Lean Startup Methodology

When to Use This Skill

Use this skill when:

• Lean Startup tasks - Working on apply lean startup methodology for validated learning. guides build-measure-learn cycles, mvp definition, hypothesis testing, and pivot/persevere decisions
• Planning or design - Need guidance on Lean Startup approaches
• Best practices - Want to follow established patterns and standards

Overview

Lean Startup is a methodology for developing businesses and products that aims to shorten product development cycles and rapidly discover if a proposed business model is viable. It emphasizes validated learning through experimentation over elaborate planning.

Core Principles

1. Validated Learning

Learning through systematic experimentation rather than assumptions. Every product decision should be based on evidence, not opinions.

2. Build-Measure-Learn

The fundamental feedback loop:

       ┌─────────────────────────────────────────┐
       │                                         │
       ▼                                         │
   ┌───────┐     ┌─────────┐     ┌───────┐      │
   │ BUILD │────▶│ MEASURE │────▶│ LEARN │──────┘
   └───────┘     └─────────┘     └───────┘
       │
       └── Start with IDEAS, end with DATA

Build: Create the minimum needed to test a hypothesis Measure: Collect data on what users actually do Learn: Analyze data to validate or invalidate hypothesis

3. Minimum Viable Product (MVP)

The smallest thing you can build that allows you to learn something meaningful.

MVP Types:

• Concierge MVP: Manually deliver the service to understand needs
• Wizard of Oz MVP: Appear automated, but manual behind the scenes
• Landing Page MVP: Test demand before building anything
• Explainer Video MVP: Demonstrate concept to gauge interest
• Piecemeal MVP: Combine existing tools to simulate product
• Single Feature MVP: Build one core feature only

Hypothesis Framework

Leap of Faith Assumptions

Every startup rests on untested assumptions. Identify the most critical ones:

1. Value Hypothesis: Will users find the product valuable?
2. Growth Hypothesis: Will the product grow through word of mouth, virality, or paid channels?

Hypothesis Template

We believe that [specific user segment]
will [take specific action]
because [reason/motivation]

We will know this is true when we see [measurable outcome]

Example

We believe that enterprise developers
will pay $50/month for AI code review
because they spend 20% of time on manual reviews

We will know this is true when we see:
- 10% conversion from free trial
- 70% monthly retention rate
- NPS score > 40

MVP Definition Process

Step 1: Identify Riskiest Assumptions

List all assumptions your product relies on:

• Users have this problem
• Users will pay to solve it
• We can reach these users
• We can build this at acceptable cost
• This solution will work

Prioritize by: Risk × Impact

Step 2: Design Minimum Experiment

For each risky assumption, design the smallest experiment to test it:

Assumption	Experiment Type	Success Metric	Duration
Users have problem	Interviews (20)	80% confirm	2 weeks
Users will pay	Pre-sales page	5% conversion	1 week
Solution works	Concierge MVP	3 engaged users	3 weeks

Step 3: Build MVP

MVP Scope Checklist:

• Addresses exactly one core assumption
• Can be built in 1-4 weeks
• Has clear success/failure criteria
• Produces actionable learning
• Costs acceptable amount to validate

Step 4: Measure

Actionable Metrics (use these):

• Conversion rates at each funnel stage
• Cohort retention rates
• Customer acquisition cost (CAC)
• Lifetime value (LTV)
• Time to value

Vanity Metrics (avoid these):

• Total users (without activation)
• Page views (without conversions)
• Downloads (without usage)
• Registered accounts (without engagement)

Pivot or Persevere

Decision Framework

After each Build-Measure-Learn cycle:

┌──────────────────────────────────────────────────────────┐
│                  Analyze Experiment Results              │
└───────────────────────────┬──────────────────────────────┘
                            │
            ┌───────────────┴───────────────┐
            ▼                               ▼
    ┌───────────────┐               ┌───────────────┐
    │ Hypothesis    │               │ Hypothesis    │
    │ VALIDATED     │               │ INVALIDATED   │
    └───────┬───────┘               └───────┬───────┘
            │                               │
            ▼                               ▼
    ┌───────────────┐               ┌───────────────┐
    │  PERSEVERE    │               │    PIVOT      │
    │ Scale what    │               │ Change one    │
    │ works         │               │ fundamental   │
    └───────────────┘               │ aspect        │
                                    └───────────────┘

Pivot Types

Pivot Type	Description	Example
Zoom-in	Single feature becomes whole product	Flickr (from game to photo sharing)
Zoom-out	Whole product becomes single feature	Microsoft Office (suite from app)
Customer Segment	Same product, different users	Starbucks (B2B to B2C)
Customer Need	Same users, different problem	YouTube (dating to video sharing)
Platform	App to platform or vice versa	iOS App Store
Business Architecture	High margin/low volume ↔ low margin/high volume	Enterprise to consumer
Value Capture	Change monetization	Freemium to subscription
Engine of Growth	Viral ↔ paid ↔ sticky	Facebook (sticky to viral)
Channel	Change distribution	Direct to retail
Technology	Same solution, new technology	Film to digital cameras

Pivot Signals

Consider pivoting when:

• Metrics plateau despite iterations
• Customer interviews reveal different core need
• Retention remains low after multiple attempts
• CAC exceeds LTV by significant margin
• Team loses conviction in current direction

Innovation Accounting

Track progress with metrics that matter:

Three Learning Milestones

1. Establish Baseline: First MVP measures current reality
2. Tune the Engine: Iterate to improve metrics toward business model requirements
3. Pivot or Persevere: Decide based on rate of progress

Cohort Analysis

Track user behavior by acquisition cohort:

Cohort     | Week 1 | Week 2 | Week 3 | Week 4
-----------|--------|--------|--------|--------
Jan 2025   | 100%   | 45%    | 30%    | 22%
Feb 2025   | 100%   | 52%    | 38%    | 30%
Mar 2025   | 100%   | 60%    | 45%    | 38%

Improving retention across cohorts = validated learning.

AI-Assisted Lean Startup

Hypothesis Generation

When provided with a product concept, generate:

1. Core value hypothesis
2. Growth hypothesis
3. 5-10 leap of faith assumptions
4. Prioritized by risk × impact

MVP Design

For each risky assumption, suggest:

1. Experiment type (interview, landing page, concierge, etc.)
2. Sample size needed
3. Success/failure criteria
4. Timeline estimate

Experiment Analysis

Given experiment results, provide:

1. Statistical significance assessment
2. Hypothesis validation status
3. Recommended next steps
4. Pivot considerations if relevant

Integration Points

Inputs from:

• design-thinking skill: Validated problem → Value hypothesis
• jtbd-analysis skill: Jobs identified → Solution hypothesis
• assumption-testing skill: Prioritized assumptions

Outputs to:

• opportunity-mapping skill: Validated opportunities
• persona-development skill: Customer segment refinement
• impact-mapping skill: Validated goals and impacts

References

For additional Lean Startup resources, see: