react-modernization
Master React version upgrades, class to hooks migration, concurrent features adoption, and codemods for automated transformation.
data-storytelling
Transform raw data into compelling narratives that drive decisions and inspire action.
Full skill instructions
Transform raw data into compelling narratives that drive decisions and inspire action.
resources/implementation-playbook.md.Setup → Conflict → Resolution
Setup: Context and baseline
Conflict: The problem or opportunity
Resolution: Insights and recommendations
1. Hook: Grab attention with surprising insight
2. Context: Establish the baseline
3. Rising Action: Build through data points
4. Climax: The key insight
5. Resolution: Recommendations
6. Call to Action: Next steps
| Pillar | Purpose | Components |
|---|---|---|
| Data | Evidence | Numbers, trends, comparisons |
| Narrative | Meaning | Context, causation, implications |
| Visuals | Clarity | Charts, diagrams, highlights |
# Customer Churn Analysis
## The Hook
"We're losing $2.4M annually to preventable churn."
## The Context
- Current churn rate: 8.5% (industry average: 5%)
- Average customer lifetime value: $4,800
- 500 customers churned last quarter
## The Problem
Analysis of churned customers reveals a pattern:
- 73% churned within first 90 days
- Common factor: < 3 support interactions
- Low feature adoption in first month
## The Insight
[Show engagement curve visualization]
Customers who don't engage in the first 14 days
are 4x more likely to churn.
## The Solution
1. Implement 14-day onboarding sequence
2. Proactive outreach at day 7
3. Feature adoption tracking
## Expected Impact
- Reduce early churn by 40%
- Save $960K annually
- Payback period: 3 months
## Call to Action
Approve $50K budget for onboarding automation.
# Q4 Performance Analysis
## Where We Started
Q3 ended with $1.2M MRR, 15% below target.
Team morale was low after missed goals.
## What Changed
[Timeline visualization]
- Oct: Launched self-serve pricing
- Nov: Reduced friction in signup
- Dec: Added customer success calls
## The Transformation
[Before/after comparison chart]
| Metric | Q3 | Q4 | Change |
|----------------|--------|--------|--------|
| Trial → Paid | 8% | 15% | +87% |
| Time to Value | 14 days| 5 days | -64% |
| Expansion Rate | 2% | 8% | +300% |
## Key Insight
Self-serve + high-touch creates compound growth.
Customers who self-serve AND get a success call
have 3x higher expansion rate.
## Going Forward
Double down on hybrid model.
Target: $1.8M MRR by Q2.
# Market Opportunity Analysis
## The Question
Should we expand into EMEA or APAC first?
## The Comparison
[Side-by-side market analysis]
### EMEA
- Market size: $4.2B
- Growth rate: 8%
- Competition: High
- Regulatory: Complex (GDPR)
- Language: Multiple
### APAC
- Market size: $3.8B
- Growth rate: 15%
- Competition: Moderate
- Regulatory: Varied
- Language: Multiple
## The Analysis
[Weighted scoring matrix visualization]
| Factor | Weight | EMEA Score | APAC Score |
| ----------- | ------ | ---------- | ---------- |
| Market Size | 25% | 5 | 4 |
| Growth | 30% | 3 | 5 |
| Competition | 20% | 2 | 4 |
| Ease | 25% | 2 | 3 |
| **Total** | | **2.9** | **4.1** |
## The Recommendation
APAC first. Higher growth, less competition.
Start with Singapore hub (English, business-friendly).
Enter EMEA in Year 2 with localization ready.
## Risk Mitigation
- Timezone coverage: Hire 24/7 support
- Cultural fit: Local partnerships
- Payment: Multi-currency from day 1
Start simple, add layers:
Slide 1: "Revenue is growing" [single line chart]
Slide 2: "But growth is slowing" [add growth rate overlay]
Slide 3: "Driven by one segment" [add segment breakdown]
Slide 4: "Which is saturating" [add market share]
Slide 5: "We need new segments" [add opportunity zones]
Before/After:
┌─────────────────┬─────────────────┐
│ BEFORE │ AFTER │
│ │ │
│ Process: 5 days│ Process: 1 day │
│ Errors: 15% │ Errors: 2% │
│ Cost: $50/unit │ Cost: $20/uni
Master React version upgrades, class to hooks migration, concurrent features adoption, and codemods for automated transformation.
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