# Innovation Accounting

## Purpose
Measure progress toward product-market fit when traditional metrics (revenue, profit) don't yet apply, providing a framework for accountability and decision-making in early-stage ventures.

## When to Use
- Early stage when revenue is minimal or zero
- When traditional metrics don't reflect progress
- Need to demonstrate progress to stakeholders (board, investors)
- Making pivot or persevere decisions
- Setting milestones for innovation teams

## Process

### 1. The Three Steps of Innovation Accounting

**Step 1: Establish Baseline**
Use an MVP or experiment to measure where you are NOW
- Run your first experiment
- Measure current conversion rates, engagement, etc.
- Establish starting point for all key metrics

**Step 2: Tune the Engine**
Make changes to improve metrics toward your ideal
- Each experiment targets specific metric improvement
- If changes don't improve metrics, they failed
- Iterate until metrics approach target

**Step 3: Pivot or Persevere**
Decide whether progress justifies continuing
- Are metrics improving toward sustainable business?
- If yes: continue tuning
- If no: pivot (structured change) or stop

### 2. Define Your Learning Milestones

Instead of revenue targets (too early), define learning milestones:

**Problem Validation Milestones:**
- [ ] Completed 20+ customer discovery conversations
- [ ] Identified 5+ earlyvangelists with all characteristics
- [ ] Documented problem in customer's own words
- [ ] Quantified cost of problem (time, money, pain)

**Solution Validation Milestones:**
- [ ] Built MVP that tests value hypothesis
- [ ] Achieved X% activation rate
- [ ] Received commitment (time, money, reputation)
- [ ] Got referrals to additional potential customers

**Product-Market Fit Milestones:**
- [ ] Achieved Y% retention at Week 4
- [ ] Paying customers without heavy sales effort
- [ ] Word-of-mouth driving new customers
- [ ] 40%+ "very disappointed" on Sean Ellis test

**Growth Milestones:**
- [ ] Identified primary engine of growth
- [ ] Achieved positive unit economics (LTV > CAC)
- [ ] Demonstrated repeatable acquisition channel
- [ ] Scaling while maintaining quality

### 3. Track Actionable Metrics

**Vanity metrics to avoid:**
- Total registered users (if most are inactive)
- Page views (if they don't convert)
- "Engagement" without definition
- Gross numbers without cohort analysis

**Actionable metrics to track:**
- Activation rate (% reaching "aha moment")
- Retention by cohort (are newer cohorts better?)
- Conversion rate (% taking desired action)
- Engagement frequency (DAU/MAU ratio)
- Net Promoter Score (would they recommend?)

**The Three A's of Good Metrics:**

1. **Actionable:** Clear cause and effect
   - Bad: "Engagement increased"
   - Good: "Changing onboarding increased activation 20%"

2. **Accessible:** Everyone understands
   - Use real units (customers, dollars)
   - Make dashboards simple

3. **Auditable:** Can be verified
   - Can you manually check?
   - Can you talk to the people in the data?

### 4. Build Your Innovation Dashboard

Track these categories:

**Validated Learning:**
- Hypotheses tested this period
- Hypotheses validated vs. invalidated
- Key insights documented

**Progress Metrics:**
- Activation rate (current vs. target)
- Retention cohorts (current vs. target)
- Conversion rate (current vs. target)
- Growth rate (current vs. target)

**Engine-Specific Metrics:**
- Sticky: Churn rate, DAU/MAU
- Viral: K-factor, cycle time
- Paid: CAC, LTV, LTV:CAC ratio

**Resource Efficiency:**
- Burn rate
- Runway remaining
- Cost per experiment
- Time per iteration

### 5. Report Progress

For stakeholders, report:

**What we learned:**
- Hypotheses tested
- Results (validated/invalidated)
- Insights that changed our understanding

**Where we are:**
- Current metrics vs. baseline
- Trend direction (improving, stable, declining)
- Distance to target milestones

**What we're doing next:**
- Next experiments planned
- Expected timeline
- What we need to learn

**Decision points ahead:**
- When we'll have enough data to decide
- What would trigger a pivot
- What would trigger scaling

### 6. Make Pivot/Persevere Decisions

**Hold regular decision meetings:**
- Weekly for early-stage (high iteration rate)
- Monthly for growth-stage
- Include product AND business perspectives

**Evaluate evidence:**
- Are metrics moving in the right direction?
- Is the rate of improvement sufficient?
- What would change our trajectory?

**Apply decision criteria:**

| Situation | Evidence | Decision |
|-----------|----------|----------|
| Improving fast | Metrics trending toward target | Persevere |
| Improving slowly | Progress but below expectations | Set deadline for decision |
| Flat | Multiple experiments, no change | Consider pivot |
| Declining | Getting worse despite efforts | Pivot or stop |

### 7. Avoid Common Traps

**The success theater trap:**
- Reporting vanity metrics that look good
- Fix: Report only actionable metrics

**The "just one more feature" trap:**
- Believing the next feature will change everything
- Fix: Set clear experiment criteria upfront

**The prolonged runway trap:**
- Persevering because you still have money
- Fix: Time-box decisions regardless of runway

**The "we're learning" trap:**
- Calling all failure "learning" without action
- Fix: Document what changed based on learning

### 8. Synthesis

After setting up innovation accounting:
- What are your current baseline metrics?
- What would validated product-market fit look like (targets)?
- What experiments will move you toward targets?
- When will you make your next pivot/persevere decision?
- What would trigger a pivot vs. persevering?

## Example

**Startup**: AI-powered customer support tool

### Baseline (Month 0)

After launching MVP:
- Activation rate: 15% (% completing first ticket deflection)
- Week 1 retention: 20%
- Week 4 retention: 8%
- Willingness to pay: 2% (% attempting purchase)

### Targets (Product-Market Fit)

Based on comparable companies:
- Activation rate: 50%
- Week 1 retention: 60%
- Week 4 retention: 40%
- Willingness to pay: 10%

### Month 1-3: Tuning the Engine

**Experiments run:**

| Experiment | Target | Result | Learning |
|------------|--------|--------|----------|
| Simplified onboarding | Activation 25% | 28% ✓ | Removing optional steps helps |
| In-app guidance | Activation 35% | 32% ✓ | Guided setup works |
| Email drip campaign | Retention 25% | 22% ✗ | Users don't read emails |
| Slack integration | Retention 35% | 38% ✓ | Integration = stickiness |
| Free trial extension | WTP 5% | 3% ✗ | Longer trial doesn't convert |

**Month 3 metrics:**
- Activation: 32% (was 15%, target 50%)
- Week 1 retention: 35% (was 20%, target 60%)
- Week 4 retention: 18% (was 8%, target 40%)
- WTP: 4% (was 2%, target 10%)

### Pivot/Persevere Decision (Month 3)

**Evidence:**
- Activation: +17% improvement (good trajectory)
- Retention: +15%/+10% improvement (good trajectory)
- WTP: +2% improvement (slow but positive)

**Decision: Persevere** with focus on:
- Continue onboarding improvements
- Double down on integrations (proven driver)
- Experiment more on conversion to paid

### Month 4-6: Continued Tuning

**Experiments run:**

| Experiment | Target | Result |
|------------|--------|--------|
| Integration marketplace | Retention 45% | 48% ✓ |
| Usage-based pricing | WTP 8% | 7% ~ |
| Success stories/case studies | WTP 8% | 9% ✓ |
| Reduced price | WTP 12% | 15% ✓ |

**Month 6 metrics:**
- Activation: 52% (target 50% ✓ HIT)
- Week 1 retention: 55% (target 60% ~)
- Week 4 retention: 38% (target 40% ~)
- WTP: 15% (target 10% ✓ HIT)

### Pivot/Persevere Decision (Month 6)

**Evidence:**
- 3 of 4 targets hit or nearly hit
- Clear trajectory toward product-market fit
- Unit economics becoming viable

**Decision: Persevere and begin scaling**
- Transition from finding fit to growth
- Begin paid acquisition experiments
- Hire first customer success person

### Innovation Accounting Report (for Board)

**Summary:**
Over 6 months, we validated:
1. Problem exists (customers have support overload)
2. Solution works (AI deflects tickets effectively)
3. Business model viable (customers will pay at current price)

**Key Metrics:**
| Metric | Baseline | Month 3 | Month 6 | Target |
|--------|----------|---------|---------|--------|
| Activation | 15% | 32% | 52% | 50% |
| Week 4 Retention | 8% | 18% | 38% | 40% |
| Conversion | 2% | 4% | 15% | 10% |

**Next Phase:**
Shift from innovation accounting to growth accounting
- Focus on CAC, LTV, payback period
- Begin scaling acquisition
- Maintain product-market fit while growing

**Insight:** Innovation accounting provided clear evidence of progress when revenue was near-zero. The metrics showed we were building something people wanted, not just something that felt good to build.
