validation-frameworks

name	validation-frameworks
description	Problem and solution validation methodologies, assumption testing, and MVP experiments. Use when validating a problem worth solving, testing solution assumptions, designing MVP experiments, or deciding whether to pivot or persevere. Trigger on: 'validate my idea', 'test my assumptions', 'design an MVP experiment', 'should I pivot', 'is this problem worth solving'.

Validation Frameworks

Frameworks for validating problems, solutions, and product assumptions before committing to full development.

When to Use This Skill

Auto-loaded by agents:

• research-ops - For problem/solution validation and assumption testing

Use when you need:

• Validating product ideas before building
• Testing assumptions and hypotheses
• De-risking product decisions
• Running MVP experiments
• Validating problem-solution fit
• Testing willingness to pay
• Evaluating technical feasibility

Core Principle: Reduce Risk Through Learning

Building the wrong thing is expensive. Validation reduces risk by answering critical questions before major investment:

• Problem validation: Is this a real problem worth solving?
• Solution validation: Does our solution actually solve the problem?
• Market validation: Will people pay for this?
• Usability validation: Can people use it?
• Technical validation: Can we build it?

The Validation Spectrum

Validation isn't binary (validated vs. not validated). It's a spectrum of confidence:

Wild Guess → Hypothesis → Validated Hypothesis → High Confidence → Proven

Early stage: Cheap, fast tests (low confidence gain) Later stage: More expensive tests (high confidence gain)

Example progression:

1. Assumption: "Busy parents struggle to plan healthy meals"
2. Interview 5 parents → Some validation (small sample)
3. Survey 100 parents → More validation (larger sample)
4. Prototype test with 20 parents → Strong validation (behavior observed)
5. Launch MVP, track engagement → Very strong validation (real usage)
6. Measure retention after 3 months → Proven (sustained behavior)

Problem Validation vs. Solution Validation

Problem Validation

Question: "Is this a problem worth solving?"

Goal: Confirm that:

• The problem exists
• It's painful enough that people want it solved
• It's common enough to matter
• Current solutions are inadequate

Methods:

• Customer interviews (discovery-focused)
• Ethnographic observation
• Surveys about pain points
• Data analysis (support tickets, analytics)
• Jobs-to-be-Done interviews

Red flags that stop here:

• No one cares about this problem
• Existing solutions work fine
• Problem only affects tiny niche
• Pain point is mild annoyance, not real pain

Output: Problem statement + evidence it's worth solving

See assets/problem-validation-canvas.md for a ready-to-use framework.

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Solution Validation

Question: "Does our solution solve the problem?"

Goal: Confirm that:

• Our solution addresses the problem
• Users understand it
• Users would use it
• It's better than alternatives
• Value proposition resonates

Methods:

• Prototype testing
• Landing page tests
• Concierge MVP (manual solution)
• Wizard of Oz (fake backend)
• Pre-sales or waitlist signups

Red flags:

• Users don't understand the solution
• They prefer their current workaround
• Would use it but not pay for it
• Solves wrong part of the problem

Output: Validated solution concept + evidence of demand

See assets/solution-validation-checklist.md for validation steps.

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The Assumption-Validation Cycle

1. Identify Assumptions

Every product idea rests on assumptions. Make them explicit.

Types of assumptions:

Desirability: Will people want this?

• "Users want to track their habits"
• "They'll pay $10/month for this"
• "They'll invite their friends"

Feasibility: Can we build this?

• "We can process data in under 1 second"
• "We can integrate with their existing tools"
• "Our team can build this in 3 months"

Viability: Should we build this?

• "Customer acquisition cost will be under $50"
• "Retention will be above 40% after 30 days"
• "We can reach 10k users in 12 months"

Best practice: Write assumptions as testable hypotheses

• Not: "Users need this feature"
• Yes: "At least 60% of users will use this feature weekly"

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2. Prioritize Assumptions to Test

Not all assumptions are equally important. Prioritize by:

Risk: How uncertain are we? (Higher risk = higher priority) Impact: What happens if we're wrong? (Higher impact = higher priority)

Prioritization matrix:

Risk/Impact	High Impact	Low Impact
High Risk	Test first	Test second
Low Risk	Test second	Maybe skip

Riskiest assumptions (test these first):

• Leap-of-faith assumptions the entire business depends on
• Things you've never done before
• Assumptions about user behavior with no data
• Technical feasibility of core functionality

Lower-risk assumptions (test later or assume):

• Based on prior experience
• Easy to change if wrong
• Industry best practices
• Minor features

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3. Design Validation Experiments

For each assumption, design the cheapest test that could prove it wrong.

