lean-startup

Files (12)

• SKILL.md
• applications.md · references/
• assumptions.md · references/
• build-measure-learn.md · references/
• case-studies.md · references/
• five-whys.md · references/
• growth-engines.md · references/
• innovation-accounting.md · references/
• metrics.md · references/
• mvp-design.md · references/
• pivots.md · references/
• small-batches.md · references/

SKILL.md (first lines)

# 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](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:**

| Level | Evidence | Strength |
|-------|----------|----------|
| 1 | "I think customers want this" | Weakest (opinion) |
| 2 | "Customers said they want this" | Weak (stated preference) |
| 3 | "Customers signed up for early access" | Medium (low commitment) |
| 4 | "Customers paid a deposit" | Strong (real commitment) |
| 5 | "Customers are actively using it" | Strongest (revealed preference) |

**Target:** Level 4-5 before building at scale.

## Minimum Viable Product (MVP)

The version of a new product that allows maximum validated learning with the least effort. Not a prototype (technical feasibility), not a beta (quality), not a minimum marketable product—a learning vehicle, often embarrassingly small and low quality, and usually much smaller than you think.

**MVP Types:**

| Type | What It Is | When to Use | Example |
|------|------------|-------------|---------|
| **Concierge** | Manual service pretending to be automated | Test if solution is valuable | Food on the Table (manual meal planning) |
| **Wizard of Oz** | Fake automation, manual backend | Test if automation is needed | Zappos (no inventory, bought shoes retail) |
| **Smoke test** | Landing page + signup, no product | Test demand before building | Dropbox video (explained concept, measured signups) |
| **Single feature** | One core feature only | Test which feature is most valuable | Twitter (just status updates) |
| **Piecemeal** | Combine existing tools | Test workflow before custom build | Groupon (WordPress + email) |

**Design questions:** What's the riskiest assumption? What's the minimum that tests it? How do we measure whether it was validated?

See [references/mvp-design.md](references/mvp-design.md) when choosing and sizing an MVP—seven types in depth, a type-selection decision matrix, lower/upper sizing bounds, and the MVP Design Canvas.

## Leap-of-Faith Assumptions

The assumptions that, if wrong, will cause your business to fail. Identify them, prioritize by risk (which failure would be fatal?), and test the riskiest first—never in order of ease.

| Assumption Type | Question | Test Method |
|----------------|----------|-------------|
| **Value hypothesis** | Do customers care about this problem? | Smoke test, concierge MVP |
| **Growth hypothesis** | How will customers discover us? | Channel tests, referral experiments |
| **Retention hypothesis** | Will customers come back? | Cohort analysis, engagement metrics |
| **Monetization hypothesis** | Will customers pay? | Pre-orders, pricing tests |

**Example—Dropbox:** Leap of faith: "people will download and use a file sync tool." Test: explainer video before building scale infrastructure. Result: beta list grew from 5,000 to 75,000 overnight—demand validated.

See [references/assumptions.md](references/assumptions.md) when mapping and ranking assumptions—the Impact-Uncertainty matrix, a prioritization scoring template, test methods per assumption type, and industry-specific assumption lists.

## Innovation Accounting

Measuring progress when traditional metrics fail: revenue and customers start at zero, and vanity metrics look good without driving decisions.

### 1. Establish the Baseline

Measure current reality precisely, even if it's zero or embarrassing: conversion funnel (signup → active → retained → paying), engagement (DAU/MAU, session length, features used), economics (CAC, LTV, churn).

### 2. Tune the Engine

Run experiments to improve baseline metrics: A/B test pricing ($9 vs. $19/mo), onboarding completion rates, acquisition channels (SEO vs. paid vs. referral). Each experiment targets a measurable improvement through validated learning.

### 3. Pivot or Persevere

When tuning stalls, make the evidence-based call (criteria and pivot types below in **Pivot or Persevere**).

See [references/innovation-accounting.md](references/innovation-accounting.md) when building the baseline dashboard—funnel, cohort, and economics metric frameworks.

## Actionable vs. Vanity Metrics

Vanity metrics make you feel good but don't change behavior; actionable metrics drive decisions and clarify cause and effect.

| Vanity | Why It's Bad | Actionable Alternative |
|--------|-------------|------------------------|
| **Total signups** | Always goes up, no context | **% signup → active** (conversion rate) |
| **Page views** | Doesn't indicate value | **Time on page**, **bounce rate** |
| **Total users** | Includes inactive/churned | **Active users** (DAU, WAU, MAU) |
| **Downloads** | Doesn't mean usage | **DAU/downloads** (activation rate) |
| **Revenue** | Without context | **Revenue per cohort**, **LTV/CAC** |

**Three characteristics of actionable metrics:** actionable (clear cause-and-effect, reproducible), accessible (simple, understood by everyone), auditable (underlying data can be checked).

**Example:** Vanity: "We have 100,000 users!" Actionable: "Channel X users retain 2x better than channel Y—double down on X."

**Cohort analysis:** Group users by signup date and track behavior over time—the only way to see whether the product is actually improving.

See [references/metrics.md](references/metrics.md) when building a cohort table or choosing what to track—a five-step cohort walkthrough and AARRR (Pirate Metrics) aligned with Lean Startup stages.

## Pivot or Persevere

From wondelai/skills, MIT.

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