APIWatch - API Changelog Tracker

User Research & Validation Plan

This plan outlines a structured approach to validate the core assumptions for APIWatch, a monitoring service for third-party API changes. By conducting targeted interviews, surveys, and experiments, we aim to confirm problem severity, solution fit, and business viability before investing in development. The focus is on engineering teams at startups and mid-size companies reliant on external APIs.

1. Key Assumptions to Validate

Below are the critical assumptions categorized by problem, solution, and business. Each includes a risk level (High/Critical/Medium/Low), validation method, and target evidence threshold for success.

Problem Assumptions

Assumption	Risk if Wrong	Validation Method	Target Evidence
Engineering teams experience production incidents due to undetected API changes at least 2-4 times per year.	High	Interviews, surveys	70% of interviewees confirm this frequency
Current manual monitoring (e.g., checking changelogs) is time-consuming, taking 5+ hours weekly per team.	High	Observation in interviews, time logs	60% report >5 hours/week spent
Scattered changelog sources lead to missed deprecations in 50%+ of cases.	Medium	Surveys, competitive analysis	50%+ cite missed changes as issue
Security-related API changes (e.g., auth updates) heighten compliance risks for teams.	High	Interviews with DevOps personas	80% express compliance concerns
Email alerts from API providers are ignored or lost in 70% of inboxes.	Medium	Surveys	70% confirm low visibility
Teams using 20+ external APIs struggle with dependency visibility.	Low	Interviews	Average 20+ APIs reported
Breaking changes cause deploy delays or outages costing 1-2 days of engineering time.	Critical	Quantitative surveys	Average cost >1 day per incident

Solution Assumptions

Assumption	Risk if Wrong	Validation Method	Target Evidence
Teams will adopt automated API change monitoring via a dashboard and alerts.	High	Prototype testing, landing page	40% express strong interest
Change detection accuracy (via scraping/LLM) will exceed 85% for popular APIs.	Critical	Manual testing with experts	85%+ accuracy in sample tests
GitHub integration for impact analysis will be valued by 60% of teams.	Medium	Interviews, prototype feedback	60% rate as essential
Slack/PagerDuty alerts will reduce response time to changes by 50%.	High	Wizard of Oz simulation	Users report 50% faster awareness
Auto-detection from package files will cover 70% of common dependencies.	Medium	Technical validation with sample repos	70% auto-detection rate
Severity categorization will align with user priorities in 80% of cases.	High	Expert review	80% agreement on severity
Dashboard health scores will influence upgrade planning for teams.	Low	Prototype usability tests	70% find it actionable

Business Assumptions

Assumption	Risk if Wrong	Validation Method	Target Evidence
Teams will pay $49/month for core features (50 APIs, integrations).	Critical	Pricing surveys, pre-orders	20% willingness at $49
Customer acquisition cost (CAC) via dev communities will be <$100.	High	Ad tests on Reddit/LinkedIn	CAC <$100 for 50 signups
Free tier conversion to paid will reach 10% within 3 months.	High	Landing page cohorts	10% upgrade rate
Churn will be <5% monthly due to sticky monitoring needs.	Medium	Beta user tracking	<5% churn in tests
Market size supports 1,000 paying teams in Year 1 ($500K ARR).	Medium	Survey reach, TAM analysis	Survey pool >500 qualified leads

2. Customer Discovery Interview Guide

60-90 minute semi-structured interviews with 20-30 engineering leads from startups/mid-size firms. Recruit via LinkedIn (target: "API integration" keywords), Reddit (r/devops, r/programming), and warm intros. Offer $50 Amazon gift card. Record with permission using Otter.ai; use template for quotes on pains, reactions, pricing.

Part 1: Background & Context (10 min)

• Tell me about your role and day-to-day responsibilities in managing external dependencies.
• How long have you been working with third-party APIs in production apps?
• What are your biggest challenges with API integrations right now?

Part 2: Problem Exploration (20 min)

• Walk me through the last time an API change caused an issue in your app.
• How often do you deal with breaking changes or deprecations (e.g., monthly, quarterly)?
• What triggers you to check for API updates?
• How does discovering a change too late make you feel—frustrated, stressed?
• What's the worst part about handling these changes?
• What have you tried to stay on top of them (e.g., RSS, emails)?
• How much time or money does your team spend on API maintenance weekly?

Part 3: Current Solutions (15 min)

• What tools or processes do you use to monitor API changes (e.g., Dependabot, manual checks)?
• What do you like about your current setup?
• What do you wish was different (e.g., better alerts, unified view)?
• Have you switched monitoring tools? Why or why not?
• What would make you switch to a new API monitoring service?

Part 4: Solution Exploration (15 min)

• If there was a service that automatically tracked changes across your APIs and alerted via Slack with impact analysis...
• What would be most valuable about that (e.g., time saved, outage prevention)?
• What concerns would you have (e.g., accuracy, privacy)?
• What features would it need for you to try it (e.g., GitHub integration)?
• How much would you expect to pay monthly for monitoring 50 APIs?
• Who else on your team would need to approve this purchase?

Part 5: Wrap-up (10 min)

• On a scale of 1-10, how painful is managing API changes for your team?
• Would you be interested in beta testing an API change tracker? (Collect contact)
• Who else should I talk to in your network?

3. Survey Design

Screening Survey (5-10 questions)

Distribute via Typeform/Google Forms to 200+ devs on LinkedIn/Reddit. Purpose: Qualify for interviews and quantify pain.

