# Build-Measure-Learn Loop

## Purpose
Systematically test business hypotheses through rapid iteration, turning ideas into products, measuring customer response, and learning whether to pivot or persevere.

## When to Use
- Running experiments to validate product ideas
- Deciding what to build next
- Measuring progress when traditional metrics don't apply
- Avoiding "building in the dark" based on assumptions
- Accelerating time to product-market fit

## Process

### 1. Understand the Loop

The Build-Measure-Learn loop is the fundamental feedback cycle:

```
       IDEAS
         ↓
      [BUILD]  → Minimize time through the loop
         ↓
      PRODUCT (MVP)
         ↓
     [MEASURE]  → Focus on actionable metrics
         ↓
       DATA
         ↓
      [LEARN]  → Validated learning is the goal
         ↓
       IDEAS ←─┘
```

**Critical insight:** Work BACKWARDS through the loop when planning:
1. What do we need to LEARN?
2. What do we need to MEASURE to learn that?
3. What do we need to BUILD to measure that?

### 2. Start with Learning Goals

Before building anything, define:

**What hypothesis are we testing?**
- Be specific: "Customers will pay $50/month" not "People will like this"
- Make it falsifiable: There should be a clear way to be wrong
- Make it meaningful: If wrong, it changes your strategy

**What would validate this hypothesis?**
- Specific numbers: "30% conversion rate"
- Specific behaviors: "Use feature 3+ times per week"
- Specific commitments: "5 pre-orders in 30 days"

**What would invalidate this hypothesis?**
- Define failure criteria upfront
- "If conversion is below 10%, we abandon this direction"
- Prevents moving the goalposts later

### 3. Design the Measurement

**Choose metrics that are:**

**Actionable** - Shows cause and effect
- Bad: "Traffic increased" (why? what do we do about it?)
- Good: "Signup rate increased 20% after headline change"

**Accessible** - Everyone can understand
- Use real units (customers, revenue) not abstractions
- If a new employee can't understand your dashboard, simplify it

**Auditable** - Can be verified
- Can you manually check the numbers?
- Can you talk to the actual customers represented?

### 4. Build the Minimum Required

**Only build what's needed to test the hypothesis:**

- Does the hypothesis require a full product? (Usually no)
- Does it require that feature? (Often no)
- Does it require that level of polish? (Rarely)

**Types of build:**
- Conversation: Sometimes you just need to ask questions
- Landing page: Test demand without building
- Wizard of Oz: Manual fulfillment behind automated facade
- Single feature: Build one thing well
- Full MVP: Minimum complete experience

### 5. Run the Experiment

**Set time boundaries:**
- "We'll run this for 2 weeks, then evaluate"
- Prevents experiments from running indefinitely
- Creates forcing function for decision

**Maintain discipline:**
- Don't change the experiment mid-way (unless it's clearly failing)
- Don't add features "while we're at it"
- Don't extend timeline without re-scoping

**Gather data systematically:**
- Track the metrics you defined
- Collect qualitative feedback too
- Note unexpected observations

### 6. Analyze and Learn

**Compare results to hypothesis:**
- Did we hit our success criteria?
- Did we miss our failure criteria?
- Were we in the gray zone?

**Extract insights:**
- What did we learn that we didn't expect?
- What questions do we have now?
- What should we test next?

**Make a decision:**
- **Persevere:** Results support hypothesis, continue direction
- **Pivot:** Results invalidate hypothesis, change direction
- **Iterate:** Results unclear, refine and test again

### 7. Accelerate the Loop

The goal is to minimize total time through the loop:

**Reduce build time:**
- Use no-code tools
- Manual before automated
- Templates and existing components

**Reduce measure time:**
- Instrument from the start
- Use real-time analytics
- Talk to customers directly

**Reduce learn time:**
- Daily/weekly reviews (not monthly)
- Predefined decision criteria
- Team alignment on interpretation

### 8. Common Mistakes

**Building before knowing what to learn:**
"Let's build it and see what happens" = expensive randomness

**Measuring vanity metrics:**
Page views and registrations feel good but don't indicate product-market fit

**Not setting success criteria upfront:**
Leads to confirmation bias: "Well, it wasn't a complete failure..."

**Slow loops:**
Monthly iterations when weekly is possible = 4x slower learning

**Ignoring negative results:**
"Users didn't engage, but maybe if we add this feature..."

### 9. Synthesis

After each loop:
- What did we learn?
- Was the hypothesis validated, invalidated, or unclear?
- What's our next hypothesis to test?
- Are we making progress toward product-market fit?
- Should we pivot or persevere?

## Example

**Idea**: "AI-powered meal planning app for busy parents"

### Loop 1: Test Demand

**Hypothesis:** Busy parents will sign up for AI meal planning

**Success Criteria:** 100 signups in 2 weeks from $200 ad spend

**Build:** Landing page with value prop and email capture
- "Stop asking 'What's for dinner?' - AI plans your week"
- Simple email signup form
- Built in 2 days using Carrd

**Measure:**
- Visitors: 2,400
- Signups: 156 (6.5% conversion)
- Source breakdown: Facebook 60%, Google 40%
- Demographics: 80% women, 25-44, suburban

**Learn:**
- ✓ Demand exists (exceeded 100 signups)
- Facebook > Google for this audience
- Messaging resonated more with moms than dads
- Several signups asked about dietary restrictions

**Decision:** Persevere → Next hypothesis

---

### Loop 2: Test Willingness to Pay

**Hypothesis:** Parents will pay $10/month for AI meal planning

**Success Criteria:** 10 paid signups ($10/month) in 2 weeks

**Build:** Updated landing page with:
- Pricing tier: $10/month or $80/year
- Stripe checkout integration
- 7-day free trial
- Built in 3 days

**Measure:**
- Existing list emailed: 156
- Clicked through: 48 (31%)
- Started trial: 18 (12%)
- Converted to paid: 3 (2%)
- Revenue: $30/month

**Learn:**
- ✗ Below target (3 vs 10 paid)
- Trial to paid conversion very low (17%)
- Feedback: "I liked it but forgot to use it"
- Feedback: "Recipes were too complicated"

**Decision:** Iterate → Test simplified recipes

---

### Loop 3: Test Retention Mechanism

**Hypothesis:** Daily push notification with simple recipes increases engagement

**Success Criteria:** 50% of users engage 3+ times per week

**Build:**
- Mobile-optimized version
- Push notification system
- Simplified 5-ingredient recipes
- Built in 5 days

**Measure:**
- Active trial users: 25
- Engaged 3+ times/week: 9 (36%)
- Trial to paid conversion: 5 (20%)

**Learn:**
- Getting closer (36% vs 50%)
- Push notifications helped (+3% engagement)
- Simplified recipes resonated
- Users who engaged 3+ converted 60%

**Decision:** Iterate → Focus on driving engagement

---

**Insight:** Three Build-Measure-Learn loops in 6 weeks taught us:
1. Demand exists for the concept
2. Pricing works but retention is the bottleneck
3. Engagement predicts conversion
4. Simple recipes + notifications help but aren't sufficient

Next hypothesis: Test grocery list integration (user feedback suggests this is missing value)
