User Research & Validation Plan
Validation Objective
Validate that engineering teams experience significant pain from undetected API changes and would adopt a solution that provides early warnings, impact analysis, and team coordination features. Test pricing sensitivity and willingness to pay for different feature tiers.
Key Assumptions to Validate
| Assumption | Risk if Wrong | Validation Method | Target Evidence |
|---|---|---|---|
| Problem Assumptions | |||
| Engineering teams experience production incidents due to undetected API changes at least quarterly | Critical | Interviews, survey | 70%+ confirm incidents in last 12 months |
| Teams currently monitor API changes manually (checking docs, RSS, emails) and find it time-consuming | High | Interviews, observation | 80% describe current process as "painful" or "inefficient" |
| Security-related API changes (auth, permissions) are particularly concerning and require special attention | High | Interviews, survey | 60%+ rank security changes as top concern |
| Teams lack a unified view of all their API dependencies and their status | Medium | Interviews, prototype test | 70% express desire for centralized dashboard |
| Breaking changes are discovered during deployments or in production, not during development | High | Interviews | 50%+ describe late discovery as common |
| Solution Assumptions | |||
| Teams will adopt a SaaS tool that aggregates API changelogs and provides alerts | Critical | Landing page, pre-orders | 5%+ signup rate, 10+ pre-orders |
| AI-powered change classification will be accurate enough for production use | Critical | Prototype testing with experts | 90%+ accuracy on test set |
| GitHub integration (showing affected code) will significantly increase perceived value | High | Prototype testing, pricing tests | 30%+ willing to pay more for this feature |
| Teams will pay for API response diffing to detect undocumented changes | Medium | Pricing interviews, fake door | 20%+ click-through on fake door |
| Slack and PagerDuty integrations are essential for team adoption | High | Interviews, survey | 80%+ use Slack, 40%+ use PagerDuty |
| Business Assumptions | |||
| Teams will pay $49/month for the Team plan (50 APIs, Slack alerts) | Critical | Pricing interviews, pre-orders | 10+ pre-orders at $49/month |
| Enterprise teams will pay $199+/month for unlimited APIs and advanced features | High | Enterprise interviews, pilot programs | 3+ enterprise pilots |
| Customer acquisition cost (CAC) will be <$200 for Team plan customers | High | Ad tests, content marketing | Proven CAC in test campaigns |
| Free tier users will convert to paid at 5%+ rate | Medium | Product analytics | 5%+ conversion after 90 days |
| API providers will partner with us for official changelog access | Medium | Partnership outreach | 2+ official partnerships |
Customer Discovery Interview Guide
Interview Framework (60-90 minutes)
Part 1: Background & Context (10 min)
- Tell me about your role and what you do day-to-day in your engineering team
- How long have you been in this role? What's your team size?
- What types of applications does your team build/maintain?
- How many third-party APIs do you currently depend on?
- Which are the most critical to your operations?
Part 2: Problem Exploration (20 min)
- Walk me through the last time an API change caused issues for your team
- How did you discover the change? (email, docs, production incident, etc.)
- What was the impact? (downtime, bugs, security issues, etc.)
- How much time did it take to resolve?
- How often do these incidents happen? (monthly, quarterly, etc.)
- What's the worst API change incident you've experienced?
- How do you currently monitor for API changes? (RSS, email, checking docs, etc.)
- How much time does your team spend on this per week?
- What's the most frustrating part of this process?
- Have you ever missed an important API change? What happened?
- How do you handle security-related API changes (auth, permissions, etc.)?
- On a scale of 1-10, how painful is API change management for your team?
Part 3: Current Solutions (15 min)
- What tools or methods do you currently use to track API changes?
- What do you like about your current approach?
- What's missing or frustrating about it?
- Have you ever tried a dedicated tool for this? Why did/didn't it work?
- How do you coordinate API change awareness across your team?
- What happens when someone on your team misses an important change?
- How do you prioritize which API changes to address first?
- What would make you switch to a new solution for this?
Part 4: Solution Exploration (15 min)
Show concept: "Imagine a service that automatically monitors all your API dependencies, alerts you to breaking changes before they impact production, and shows you exactly what code needs to be updated."
