Validation Experiments & Hypotheses
Hypothesis #1: Problem Existence 🔴 Critical
We believe that security teams and CISOs at mid-market companies (500-5,000 employees) managing vendor risk with limited resources will actively seek automated vendor risk assessment tools if they are trying to validate the effectiveness of their current vendor risk management processes. We will know this is true when we see 60%+ of surveyed security teams confirm this is a top-3 pain point AND 5%+ landing page signup rate.
Risk Level: 🔴 Critical (product fails if wrong)
Current Evidence: Supporting - Forum discussions, search volume, competitor traction; Contradicting: None identified; Gaps: No direct user interviews yet
Experiment Design: Customer discovery interviews + landing page test; Sample Size: 20 interviews, 1,000 landing page visitors; Duration: 2 weeks; Cost: $500 (ads) + 20 hours (interviews)
Success Metrics: | Metric | Fail | Minimum | Success | Home Run | |--------|------|---------|---------|----------| | Problem confirmation rate | <40% | 40-60% | 60-80% | >80% | | Landing page signup | <2% | 2-5% | 5-10% | >10% |
Hypothesis #2: Solution Fit 🔴 Critical
We believe that founders seeking automated vendor risk assessment will use an AI-powered analysis tool instead of manual research if we deliver comprehensive, actionable reports in minutes instead of weeks. We will know this is true when we see 70%+ of prototype users rate the output as "useful" or "very useful".
Risk Level: 🔴 Critical
Current Evidence: Supporting - Industry reports suggest automation demand; Contradicting: None identified; Gaps: No direct user feedback yet
Experiment Design: Wizard of Oz MVP; Sample Size: 10 users; Duration: 4 weeks; Cost: Time only (10-20 hours of effort)
Success Metrics: | Metric | Fail | Minimum | Success | Home Run | |--------|------|---------|---------|----------| | User satisfaction | <6/10 | 6-8/10 | 8-9/10 | >9/10 | | NPS score | <30 | 30-50 | 50-70 | >70 |
Hypothesis #3: Willingness to Pay 🔴 Critical
We believe that bootstrapped founders will pay $49-$99 for a single viability analysis if we provide investor-grade output that saves 20+ hours of research. We will know this is true when we see 10+ pre-orders at target price point.
Risk Level: 🔴 Critical
Current Evidence: Supporting - Comparable products pricing; Contradicting: None identified; Gaps: No direct sales data yet
Experiment Design: Pre-order test; Sample Size: 100 visitors; Duration: 2 weeks; Cost: $500 (ads)
Success Metrics: | Metric | Fail | Minimum | Success | Home Run | |--------|------|---------|---------|----------| | Pre-order conversion | <5% | 5-10% | 10-20% | >20% | | Average order value | <$49 | $49-$99 | $99-$149 | >$149 |
Experiment Catalog
| Experiment | Hypothesis | Method | Sample Size | Duration | Cost |
|---|---|---|---|---|---|
| Discovery Interviews | #1 | Semi-structured interviews | 20 | 2 weeks | $1,000-$1,500 |
| Landing Page Test | #1, #2 | Landing page with waitlist signup | 1,000 | 2 weeks | $500 |
| Wizard of Oz MVP | #2, #3 | Manual delivery of service | 10 | 4 weeks | Time only |
8-Week Validation Sprint
Minimum Success Criteria (Go/No-Go)
To proceed, we must meet the following criteria:
- 60%+ of interviewed security teams confirm vendor risk management as a top-3 pain point
- 5%+ landing page signup rate
- 70%+ of prototype users rate the output as "useful" or "very useful"
- 10+ pre-orders at target price point
Pivot Triggers & Contingency Plans
If we fail to meet our success criteria, we will reassess our approach based on the data collected and pivot if necessary.
- Pivot Trigger #1: Problem Doesn't Exist - If <40% of users confirm problem, we will interview users about their actual top problems and identify adjacent pain points.
- Pivot Trigger #2: Solution Doesn't Resonate - If <50% satisfaction with prototype, we will deep-dive on what's missing and adjust our solution accordingly.
- Pivot Trigger #3: Won't Pay Enough - If acceptable price is <50% of target, we will find higher-value use cases or adjust our pricing model.
Experiment Documentation Template
For each completed experiment, we will document the following:
- Experiment name and hypothesis tested
- Setup and methodology
- Results and metrics
- Key learnings and insights
- Evidence and data
- Next steps and recommendations