Validation Experiments & Hypotheses
Hypothesis #1: Problem Existence 🔴 Critical
We believe that engineering teams at startups and mid-size companies
Will actively seek API changelog monitoring solutions
If they experience production incidents from undocumented API changes
We will know this is true when we see 60%+ of surveyed engineers confirm API changes as a top-3 pain point AND 5%+ landing page signup rate
Hypothesis #2: Solution Fit 🔴 Critical
We believe that DevOps engineers managing API dependencies
Will use an automated changelog tracker instead of manual monitoring
If we deliver unified alerts with impact analysis before production deployment
We will know this is true when we see 70%+ of prototype users rate the solution as "essential" or "very valuable"
Hypothesis #3: Willingness to Pay 🔴 Critical
We believe that engineering teams at 10-200 person companies
Will pay $49-$199/month for API change monitoring
If we prevent at least one production incident per quarter
We will know this is true when we see 15+ pre-orders at target price points
Hypothesis #4: Channel Effectiveness 🟡 High
We believe that DevOps engineers and platform teams
Will discover APIWatch through developer communities
If we share real "APIs that broke production" case studies
We will know this is true when we see CAC < $100 and 20%+ conversion from organic channels
Hypothesis #5: Feature Priority 🟢 Medium
We believe that engineering teams
Will prioritize breaking change alerts over new feature notifications
If we categorize changes by severity and impact
We will know this is true when we see 80%+ of users configure alerts for breaking changes only
Experiment Catalog
Experiment #1: Problem Discovery Interviews
Hypothesis: #1
Method: 25 semi-structured interviews with DevOps engineers
Success: 60%+ confirm API changes as top-3 pain point
Cost: $1,250 (incentives)
Experiment #2: Landing Page Smoke Test
Hypothesis: #1, #2
Method: Landing page with waitlist + targeted ads
Success: >5% signup rate from 1,000+ visitors
Cost: $750 (ads)
Experiment #3: Wizard of Oz MVP
Hypothesis: #2, #3
Method: Manual changelog monitoring + human alerts
Success: 7/10+ satisfaction, 50%+ would pay
Cost: 20 hours effort
Experiment #4: Pricing Survey
Hypothesis: #3
Method: Van Westendorp price sensitivity survey
Success: Clear optimal price point identified
Cost: $200 (survey tool)
Experiment #5: Pre-Order Test
Hypothesis: #3
Method: Collect payments for 3-month commitment
Success: 15+ pre-orders at target pricing
Cost: $0 (Stripe integration)
Experiment #6: Channel Testing
Hypothesis: #4
Method: Test CAC across Reddit, LinkedIn, Hacker News
Success: CAC < $100 on at least one channel
Cost: $500 (ads)
Experiment Prioritization Matrix
| Experiment | Hypothesis | Impact | Effort | Priority |
|---|---|---|---|---|
| Discovery Interviews | #1 | 🔴 Critical | Medium | 1 |
| Landing Page Test | #1, #2 | 🔴 Critical | Low | 2 |
| Wizard of Oz MVP | #2, #3 | 🔴 Critical | High | 3 |
| Pricing Survey | #3 | 🟡 High | Low | 4 |
| Pre-Order Test | #3 | 🟢 Medium | Medium | 5 |
8-Week Validation Sprint
- Launch landing page
- Recruit 25 interviewees
- Run $750 ad campaign
- Conduct interviews
- Analyze interview data
- Build manual MVP
- Deliver to 15 users
- Collect feedback
- Run pricing survey
- Launch pre-orders
- Test channel CAC
- Analyze willingness to pay
- Compile all results
- Score hypotheses
- Make Go/No-Go
- Plan Phase 2
Minimum Success Criteria (Go/No-Go)
✅ GO Decision
- 60%+ problem confirmation
- 5%+ landing page signup
- 7/10+ solution satisfaction
- 15+ pre-orders
- 3/5 critical hypotheses validated
⚠️ Conditional GO
- 40-60% problem confirmation
- 2-5% landing page signup
- 5-7/10 solution satisfaction
- 5-15 pre-orders
- Clear path to validation
❌ NO-GO Decision
- <40% problem confirmation
- <2% landing page signup
- <5/10 solution satisfaction
- <5 pre-orders
- No clear validation path
Pivot Triggers & Contingency Plans
Trigger #1: Problem Doesn't Exist
Signal: <40% confirm API changes as significant pain point
Action: Interview users about actual top DevOps problems, identify adjacent pain points in dependency management
Trigger #2: Solution Doesn't Resonate
Signal: <50% satisfaction with manual MVP
Action: Deep-dive on missing features, simplify scope to breaking change alerts only, add human expert review
Trigger #3: Won't Pay Enough
Signal: Acceptable price is <$25/month
Action: Pivot to enterprise segment, add security/compliance features, explore usage-based pricing
Trigger #4: Can't Acquire Efficiently
Signal: CAC >$200 across all channels
Action: Focus on product-led growth, build VS Code extension, partner with API providers for embedded distribution
Experiment Documentation Template
## Experiment: [Name]
**Date:** [Start - End]
**Hypothesis Tested:** #X
### Setup
- What we did
- Sample size
- Tools used
- Cost incurred
### Results
| Metric | Target | Actual | Pass/Fail |
|--------|--------|--------|-----------|
### Key Learnings
- Insight #1
- Insight #2
- Surprise finding
### Evidence
- [Link to data]
- [Quotes/screenshots]
### Next Steps
- [What this means for the product]
- [Follow-up experiments needed]