- What would be most valuable about this for your team?
- What features would you expect in a basic version?
- What would make this a "must-have" rather than "nice-to-have"?
- How would you want to receive alerts? (Slack, email, PagerDuty, etc.)
- Would you want to see which parts of your codebase are affected?
- How important is detecting undocumented API changes?
- What concerns would you have about using this?
- How much would you expect to pay for something like this?
- Who else on your team would need to approve this purchase?
- Would you be interested in trying a beta version?
Part 5: Wrap-up (10 min)
- What's one thing you wish you could change about how your team handles API dependencies?
- If you could wave a magic wand and solve this problem, what would that look like?
- Who else on your team should I talk to about this?
- Would you be open to a follow-up conversation after we build a prototype?
- Can I contact you if we have specific questions during development?
Interview Logistics
- Target interviews: 30 minimum (10 solo devs, 10 startup teams, 10 enterprise teams)
- Recruitment channels: LinkedIn (engineering managers), Twitter (dev advocates), Reddit (r/programming, r/devops), Hacker News, API provider Slack communities
- Incentive: $75 Amazon gift card or 1 year free of the Team plan
- Recording: Ask permission, use Otter.ai for transcription
- Note-taking template: Problem quotes, solution reactions, pricing signals, feature requests
- Follow-up: Send thank you email with gift card within 24 hours
Survey Design
Screening Survey (5-10 questions)
Purpose: Build a pool of validated target users for deeper research
- What best describes your role?
Solo developer/indie hacker
Software engineer at a startup (1-50 engineers)
Software engineer at a mid-size company (50-500 engineers)
DevOps/Platform engineer
Engineering manager
Other: ________ - How many third-party APIs does your application depend on?
1-5
6-10
11-20
20+ - How do you currently monitor for changes to these APIs? (Select all that apply)
Check official changelog pages manually
Subscribe to email announcements
Follow API providers on Twitter
Use RSS feeds
Check GitHub releases
We don't systematically monitor changes
Other: ________ - How often do API changes cause issues for your team?
Never
Less than once per year
1-2 times per year
3-6 times per year
7+ times per year - What's the most severe impact you've experienced from an undetected API change?
Minor bug that was easy to fix
Significant bug requiring emergency fix
Production outage (1-60 minutes)
Extended outage (1+ hours)
Security vulnerability
Data loss or corruption - How much time does your team spend monitoring API changes per week?
Less than 1 hour
1-5 hours
6-10 hours
10+ hours - On a scale of 1-10, how painful is API change management for your team?
1 (Not painful) 10 (Extremely painful)
- Would you be interested in a 30-minute interview about your API change management process? ($75 gift card)
Yes, contact me at:
No
Validation Survey (15-20 questions)
Purpose: Quantify problem severity, test solution concepts, and gauge pricing sensitivity
Key questions to include:
- How often do you experience production issues due to undetected API changes? (Never to Weekly)
- How do you currently discover API changes? (Multiple choice with "Other" option)
- How satisfied are you with your current approach? (1-5 scale)
- What's the most frustrating part of managing API changes? (Open-ended)
- How valuable would each of these features be? (1-5 scale for each):
- Automated changelog monitoring
- Slack/email alerts for breaking changes
- GitHub integration showing affected code
- Impact analysis for your specific usage
- Undocumented change detection
- Team dashboard with API health scores
- Which pricing model would you prefer? (Monthly subscription, Annual discount, Pay per API, etc.)
- What's the maximum you'd pay per month for a solution like this? (Open-ended)
- Van Westendorp pricing questions:
- At what price would you consider this product to be so expensive that you wouldn't buy it?
- At what price would you consider this product to be priced so low that you'd question its quality?
- At what price would you consider this product to be a bargain?
- At what price would you consider this product to be getting expensive, but you'd still buy it?
- What would make you switch from your current approach? (Open-ended)
- What's one feature that would make this a "must-have" for your team? (Open-ended)
Landing Page Validation Experiment
Experiment Design
Goal: Validate demand for APIWatch before building the product by measuring interest through a landing page with email signup.