Experiment design principles:

1. Falsifiable: Can produce evidence that assumption is wrong 2. Specific: Clear success/failure criteria defined upfront 3. Minimal: Smallest effort needed to learn 4. Fast: Results in days/weeks, not months 5. Ethical: Don't mislead or harm users

The Experiment Canvas: See assets/validation-experiment-template.md

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4. Run the Experiment

Before starting:

• Define success criteria ("At least 40% will click")
• Set sample size ("Test with 50 users")
• Decide timeframe ("Run for 1 week")
• Identify what success/failure would mean for product

During:

• Track metrics rigorously
• Document unexpected learnings
• Don't change experiment mid-flight

After:

• Analyze results honestly (avoid confirmation bias)
• Document what you learned
• Decide: Pivot, persevere, or iterate

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Validation Methods by Fidelity

Low-Fidelity (Hours to Days)

1. Customer Interviews

• Cost: Very low (just time)
• Validates: Problem existence, pain level, current solutions
• Limitations: What people say ≠ what they do
• Best for: Early problem validation

2. Surveys

• Cost: Low
• Validates: Problem prevalence, feature preferences
• Limitations: Response bias, can't probe deeply
• Best for: Quantifying what you learned qualitatively

3. Landing Page Test

• Cost: Low (1-2 days to build)
• Validates: Interest in solution, value proposition clarity
• Measure: Email signups, clicks to "Get Started"
• Best for: Demand validation before building

4. Paper Prototypes

• Cost: Very low (sketch on paper/whiteboard)
• Validates: Concept understanding, workflow feasibility
• Limitations: Low realism
• Best for: Very early solution concepts

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Medium-Fidelity (Days to Weeks)

5. Clickable Prototypes

• Cost: Medium (design tool, 2-5 days)
• Validates: User flow, interaction patterns, comprehension
• Tools: Figma, Sketch, Adobe XD
• Best for: Usability validation pre-development

6. Concierge MVP

• Cost: Medium (your time delivering manually)
• Validates: Value of outcome, willingness to use/pay
• Example: Manually curate recommendations before building algorithm
• Best for: Validating value before automation

7. Wizard of Oz MVP

• Cost: Medium (build facade, manual backend)
• Validates: User behavior, feature usage, workflows
• Example: "AI" feature that's actually humans behind the scenes
• Best for: Testing expensive-to-build features

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High-Fidelity (Weeks to Months)

8. Functional Prototype

• Cost: High (weeks of development)
• Validates: Technical feasibility, actual usage patterns
• Limitations: Expensive if you're wrong
• Best for: After other validation, final pre-launch check

9. Private Beta

• Cost: High (full build + support)
• Validates: Real-world usage, retention, bugs
• Best for: Pre-launch final validation with early adopters

10. Public MVP

• Cost: Very high (full product)
• Validates: Product-market fit, business model viability
• Best for: After all other validation passed

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Setting Success Criteria

Before running experiment, define what success looks like.

Framework: Set three thresholds

1. Strong success: Clear green light, proceed confidently
2. Moderate success: Promising but needs iteration
3. Failure: Pivot or abandon

Example: Landing page test

• Strong success: > 30% email signup rate
• Moderate success: 15-30% signup rate
• Failure: < 15% signup rate

Example: Prototype test

• Strong success: 8/10 users complete task, would use weekly
• Moderate success: 5-7/10 complete, would use monthly
• Failure: < 5/10 complete or no usage intent

Important: Decide criteria before seeing results to avoid bias.

