continuous Interview Synthesis

You are synthesizing user interview notes into opportunities using Teresa Torres' continuous discovery methodology. The goal is to extract struggles and unmet needs — not feature requests — and connect them to OST opportunities.

Key principle from Torres: "The most common interview mistake is asking users what they want. Instead, ask about their past behavior. Reconstruct the experience." — Teresa Torres

Step 1 — Load Context

Read memory/user-profile.md and context/product/personas.md. Understand: what desired outcome is this discovery work in service of? What OST opportunities already exist?

Step 2 — Assess the Interview Data

Identify what's been provided:

• Number of distinct interviews or conversations
• Format (full transcript, bullet notes, voice-to-text, sparse jottings)
• Whether each interview has a clear interviewee profile (role, segment, context)

If the interviewee context is missing, note it — segment-level patterns matter more than individual quotes.

Step 3 — Clean the Data

For each interview, extract:

• Context: Who is this person? What's their situation? What were they doing before this conversation?
• Moments of struggle: Where did they describe something being hard, slow, confusing, or impossible?
• Workarounds in use: What are they doing today to handle the struggle?
• Desired outcomes: What did they say they wished they could do? What would success look like?
• Emotional signals: Where did they express frustration, relief, surprise, or delight?
• Feature requests (to be reframed): Any "I wish it had X" statements — note but immediately ask: what problem does X solve?

Step 4 — Switch Interview Pattern (Bob Moesta)

For any interview that touched on when or why the user adopted your product (or a competitor), apply the switch interview pattern:

• First thought: What happened right before they started looking for a new solution?
• Passive looking: What made them more actively aware of alternatives?
• Active looking: What trigger caused them to search seriously?
• Decision: What was the deciding moment — what tipped them?
• Consuming: What was the onboarding experience like?

This surfaces JTBD insights from the interview data.

Step 5 — Cluster Into Opportunities

Group interview moments across all transcripts by underlying struggle. A cluster becomes an opportunity when:

• At least 2 interviewees describe a similar struggle
• The struggle is about something users need to do, not about wanting a feature

Name each opportunity as: "Users struggle to [action] when [context]"

For each opportunity, capture:

• Number of interviews it appeared in
• Strongest representative quote
• Severity signal (blocking / frustration / mild annoyance)
• Segment affiliation (primary persona / secondary / unknown)

Step 6 — OST Mapping

Map the opportunities into the existing OST structure (if one exists in memory or context). If no OST exists, create one.

Check: do any of the interview opportunities suggest sub-opportunities under existing OST nodes? Do any suggest the current OST is missing a major opportunity branch?

Step 7 — Interview Quality Assessment

Evaluate the quality of the interview data:

• Attitudinal vs. behavioral: Are quotes based on what users say they do, or what they actually did? Behavioral evidence is stronger.
• Leading questions: Did any interview seem to lead the user toward a particular answer?
• Sample diversity: Are all interviewees from the same segment, or is there diversity?
• Recency: Are these interviews from the last 3 months, or older?

Flag any quality concerns and recommend what additional interviews would strengthen weak signals.

Step 8 — Output

Produce a structured synthesis:

• Executive summary (3–5 bullets: what we learned and what it means)
• Full opportunity list with evidence count, quotes, and severity
• OST mapping showing where each opportunity fits
• Interview quality assessment
• Recommended next interviews (who to talk to and what to ask)
• Top assumption to test before building

Offer to save findings to memory/user-profile.md and update the persona file with new insights.