The Founder’s 1‑Page Research Pack: Turn 10 Interviews into a Build‑or‑Kill Recommendation (and a One‑Page Spec)
Written by AppWispr editorial
Return to blogTHE FOUNDER’S 1‑PAGE RESEARCH PACK: TURN 10 INTERVIEWS INTO A BUILD‑OR‑KILL RECOMMENDATION (AND A ONE‑PAGE SPEC)
You ran ten discovery interviews. Now what? This post gives founders a tight, repeatable workflow to synthesize those conversations into one page that answers: build, iterate, or kill. Included: a copy‑and‑paste synthesis table, a 5‑factor scoring rubric you can run on each interview, and a one‑page spec template you can hand to a contractor. No fluff — just the exact artifacts you’ll use to make and communicate the decision.
Section 1
Why 10 interviews — and what 'synthesis' actually means
Ten interviews is a practical balance for early founders: enough diversity to see patterns, small enough to synthesize quickly. The goal of synthesis is not a long report — it’s to convert raw quotes into repeatable observations and a clear recommendation. Treat each interview as one data point in a simple, auditable grid that yields frequency plus evidence.
Good synthesis connects multiple data points into a cohesive theme and separates observations from solutions. That makes your recommendation traceable: stakeholders (co‑founders, contractors, early users) can see which interviews supported which claims and how the decision was reached.
- 10 interviews gives decent signal for narrow discovery questions (if your problem or audience is segmented, repeat for each segment).
- Synthesis = tag → cluster → prioritize → recommend (with evidence).
- Keep insights behavior-focused (what users did or failed to do), not wishful (what they said they'd like).
Section 2
Step‑by‑step workflow: from transcript to one page (30–90 minutes)
Run this workflow immediately after you finish the 10th interview. Use a shared Google Sheet or Notion page and work in two passes: (A) rapid extraction — 10× 5‑minute rows; (B) synthesis and scoring — 1 pass through the grid to create themes and a recommendation.
Pass A — rapid extraction: create one row per interview with 5 columns: persona, core problem described, behavior observed (not claimed), most persuasive quote, friction point. Keep each cell 10–25 words. This yields a compact dataset you can scan quickly.
Pass B — scoring & clustering: copy the 10 rows into the synthesis table below, add a 5‑factor score for each interview (problem severity, frequency, willingness to pay/time, workaround strength, and fit with your go‑to‑market). Sum the scores and use thresholds to nominate build / iterate / kill. Then cluster the 10 interview quotes into 3 top themes and extract 2–3 supporting quotes per theme.
- Two passes: extraction (fast) then synthesis (analytic).
- Five extraction columns: persona, problem, behavior, quote, friction.
- Five scoring factors: severity, frequency, willingness, workaround, GTM fit — total scores give a reproducible recommendation.
Section 3
Copy‑and‑paste Synthesis Table + Scoring Rubric
Below is the table you can paste into a sheet and use immediately. Each interview is a row. Keep entries short so you can scan and sort. After filling all 10 rows, sort by total score to see the highest‑priority data points.
Scoring rubric (0–3 per factor; 0 = no evidence, 3 = clear, repeated evidence): 1) Problem severity (how big of a pain?), 2) Frequency (how often it occurs?), 3) Willingness to pay or trade time (explicit or implied), 4) Workaround strength (is there an easy existing solution?), 5) GTM fit (is this aligned with how you reach users?). Use thresholds: 12+ = strong build signal; 8–11 = iterate/prototype; 0–7 = kill or deprioritize.
- Table columns: Interview ID | Persona | Problem (10–20 words) | Observed behavior | Quote | Friction | S1|S2|S3|S4|S5|Total | Theme
- Scoring: 0–3 per S1–S5. Document who scored and date for traceability.
- Decision thresholds: 12+ build, 8–11 prototype, 0–7 kill.