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Teresa Torres Continuous Discovery Validation

Opportunity Solution Trees

Map opportunities (user needs) to solutions to validate:

Outcome
└── Opportunity 1
    ├── Solution A
    ├── Solution B
    └── Solution C
└── Opportunity 2
    └── Solution D

Validate each level:

1. Outcome: Is this the right goal?
2. Opportunities: Are these real user needs?
3. Solutions: Will this solution address the opportunity?

Assumption Testing

For each solution, map assumptions:

Desirability assumptions: Will users want this? Usability assumptions: Can users use this? Feasibility assumptions: Can we build this? Viability assumptions: Should we build this?

Then test riskiest assumptions first with smallest possible experiments.

Weekly Touchpoints

Continuous discovery = continuous validation:

• Weekly customer interviews (problem + solution validation)
• Weekly prototype tests with 2-3 users
• Weekly assumption tests (small experiments)

Goal: Continuous evidence flow, not one-time validation.

See references/lean-startup-validation.md and references/assumption-testing-methods.md for detailed methodologies.

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Common Validation Anti-Patterns

1. Fake Validation

What it looks like:

• Asking friends and family (they'll lie to be nice)
• Leading questions ("Wouldn't you love...?")
• Testing with employees
• Cherry-picking positive feedback

Fix: Talk to real users, ask open-ended questions, seek disconfirming evidence.

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2. Analysis Paralysis

What it looks like:

• Endless research without decisions
• Testing everything before building anything
• Demanding statistical significance with 3 data points

Fix: Accept uncertainty, set decision deadlines, bias toward action.

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3. Confirmation Bias

What it looks like:

• Only hearing what confirms existing beliefs
• Dismissing negative feedback as "they don't get it"
• Stopping research when you hear what you wanted

Fix: Actively seek disconfirming evidence, set falsifiable criteria upfront.

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4. Vanity Validation

What it looks like:

• "I got 500 email signups!" (but 0 conversions)
• "People loved the demo!" (but won't use it)
• "We got great feedback!" (but all feature requests, no usage)

Fix: Focus on behavior over opinions, retention over acquisition.

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5. Building Instead of Validating

What it looks like:

• "Let's build it and see if anyone uses it"
• "It'll only take 2 weeks" (takes 2 months)
• Full build before any user contact

Fix: Always do cheapest possible test first, build only after validation.

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Validation Checklist by Stage

Idea Stage

• Problem validated through customer interviews
• Current solutions identified and evaluated
• Target user segment defined and accessible
• Pain level assessed (nice-to-have vs. must-have)

Concept Stage

• Solution concept tested with users
• Value proposition resonates
• Demand signal measured (signups, interest)
• Key assumptions identified and prioritized

Pre-Build Stage

• Prototype tested with target users
• Core workflow validated
• Pricing validated (willingness to pay)
• Technical feasibility confirmed

MVP Stage

• Beta users recruited
• Usage patterns observed
• Retention measured
• Unit economics validated

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Ready-to-Use Resources

In assets/:

• problem-validation-canvas.md: Framework for validating problems before solutions
• solution-validation-checklist.md: Step-by-step checklist for solution validation
• validation-experiment-template.md: Design experiments to test assumptions

In references/:

• lean-startup-validation.md: Build-Measure-Learn cycle, MVP types, pivot decisions
• assumption-testing-methods.md: Comprehensive assumption testing techniques

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Troubleshooting

"I validated the problem but the solution still failed": Problem validation and solution validation are separate steps. Confirming a problem exists doesn't mean your specific solution is right. Run a concierge or Wizard of Oz test before building anything.

"My MVP got no signups": Either the problem isn't urgent enough, your audience can't find you, or your messaging doesn't communicate the value. Check distribution before blaming the product. A landing page with 0 signups is a distribution problem; one with signups but no activation is a product problem.

"Stakeholders want to skip validation and just build it": Frame validation as de-risking, not delaying. "We can spend 2 weeks testing this assumption, or 3 months building something nobody wants." Quantify the cost of being wrong.

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Related Skills

• product-market-fit - Measuring and achieving product-market fit after validation
• interview-frameworks - Interview techniques for problem discovery
• user-research-techniques - Broader research methods for gathering validation data
• market-sizing-frameworks - Validating market size before committing