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.
