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Transform raw data into compelling narratives that drive decisions and inspire action.
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Based on SOC occupation classification
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| name | data-storytelling |
| description | Transform raw data into compelling narratives that drive decisions and inspire action. |
| risk | safe |
| source | community |
| date_added | 2026-02-27 |
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/unit │
└─────────────────┴─────────────────┘
This/That (emphasize difference):
┌─────────────────────────────────────┐
│ CUSTOMER A vs B │
│ ┌──────────┐ ┌──────────┐ │
│ │ ████████ │ │ ██ │ │
│ │ $45,000 │ │ $8,000 │ │
│ │ LTV │ │ LTV │ │
│ └──────────┘ └──────────┘ │
│ Onboarded No onboarding │
└─────────────────────────────────────┘
import matplotlib.pyplot as plt
import pandas as pd
fig, ax = plt.subplots(figsize=(12, 6))
# Plot the main data
ax.plot(dates, revenue, linewidth=2, color='#2E86AB')
# Add annotation for key events
ax.annotate(
'Product Launch\n+32% spike',
xy=(launch_date, launch_revenue),
xytext=(launch_date, launch_revenue * 1.2),
fontsize=10,
arrowprops=dict(arrowstyle='->', color='#E63946'),
color='#E63946'
)
# Highlight a region
ax.axvspan(growth_start, growth_end, alpha=0.2, color='green',
label='Growth Period')
# Add threshold line
ax.axhline(y=target, color='gray', linestyle='--',
label=f'Target: ${target:,.0f}')
ax.set_title('Revenue Growth Story', fontsize=14, fontweight='bold')
ax.legend()
┌─────────────────────────────────────────────────────────────┐
│ KEY INSIGHT │
│ ══════════════════════════════════════════════════════════│
│ │
│ "Customers who complete onboarding in week 1 │
│ have 3x higher lifetime value" │
│ │
├──────────────────────┬──────────────────────────────────────┤
│ │ │
│ THE DATA │ THE IMPLICATION │
│ │ │
│ Week 1 completers: │ ✓ Prioritize onboarding UX │
│ • LTV: $4,500 │ ✓ Add day-1 success milestones │
│ • Retention: 85% │ ✓ Proactive week-1 outreach │
│ • NPS: 72 │ │
│ │ Investment: $75K │
│ Others: │ Expected ROI: 8x │
│ • LTV: $1,500 │ │
│ • Retention: 45% │ │
│ • NPS: 34 │ │
│ │ │
└──────────────────────┴──────────────────────────────────────┘
Slide 1: THE HEADLINE
"We can grow 40% faster by fixing onboarding"
Slide 2: THE CONTEXT
Current state metrics
Industry benchmarks
Gap analysis
Slide 3: THE DISCOVERY
What the data revealed
Surprising finding
Pattern identification
Slide 4: THE DEEP DIVE
Root cause analysis
Segment breakdowns
Statistical significance
Slide 5: THE RECOMMENDATION
Proposed actions
Resource requirements
Timeline
Slide 6: THE IMPACT
Expected outcomes
ROI calculation
Risk assessment
Slide 7: THE ASK
Specific request
Decision needed
Next steps
# Monthly Business Review: January 2024
## THE HEADLINE
Revenue up 15% but CAC increasing faster than LTV
## KEY METRICS AT A GLANCE
┌────────┬────────┬────────┬────────┐
│ MRR │ NRR │ CAC │ LTV │
│ $125K │ 108% │ $450 │ $2,200 │
│ ▲15% │ ▲3% │ ▲22% │ ▲8% │
└────────┴────────┴────────┴────────┘
## WHAT'S WORKING
✓ Enterprise segment growing 25% MoM
✓ Referral program driving 30% of new logos
✓ Support satisfaction at all-time high (94%)
## WHAT NEEDS ATTENTION
✗ SMB acquisition cost up 40%
✗ Trial conversion down 5 points
✗ Time-to-value increased by 3 days
## ROOT CAUSE
[Mini chart showing SMB vs Enterprise CAC trend]
SMB paid ads becoming less efficient.
CPC up 35% while conversion flat.
## RECOMMENDATION
1. Shift $20K/mo from paid to content
2. Launch SMB self-serve trial
3. A/B test shorter onboarding
## NEXT MONTH'S FOCUS
- Launch content marketing pilot
- Complete self-serve MVP
- Reduce time-to-value to < 7 days
BAD: "Q4 Sales Analysis"
GOOD: "Q4 Sales Beat Target by 23% - Here's Why"
BAD: "Customer Churn Report"
GOOD: "We're Losing $2.4M to Preventable Churn"
BAD: "Marketing Performance"
GOOD: "Content Marketing Delivers 4x ROI vs. Paid"
Formula:
[Specific Number] + [Business Impact] + [Actionable Context]
Building the narrative:
• "This leads us to ask..."
• "When we dig deeper..."
• "The pattern becomes clear when..."
• "Contrast this with..."
Introducing insights:
• "The data reveals..."
• "What surprised us was..."
• "The inflection point came when..."
• "The key finding is..."
Moving to action:
• "This insight suggests..."
• "Based on this analysis..."
• "The implication is clear..."
• "Our recommendation is..."
Acknowledge limitations:
• "With 95% confidence, we can say..."
• "The sample size of 500 shows..."
• "While correlation is strong, causation requires..."
• "This trend holds for [segment], though [caveat]..."
Present ranges:
• "Impact estimate: $400K-$600K"
• "Confidence interval: 15-20% improvement"
• "Best case: X, Conservative: Y"