1. What best describes your role? [Engineering lead / DevOps / Technical founder / Other: ___]
2. How many third-party APIs does your app/team rely on? [1-10 / 11-50 / 50+]
3. Have you experienced a production issue from an API change in the last year? [Yes / No]
4. On a scale of 1-10, how painful is tracking API changes? [1-10 sliders]
5. How do you currently monitor API updates? [Manual checks / Tools like Dependabot / Emails / Other: ___]
6. How much time does your team spend weekly on API maintenance? [<1hr / 1-5hrs / >5hrs]
7. What's your team's size? [1-10 / 11-50 / 51-200 / 200+]
8. Would you join a 30-min interview on API management? ($50 gift card) [Yes, email: ___ / No]

Validation Survey (15-20 questions)

Follow-up to screening (target 100 responses). Quantify severity, test messaging, use Van Westendorp for pricing.

• Frequency: How often do API changes impact your work? [Weekly / Monthly / Quarterly / Rarely]
• Satisfaction: Rate your current monitoring tools (1-10).
• Messaging A/B: "Prevent API outages with automated tracking" vs. "Unified dashboard for all your APIs."
• Pricing: At what price is it too cheap/expensive? (Van Westendorp: $10/$20/$50/$100 options)
• Demographics: Company size, tech stack (e.g., Node.js, Python).
• Interest: Likelihood to use a service with Slack alerts and impact analysis (1-10).

4. Landing Page Validation Experiment

Build with Carrd or Webflow: Describe APIWatch value prop, features, tiers. Drive 1,000+ visitors via $500-1,000 Facebook/LinkedIn ads targeting "API developer" keywords. Track with Google Analytics.

Headlines to Test (A/B)

1. "Catch API Changes Before They Break Production"
2. "Automated Monitoring for Your Third-Party APIs"
3. "Unified Alerts for Stripe, Twilio, and 100+ APIs"

Metrics & Success Criteria

Metric	Target
Unique visitors	>1,000 in 2 weeks
Waitlist signup rate	>5% (50+ emails)
Pricing click-through	>10%
Bounce rate	<30%

5. Prototype Testing Plan

Test core workflows (add API, receive alert, view impact) with 10-20 qualified users from interviews.

• Option A: Wizard of Oz (Recommended Start) - Users submit API list via form; manually scrape/simulate changes using tools like Browserless + LLM; email alerts. Cost: $0 + 10-20 hours. Timeline: 2-4 weeks. Measures engagement without code.
• Option B: Concierge MVP - High-touch: Founder demos dashboard via Zoom, simulates analysis. Ideal for deep insights. Cost: $0 + time. Timeline: 4-6 weeks.
• Option C: Clickable Prototype - Figma mockup of dashboard/alert flow. Test navigation/usability. Cost: $200 (Figma Pro). Timeline: 1-2 weeks.

Recommendation: Begin with Wizard of Oz to validate end-to-end value, then iterate to clickable prototype. Collect NPS post-session (target >40).

6. Fake Door & Pre-Order Tests

Integrate into landing page for demand gauging.

• Fake Door: "Start Monitoring Now" button leads to "Coming Soon" form (email capture). Track clicks on tiers ($49 Team). Success: >10% click rate signals demand.
• Pre-Order: Offer 50% off first month ($24.50) via Stripe (refundable). Deadline: 30 days. Success: >2% of visitors pay (e.g., 20 from 1,000); <20% refunds.

7. 8-Week Validation Experiment Timeline

Phased approach to de-risk assumptions progressively.

Go/No-Go Decision Criteria

Metric	Target	Actual	Pass?
Interview problem validation	80%+ confirm pain	___	☐
Landing page signup rate	>5%	___	☐
Price acceptance	60%+ at $49	___	☐
Pre-orders	10+ customers	___	☐
Prototype NPS	>40	___	☐

Next Steps if Pass: Proceed to MVP build with validated features. If fail >2 metrics, pivot (e.g., focus on security-only monitoring).

8. User Research Synthesis Template

Post-validation document in Notion/Google Doc for team review.

Problem Validation Summary

• Top 3 validated pain points: [e.g., Missed deprecations, Alert overload, No impact visibility]
• Quotes: [e.g., "We lost a day debugging a Stripe webhook change."]
• Unexpected findings: [e.g., Microservices add internal API needs]
• Wrong assumptions: [e.g., Time spent lower than expected]

Solution Validation Summary

• Most compelling features: [e.g., Real-time alerts, Code impact]
• Unwanted features: [e.g., Basic RSS if scraping preferred]
• UX concerns: [e.g., Overly technical dashboard]
• Integration needs: [e.g., More than GitHub, like Jira]

Pricing Validation Summary

• Optimal price: [$49 for teams, per Van Westendorp]
• Sensitivity by segment: [Startups < enterprises]
• Value anchors: [Compared to Snyk at $100+]
• Model preferences: [Subscription over per-API]

Go-to-Market Insights

• Where users hang out: [Reddit, Hacker News, dev Slack groups]
• Discovery: [Blogs, webinars, tool integrations]
• Decision process: [Tech lead approval, no CTO for small teams]
• Objections: [Scraping reliability, data privacy]

Total estimated cost: $2,000-3,000 (ads, incentives). Expected outcome: 80% assumption validation, clear path to MVP.