Landing Page Structure
Hero Section
- Headline: "Never be surprised by API changes again"
- Subheadline: "Monitor all your API dependencies in one place. Get alerts for breaking changes before they impact production."
- Email signup form: "Get early access"
- Hero image: Dashboard mockup showing API health scores
Feature Section
- Automated changelog monitoring (icon + short description)
- Smart alerts with severity levels
- GitHub integration showing affected code
- Team dashboard with API health scores
Pricing Section (Fake Door Test)
Free
5 APIs • Email alerts • 7-day history
Team Most Popular
$49/month • 50 APIs • Slack alerts • GitHub integration • 90-day history
Business
$199/month • Unlimited APIs • PagerDuty • SSO • Response diffing • Priority support
Headlines to A/B Test
- "API changes breaking your app? Get alerts before they hit production"
- "The missing piece of your API dependency management"
- "Monitor all your API dependencies in one dashboard"
- "Stop firefighting API changes. Start preventing them."
- "APIWatch: The early warning system for API changes"
Traffic Sources
- Google Ads: Target keywords like "API changelog monitoring", "track API changes", "API dependency management"
- LinkedIn Ads: Target engineering managers, DevOps engineers, platform engineers
- Twitter/X: Target developer advocates, tech leads, startup CTOs
- Reddit: r/programming, r/devops, r/webdev, r/startups
- Hacker News: "Show HN" when ready, target relevant threads
- Organic: Blog post: "The APIs that broke production this month" with signup CTA
Success Metrics
| Metric | Target | Measurement |
|---|---|---|
| Unique visitors | 1,000+ in 2 weeks | Google Analytics |
| Time on page | >1 minute | Google Analytics |
| Scroll depth | 70%+ see pricing section | Hotjar |
| Waitlist signup rate | >5% (50+ emails) | Mailchimp/ConvertKit |
| Email quality | <10% bounce rate | Email service |
| Fake door click rate (Team plan) | >10% | Google Analytics |
Budget & Timeline
- Budget: $1,000 total ($500 Google Ads, $300 LinkedIn, $200 Twitter/Reddit)
- Timeline: 2 weeks to build landing page, 2 weeks to run experiment
- Tools: Carrd or Webflow for landing page, Mailchimp for email collection, Google Analytics, Hotjar
Prototype Testing Plan
Option A: Wizard of Oz (Manual)
- Collect API dependencies via Google Form
- Manually monitor changelogs using RSS feeds and GitHub releases
- Use LLM to classify changes (breaking, deprecation, etc.)
- Send personalized alerts via email with impact analysis
- Follow up with users to test willingness to pay
Cost: $0 + 10-15 hours/week
Timeline: 2-4 weeks
Learning: High - direct user interaction
Option B: Concierge MVP
- High-touch service for 10-20 early users
- Founder manually sets up monitoring for each user
- Personalized onboarding and training
- Weekly check-ins to gather feedback
- Measure retention and willingness to pay
Cost: $0 + 20-30 hours/week
Timeline: 4-6 weeks
Learning: Very high - deep user insights
Option C: Clickable Prototype
- Figma/Framer prototype showing full workflow
- Add APIs → View dashboard → Receive alert → See impact analysis
- Test navigation and user flow
- Measure time to complete key tasks
- Gather qualitative feedback on UX
Cost: $200-$500 (tools + designer time)
Timeline: 1-2 weeks
Learning: Medium - UX validation only
Recommended Approach
Start with Option A (Wizard of Oz) to validate the core value proposition with minimal investment. This will:
- Test the actual change detection and alerting workflow
- Gather real user feedback on alert quality and impact analysis
- Validate willingness to pay with actual delivered value
- Build relationships with early adopters
After 2-3 weeks, add Option C (Clickable Prototype) to test the dashboard UX and team collaboration features. Consider Option B (Concierge MVP) for enterprise prospects who need more hand-holding.
Fake Door & Pre-Order Tests
Fake Door Test
Measure interest in specific features before building them.
