| name | interview-synthesis |
| description | Use when turning raw interview notes into themes - produces themes, verbatim quotes, and decisions or hypotheses |
| kind | workflow |
| audience | anyone |
| ai-tools | any |
| complexity | guided |
| time | 30-90 min per round of interviews |
| version | 1.0.0 |
| source | bundled |
Interview Synthesis
What this does
Turns raw interview notes (1-N interviews) into a structured synthesis: themes that recurred, verbatim quotes that anchor each theme, and the decisions or hypotheses the synthesis supports.
When to use
- After user research interviews
- After expert interviews for journalism / writing / product work
- After internal stakeholder interviews
- After any structured set of conversations where the next step is "what do we do with this"
What you bring (Inputs)
- Raw notes from N interviews (transcripts or your shorthand)
- The original research question (what were you trying to learn)
- Who the synthesis is for (you / a team / a client) — drives format
What you get (Output)
A document with: research question, methods, themes (3-7), verbatim supporting quotes per theme, surprises, and the decisions or hypotheses the data supports.
How it works (Steps)
This is a workflow.
Stages
Stage 1: Re-read with fresh eyes
Read all the notes through once without highlighting anything. Just take in the totality. This guards against latching onto the first idea.
Stage 2: Tag each note
Go through each interview and tag observations. Tags are short — 2-4 words. Examples: "Pricing confusion," "Wants integration," "Onboarding too slow."
Don't merge tags yet. Keep them granular.
Stage 3: Cluster tags into themes
Group similar tags. Aim for 3-7 themes. If you have 1-2, you're under-clustering. If you have 15+, you're over-tagging.
A theme is interesting if it appears in more than one interview AND is non-obvious.
Stage 4: Find the verbatim quote per theme
For each theme, pull the strongest 1-2 verbatim quotes. Use the speaker's actual words. These anchor the theme and prevent paraphrasing drift.
Stage 5: Note surprises
What did you NOT expect to hear? What contradicted your hypothesis going in? Surprises are often the most valuable output.
Stage 6: State the decisions or hypotheses
The synthesis should support concrete next steps. Examples:
- Decision: "We will rebuild the onboarding flow."
- Hypothesis: "Pricing confusion drives churn in months 2-3 — we should test a clearer pricing page."
If the synthesis doesn't support any decision or hypothesis, you under-interviewed or under-tagged.
Stage 7: Write the document
Standard structure:
- Question: what we were trying to learn
- Method: N interviews, who, when, format
- Themes (3-7) with verbatim quotes
- Surprises
- Decisions and hypotheses
- Next steps (more interviews? a test? a decision?)
Checkpoints
- After Stage 2: every interview has been tagged
- After Stage 3: 3-7 themes, each appearing in 2+ interviews
- After Stage 4: every theme has a verbatim quote
- After Stage 6: at least 1 decision or hypothesis is named
Loop-back conditions
- If a theme has no verbatim quote: cut it (it's your projection, not data)
- If the synthesis supports no decision: you may need more interviews — name the gap explicitly
Quality bar
- Verbatim quotes are quoted exactly, with speaker attribution if appropriate
- Themes are non-obvious (if the theme is "users want it to be easier," cut it — too generic)
- Surprises section is present even if short
- Decisions/hypotheses are concrete
Variations
- Single interview: skip clustering; instead, find the 3-5 most interesting points
- Quantitative + qualitative: pair counts (how many interviewees said X) with the quote
- Long-running research: each round of interviews updates the prior synthesis; mark what changed
Example
Input: 8 interviews with new SaaS users about onboarding.
Output:
- Question: Why are users dropping off in week 1?
- Themes:
- Setup felt longer than promised (6/8) — "I thought it'd take 5 minutes; I spent 40."
- Sample data made things worse, not better (5/8) — "I couldn't tell what was demo and what was mine."
- Email reminders felt naggy (4/8) — "Three emails in two days; I unsubscribed."
- Surprises: nobody mentioned pricing.
- Decisions: cut sample data from default; reduce onboarding emails to 1.
- Hypothesis: clearer setup-time estimate would reduce week-1 drop by 20%.
- Next: A/B test the setup-time estimate language.