Section 4
One‑page spec you can hand to a contractor
After the recommendation, draft a one‑page spec that translates the top insight into a minimal deliverable. The spec’s job is clarity: context, user problem, success metric, constraints, must‑have behavior, and a 2‑week deliverable. Keep language implementation‑agnostic but specific about outcomes.
A typical one‑page spec sections: Project title, Recommendation (build/iterate/kill + why, with 2–3 evidence bullets), User story(s) (from interviews), Acceptance criteria (measurable), Minimal UI/flow sketch (3 steps), Non‑goals, Launch metric and owner. This is the exact doc a contractor needs to ship a first prototype or landing page quickly.
- Spec sections to include: Title, Recommendation + evidence, User stories, Acceptance criteria (1–2 KPIs), Flow (3 steps), Constraints/Non‑goals, Timeline & owner.
- Keep acceptance criteria measurable (e.g., 15% click-to-signup on a landing MVP, or 3 user test tasks completed within 2 minutes).
- Attach the synthesis table (or a link) as the evidence appendix so reviewers can trace the recommendation.
Sources used in this section
Section 5
How to use this pack in meetings and fundraising
Use the one‑page research pack as your canonical artifact in founder meetings: one page for the recommendation, one‑page spec for execution, and the synthesis table as evidence. For decision logs, save the scored sheet and spec with date and signer — it becomes an invaluable audit trail when you revisit decisions.
When talking to early investors or advisors, lead with the recommendation and one or two rapid evidence bullets; the full synthesis table backs up claims and shows disciplined research. Investors value founders who make traceable, data‑driven decisions that can be operationalized quickly.
- Share: Recommendation one‑pager + one‑page spec + synthesis table (appendix).
- Keep decision metadata: who scored, when, and what follow‑ups were assigned.
- Use the pack to convert research into a clear next sprint: owner, deliverable, metric, deadline.
FAQ
Common follow-up questions
Do I need to transcribe every interview before synthesizing?
No. For speed, you can do the rapid extraction pass from high‑quality notes or timestamped clips. Full transcripts help if you plan deeper analysis, but they’re not required to get to a one‑page recommendation.
What if interviews disagree — how many dissenting voices matter?
Look at the combination of frequency and evidence strength. A single loud complaint backed by behavior and a workaround might be important; but repeated, independent mentions carry more weight. Use the scoring rubric to make dissent explicit.
Can I use AI to help synthesize the interviews?
Yes — AI can speed extraction and draft themes, but always validate AI outputs against source quotes and timestamps. Treat AI as an assistant, not the final analyst.
Is this workflow suitable for B2B interviews?
Yes. For B2B, adjust persona and GTM fit factors to reflect buying centers, contract cycles, and integrations. You may need more than 10 interviews per stakeholder type.
Sources
Research used in this article
Each generated article keeps its own linked source list so the underlying reporting is visible and easy to verify.
PMToolkit
How to Synthesize User Interviews into Actionable Insights (Themes, JTBD, HMW) - AI Template
https://pmtoolkit.ai/prompts/interview-synthesis
Aha!
Analyzing User Interviews: Practical Tips for Clear Insights
https://www.aha.io/roadmapping/guide/how-to-analyze-user-interviews
Condens
How to Analyze User Interviews and Turn Raw Data into Insights
https://condens.io/user-interview-analysis/
Rishi Banerjee
The One Page Product Spec
https://banerjeerishi.com/text/the-one-page-product-spec.html
Smartsheet
ONE-PAGE PROJECT REQUEST TEMPLATE
https://www.smartsheet.com/sites/default/files/2022-04/IC-One-Page-Project-Request-11348_PDF.pdf?srsltid=AfmBOoqDbaqLE0l0TQIImeTpqgLBNTKElqcsOGue28g00rBh8eZMfsMm
Next step
Turn the idea into a build-ready plan.
AppWispr takes the research and packages it into a product brief, mockups, screenshots, and launch copy you can use right away.