6.4 KiB
Prototyping & Pretotyping Experiment Template
Workflow
Prototyping Progress:
- [ ] Step 1: Identify riskiest assumption to test
- [ ] Step 2: Choose pretotype/prototype approach
- [ ] Step 3: Design and build minimum test
- [ ] Step 4: Run experiment and collect data
- [ ] Step 5: Analyze results and decide
Experiment Design Template
1. Assumption to Test
Assumption: [What are we assuming? E.g., "Users will pay $49/mo for AI-powered analytics"] Why risky: [Why might this be wrong? Impact if wrong?] Risk score: [Probability wrong (1-5) × Impact if wrong (1-5) = Risk (1-25)]
2. Test Method
Approach: [Pretotype / Paper / Clickable / Coded / MVP] Fidelity choice rationale: [Why this fidelity level? What question does it answer?] Estimated cost: [$X or X hours] Timeline: [X days to build, Y days to test]
3. Success Criteria
Primary metric: [E.g., "10% landing page → sign-up conversion"] Secondary metrics: [E.g., "50% complete onboarding, 5 min avg session"] Minimum sample: [n=X users/observations] Decision rule:
- Persevere (build it): [Metric ≥ X means validated]
- Pivot (change direction): [Metric < Y means assumption wrong]
- Iterate (refine and re-test): [X > Metric ≥ Y means unclear, need more data]
4. Experiment Build
What we're building: [Landing page, paper prototype, working feature, etc.] Components needed:
- [Component 1, e.g., Landing page copy/design]
- [Component 2, e.g., Sign-up form]
- [Component 3, e.g., Analytics tracking]
Fake vs Real:
- Faking: [What appears real but isn't? E.g., "Buy Now button shows 'Coming Soon'"]
- Real: [What must actually work? E.g., "Email capture must work"]
5. Participant Recruitment
Target users: [Who are we testing with? Demographics, behaviors, context] Sample size: [n=X, reasoning: qualitative vs quantitative] Recruitment method: [Ads, existing users, outreach, intercepts] Screening: [How do we ensure target users? Screener questions]
6. Data Collection Plan
Quantitative data:
| Metric | How measured | Tool | Target |
|---|---|---|---|
| [Sign-ups] | [Form submissions] | [Google Analytics] | [≥100] |
| [Conversion] | [Sign-ups / Visitors] | [GA] | [≥10%] |
Qualitative data:
| Method | N | Questions/Tasks |
|---|---|---|
| [User interview] | [5-10] | [What problem were you trying to solve? Did prototype help?] |
| [Task observation] | [10] | [Complete checkout, note errors/confusion] |
7. Results
Quantitative:
| Metric | Target | Actual | Status |
|---|---|---|---|
| [Sign-ups] | [≥100] | [X] | [✓ / ✗] |
| [Conversion] | [≥10%] | [Y%] | [✓ / ✗] |
Qualitative:
- Observation 1: [E.g., "7/10 users confused by pricing page"]
- Observation 2: [E.g., "All users expected 'Export' feature"]
- Quote 1: [User said...]
- Quote 2: [User said...]
8. Decision
Decision: [Persevere / Pivot / Iterate] Rationale: [Why? Which criteria met/not met?] Next steps:
- [If Persevere: Build MVP with features X, Y, Z]
- [If Pivot: Test alternative approach A]
- [If Iterate: Refine prototype addressing issues 1, 2, 3, re-test in 2 weeks]
Learnings:
- [What we learned about assumption]
- [What surprised us]
- [What to test next]
Quick Patterns
Pretotype Methods
Fake Door Test (Test demand):
- Build: Landing page "New Feature X - Coming Soon" with "Notify Me" button
- Measure: Click rate, email sign-ups
- Example: "500 visitors, 50 sign-ups (10%) → validates demand"
Concierge MVP (Test workflow manually before automating):
- Build: Manual service delivery (no automation)
- Measure: Customer satisfaction, willingness to pay, time spent
- Example: "Manually curate recommendations for 10 users → learn what good looks like before building algorithm"
Wizard of Oz (Appear automated, human-powered):
- Build: UI looks automated, humans behind scenes
- Measure: User perception, task success, performance expectations
- Example: "Chatbot UI, humans answering questions → test if users accept chatbot interaction before building NLP"
Single-Feature MVP (Test one feature well):
- Build: One core feature, ignore rest
- Measure: Usage, retention, WTP
- Example: "Instagram v1: photo filters only → test if core value enough before building stories/reels"
Prototype Methods
Paper Prototype (Test workflow):
- Build: Hand-drawn screens on paper/cards
- Test: Users "click" on paper, swap screens, observe
- Measure: Task completion, errors, confusion points
- Example: "10 users complete checkout, 3 confused by shipping step → redesign before coding"
Clickable Prototype (Test UI/UX):
- Build: Interactive mockup in Figma/InVision (no real code)
- Test: Users complete tasks, measure success/time
- Measure: Completion rate, time, errors, satisfaction
- Example: "20 users, 85% complete task <3 min → validates flow"
Coded Prototype (Test feasibility):
- Build: Working code, limited features/data
- Test: Real users, real tasks, measure performance
- Measure: Latency, error rate, scalability, cost
- Example: "Search 10K docs <500ms → validates approach, ready to scale to 10M docs"
Measurement Approaches
Quantitative (n=100+):
- Conversion rates (landing page → sign-up, sign-up → payment)
- Task completion rates (% who finish checkout)
- Time on task (how long to complete)
- Error rates (clicks on wrong element, form errors)
Qualitative (n=5-10):
- Think-aloud protocol (users narrate thought process)
- Retrospective interview (after task, ask about confusion/delight)
- Observation notes (where they pause, retry, look confused)
- Open-ended feedback (what worked, what didn't)
Behavioral > Opinions:
- ✓ "50 clicked 'Buy', 5 completed payment" (behavior)
- ❌ "Users said they'd pay $99" (opinion, unreliable)
Quality Checklist
- Assumption is risky (high probability wrong × high impact if wrong)
- Fidelity matches question (not overbuilt)
- Success criteria set before testing (no moving goalposts)
- Recruited real target users (not friends/family)
- Sample size appropriate (n=5-10 qualitative, n=100+ quantitative)
- Measuring behavior (clicks, conversions), not just opinions
- Clear decision rule (persevere/pivot/iterate thresholds)
- Results documented and shared with team