Implementation
- Add "GitHub Integration" button to dashboard mockup
- Add "API Response Diffing" toggle in settings
- Add "Enterprise SSO" option in pricing
- Track clicks on these elements
- Show "Coming soon" message after click
- Collect email for notification
Success Metrics
| Feature | Target Click Rate |
|---|---|
| GitHub Integration | >15% |
| API Response Diffing | >10% |
| Enterprise SSO | >5% |
Follow-up
- Email users who clicked: "You asked for [feature] - we're building it!"
- Offer early access or beta testing
- Gather more detailed feedback on requirements
Pre-Order Test
Measure actual willingness to pay before building the product.
Implementation
- Add "Pre-order" button to pricing section
- Show limited-time early bird pricing ($39/month instead of $49)
- Collect payment via Stripe (refundable if not launched)
- Show estimated launch date (3 months from now)
- Send confirmation email with timeline
Success Metrics
| Plan | Target Pre-Orders | Conversion Rate |
|---|---|---|
| Team ($39/month) | 10+ | >2% of visitors |
| Business ($159/month) | 3+ | >0.5% of visitors |
Follow-up
- Send survey to pre-order customers: "What made you decide to pre-order?"
- Offer to schedule a call to discuss their needs
- Invite to beta testing program
- Send monthly updates on progress
8-Week Validation Timeline
| Week | Focus Area | Tasks | Success Metrics |
|---|---|---|---|
| Week 1 | Research Setup |
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| Week 2 | Problem Validation |
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| Week 3 | Solution Concept |
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| Week 4 | Demand Validation |
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| Week 5 | Pricing Validation |
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| Week 6 | Prototype Testing |
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| Week 7 | Enterprise Validation |
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| Week 8 | Synthesis & Decision |
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Go/No-Go Decision Criteria
After 8 weeks of validation, we will make a data-driven decision to proceed, pivot, or abandon the project based on these criteria:
| Metric | Target | Actual | Pass? |
|---|---|---|---|
| Problem Validation | |||
| % of users confirming significant API change pain | ≥80% | ||
| Average pain score (1-10 scale) | ≥7 | ||
| % experiencing production incidents from API changes | ≥60% | ||
| % describing current solution as "painful" or "inefficient" | ≥70% | ||
| Solution Validation | |||
| Landing page signup rate | ≥5% | ||
| Fake door click rate (GitHub integration) | ≥15% | ||
| Wizard of Oz NPS score | ≥40 | ||
| % of users requesting beta access | ≥30% | ||
| Willingness to Pay | |||
| Pre-orders at $49/month (Team plan) | ≥10 | ||
| Pre-orders at $199/month (Business plan) | ≥3 | ||
| Optimal price point (Van Westendorp) | $49-$99/month | ||
| % willing to pay for GitHub integration | ≥30% | ||
| Decision Guidelines | |||
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✓ GO
10+ criteria met ? PIVOT
5-9 criteria met ✗ NO-GO
0-4 criteria met |
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User Research Synthesis Template
After completing the validation process, document key findings using this template:
Problem Validation Summary
Top 3 Validated Pain Points
- Pain Point: Evidence: Quotes:
- Pain Point: Evidence: Quotes:
- Pain Point: Evidence: Quotes:
Unexpected Findings
Assumptions That Were Wrong
Solution Validation Summary
Most Compelling Features
- Feature: Why it resonated:
- Feature: Why it resonated:
- Feature: Why it resonated:
Features Users Don't Care About
UX Concerns Raised
- Concern: Evidence:
- Concern: Evidence:
Integration Needs Identified
- Integration: Why needed:
- Integration: Why needed:
Pricing Validation Summary
Optimal Price Point
Team Plan: per month
Business Plan: per month
Enterprise: per month or per year
Price Sensitivity by Segment
| Segment | Max Willing to Pay | Preferred Features |
|---|---|---|
| Solo developers | ||
| Startup teams (1-50 engineers) | ||
| Mid-size companies (50-500 engineers) | ||
| Enterprise (>500 engineers) |
Value Anchors
What users compare our pricing to:
Pricing Model Preferences
- Preferred:
- Rejected:
Go-to-Market Insights
Where Users Hang Out
- Online communities:
- Social media:
- Events:
How They Discover Solutions
- Search terms:
- Recommendations:
- Content:
Decision-Making Process
Buying Objections
- Objection: How to address:
- Objection: How to address: