Lean Startup Methodology

A systematic approach to building startups and launching new products that shortens development cycles and rapidly discovers whether a business model is viable.

Core Principle

Entrepreneurship is a form of management. Success doesn't require a perfect plan or brilliant insight—it requires a systematic process for testing assumptions, learning from customers, and iterating rapidly. Most startups fail not because they couldn't build what they planned, but because they built the wrong thing: treat every plan as a set of hypotheses to falsify, and spend effort to eliminate waste and accelerate validated learning, not to execute a fixed roadmap.

Scoring

Goal: 10/10. Score a plan, experiment, or metric set by the five Quick Diagnostic rows—1 point each when the answer is yes, 2 points when it is also backed by evidence on the Validation Ladder (Level 3+):

• 9-10: every leap-of-faith assumption named and ranked by risk, the riskiest tested by a real MVP, actionable metrics defined, and explicit pivot criteria set before building.
• 5-6: a hypothesis and some MVP exist, but metrics are vanity or pivot criteria are undefined—decisions can't be made from the data.
• ≤3: waterfall thinking—building the full product first, asking customers what they want, or scaling before product/market fit.

State the current score and the lowest-scoring diagnostic row to fix next.

The Build-Measure-Learn Loop

The fundamental cycle: IDEAS → BUILD (product) → MEASURE (data) → LEARN (knowledge) → back to IDEAS.

Critical insight: Plan the loop backward:

1. What do we want to learn? (hypothesis to test)
2. How will we know if we learned it? (metrics)
3. What's the minimum we can build? (MVP)

Goal: Minimize total time through the loop.

See references/build-measure-learn.md when planning an experiment—reverse-planning sequence, an experiment-design template, per-product-type loop examples, and the build/vanity-metric loop traps.

Validated Learning

Learning what customers really want through experiments on real behavior—not feature requests, surveys, or focus groups (people mispredict their own behavior). Measure what customers do, not what they say, and run experiments that could falsify your assumptions. Vanity wins (downloads, signups without engagement) are not learning.

The Validation Ladder: