# Monitoring Templates

Templates for monitoring impact and iterating in Phase 8 (Product Evolution).

---

## Metrics Tracking Dashboard

```markdown
# Metrics Tracking: DD-XXX

**Release Date:** 2024-12-13
**Measurement Period:** 2024-12-13 to 2024-12-27

## Daily Tracking

| Date  | Feature X Usage | Drop-off Rate | Notes         |
| ----- | --------------- | ------------- | ------------- |
| 12/13 | 18%             | 38%           | Day 1         |
| 12/14 | 22%             | 35%           | Trending up   |
| 12/15 | 28%             | 30%           | Good progress |
| ...   | ...             | ...           | ...           |
| 12/27 | 58%             | 12%           | Final         |

## Trend Analysis

[Chart or description of trends]
```

---

## Qualitative Feedback Tracking

```markdown
# Qualitative Feedback: DD-XXX

## Positive Feedback (8 mentions)
- "Now I understand how to use Feature X!" (3)
- "The guide was really helpful" (2)
- "Love the new onboarding" (3)

## Negative Feedback (2 mentions)
- "Guide is too long" (1)
- "Can't skip the guide" (1)

## Neutral Feedback (3 mentions)
- "Didn't notice the change" (3)
```

---

## Impact Report Template

**File:** `analytics/DD-XXX-impact-report.md`

```markdown
# Impact Report: DD-XXX [Name]

**Release Date:** 2024-12-13
**Measurement Period:** 2024-12-13 to 2024-12-27
**Report Date:** 2024-12-28

---

## Executive Summary

**Result:** [SUCCESS | PARTIAL SUCCESS | FAILURE]

[2-3 sentences summarizing the impact]

Example:
"Design Delivery DD-XXX successfully improved Feature X usage from
15% to 58%, nearly meeting the 60% target. Drop-off decreased
from 40% to 12%, exceeding the 10% target. User feedback is
overwhelmingly positive."

---

## Metrics Results

### Metric 1: Feature X Usage Rate
- **Baseline:** 15%
- **Target:** 60%
- **Actual:** 58%
- **Result:** 97% of target ✅ (PASS)
- **Trend:** Steady increase over 2 weeks

### Metric 2: Drop-off Rate
- **Baseline:** 40%
- **Target:** 10%
- **Actual:** 12%
- **Result:** Exceeded target ✅ (PASS)
- **Trend:** Sharp decrease in first week, stabilized

### Metric 3: Support Tickets
- **Baseline:** 12/month
- **Target:** 2/month
- **Actual:** 3/month
- **Result:** 75% reduction ✅ (PASS)

### Metric 4: User Satisfaction
- **Baseline:** 3.2/5
- **Target:** 4.5/5
- **Actual:** 4.3/5
- **Result:** 96% of target ✅ (PASS)

---

## Overall Assessment

**Success Criteria:**
- Feature X usage > 50% ✅
- Drop-off < 15% ✅
- Support tickets < 5/month ✅

**Result:** SUCCESS ✅

All success criteria met or exceeded.

---

## What Worked

1. **Inline onboarding was effective**
   - Users understood Feature X immediately
   - Completion rate increased significantly

2. **Step-by-step guide was helpful**
   - User feedback praised the guide
   - Reduced confusion

3. **Success celebration was motivating**
   - Users felt accomplished
   - Positive sentiment increased

---

## What Didn't Work

1. **Guide length**
   - Some users found it too long
   - Consider shortening in future iteration

2. **Skip option**
   - Some users wanted to skip
   - Consider adding "Skip" button

---

## Learnings

1. **Onboarding matters for complex features**
   - Even simple features benefit from guidance
   - First impression is critical

2. **Measurement validates hypotheses**
   - Our hypothesis was correct
   - Data-driven decisions work

3. **Small changes have big impact**
   - 3-day effort → 4x usage increase
   - Kaizen philosophy validated

---

## Recommendations

### Short-term (Next Sprint)
1. Add "Skip" button to guide
2. Shorten guide from 5 steps to 3 steps
3. A/B test guide length

### Long-term (Next Quarter)
1. Apply onboarding pattern to other features
2. Create reusable onboarding component
3. Measure onboarding impact across product

---

## Next Kaizen Cycle

**Based on this success, next improvement opportunity:**

[Identify next improvement based on learnings]

Example:
"Feature Y has similar low usage (20%). Apply same onboarding
pattern to Feature Y in next Kaizen cycle."

---

## Conclusion

Design Delivery DD-XXX successfully achieved its goals. The
improvement demonstrates the power of Kaizen - small, focused
changes that compound over time.

**Status:** ✅ SUCCESS - Ready for next cycle!
```

---

## Team Results Communication

```
WDS Designer → Team

Subject: Impact Report: DD-XXX - SUCCESS ✅

Hi Team!

Impact report for DD-XXX is complete!

🎉 **Result:** SUCCESS

📊 **Key Results:**
- Feature X usage: 15% → 58% (4x increase!)
- Drop-off: 40% → 12% (70% reduction!)
- Support tickets: 12/month → 3/month (75% reduction!)
- User satisfaction: 3.2/5 → 4.3/5

💡 **Key Learning:**
Small, focused improvements (3 days effort) can have massive
impact (4x usage increase). Kaizen philosophy works!

📁 **Full Report:**
analytics/DD-XXX-impact-report.md

🔄 **Next Cycle:**
Apply same pattern to Feature Y (similar low usage issue).

Thanks for the great collaboration!

[Your name]
WDS Designer
```

---

## Kaizen Cycle Log Template

```markdown
# Kaizen Cycle Log

## Cycle 1: DD-001 Feature X Onboarding
- Started: 2024-12-09
- Completed: 2024-12-28
- Result: SUCCESS ✅
- Impact: 4x usage increase
- Learning: Onboarding matters for complex features

## Cycle 2: DD-002 Feature Y Onboarding
- Started: 2024-12-28
- Status: In Progress
- Goal: Apply validated pattern to similar feature
- Expected: 4x usage increase
```

---

## Kaizen Prioritization Template

```markdown
# Kaizen Prioritization

## Option A: Refine DD-XXX
- Impact: Medium (58% → 65%)
- Effort: Low (1 day)
- Learning: Low (incremental)
- Priority: MEDIUM

## Option B: Apply to Feature Y
- Impact: High (20% → 80%)
- Effort: Low (2 days)
- Learning: High (validates pattern)
- Priority: HIGH ✅

## Option C: Fix Feature Z Performance
- Impact: Medium (35% → 20% drop-off)
- Effort: Low (1 day)
- Learning: Medium (performance optimization)
- Priority: MEDIUM

**Decision:** Start with Option B (highest priority)
```

---

## Learnings Documentation Template

```markdown
# Learnings from DD-XXX

## What Worked
1. [Learning 1]
2. [Learning 2]
3. [Learning 3]

## What Didn't Work
1. [Learning 1]
2. [Learning 2]

## Patterns Emerging
1. [Pattern 1]
2. [Pattern 2]

## Hypotheses Validated
1. [Hypothesis 1]: ✅ Confirmed
2. [Hypothesis 2]: ❌ Rejected

## New Questions
1. [Question 1]
2. [Question 2]
```

---

## Next Iteration Templates

### Iterate on Current Update

```markdown
# Next Iteration: DD-XXX Refinement

**Current Status:**
- Feature X usage: 58% (target: 60%)
- User feedback: "Guide too long"

**Next Improvement:**
- Shorten guide from 5 steps to 3 steps
- Add "Skip" button
- A/B test guide length

**Expected Impact:**
- Feature X usage: 58% → 65%
- User satisfaction: 4.3/5 → 4.7/5

**Effort:** 1 day
**Priority:** Medium
```

### Apply Pattern to Similar Feature

```markdown
# Next Opportunity: Apply Pattern to Feature Y

**Learning from DD-XXX:**
"Onboarding increases usage 4x for complex features"

**Similar Problem:**
- Feature Y usage: 20% (low)
- User feedback: "Don't understand Feature Y"
- Similar complexity to Feature X

**Proposed Solution:**
Apply same onboarding pattern to Feature Y

**Expected Impact:**
- Feature Y usage: 20% → 80% (4x increase)
- Based on DD-XXX results

**Effort:** 2 days
**Priority:** High
```

### Address New Problem

```markdown
# Next Opportunity: New Problem Identified

**New Data:**
- Feature Z drop-off: 35% (increased from 20%)
- User feedback: "Feature Z is slow"
- Analytics: Load time 5 seconds (was 2 seconds)

**Root Cause:**
Recent update added heavy images, slowing load time

**Proposed Solution:**
Optimize images and implement lazy loading

**Expected Impact:**
- Load time: 5s → 2s
- Drop-off: 35% → 20%

**Effort:** 1 day
**Priority:** High
```
