{"id":"8480b894-a40f-4ca7-83cb-f4c7389cfc83","shortId":"DbjepK","kind":"skill","title":"data-storytelling","tagline":"Transform raw data into compelling narratives that drive decisions and inspire action.","description":"# Data Storytelling\n\nTransform raw data into compelling narratives that drive decisions and inspire action.\n\n## Do not use this skill when\n\n- The task is unrelated to data storytelling\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources/implementation-playbook.md`.\n\n## Use this skill when\n\n- Presenting analytics to executives\n- Creating quarterly business reviews\n- Building investor presentations\n- Writing data-driven reports\n- Communicating insights to non-technical audiences\n- Making recommendations based on data\n\n## Core Concepts\n\n### 1. Story Structure\n\n```\nSetup → Conflict → Resolution\n\nSetup: Context and baseline\nConflict: The problem or opportunity\nResolution: Insights and recommendations\n```\n\n### 2. Narrative Arc\n\n```\n1. Hook: Grab attention with surprising insight\n2. Context: Establish the baseline\n3. Rising Action: Build through data points\n4. Climax: The key insight\n5. Resolution: Recommendations\n6. Call to Action: Next steps\n```\n\n### 3. Three Pillars\n\n| Pillar        | Purpose  | Components                       |\n| ------------- | -------- | -------------------------------- |\n| **Data**      | Evidence | Numbers, trends, comparisons     |\n| **Narrative** | Meaning  | Context, causation, implications |\n| **Visuals**   | Clarity  | Charts, diagrams, highlights     |\n\n## Story Frameworks\n\n### Framework 1: The Problem-Solution Story\n\n```markdown\n# Customer Churn Analysis\n\n## The Hook\n\n\"We're losing $2.4M annually to preventable churn.\"\n\n## The Context\n\n- Current churn rate: 8.5% (industry average: 5%)\n- Average customer lifetime value: $4,800\n- 500 customers churned last quarter\n\n## The Problem\n\nAnalysis of churned customers reveals a pattern:\n\n- 73% churned within first 90 days\n- Common factor: < 3 support interactions\n- Low feature adoption in first month\n\n## The Insight\n\n[Show engagement curve visualization]\nCustomers who don't engage in the first 14 days\nare 4x more likely to churn.\n\n## The Solution\n\n1. Implement 14-day onboarding sequence\n2. Proactive outreach at day 7\n3. Feature adoption tracking\n\n## Expected Impact\n\n- Reduce early churn by 40%\n- Save $960K annually\n- Payback period: 3 months\n\n## Call to Action\n\nApprove $50K budget for onboarding automation.\n```\n\n### Framework 2: The Trend Story\n\n```markdown\n# Q4 Performance Analysis\n\n## Where We Started\n\nQ3 ended with $1.2M MRR, 15% below target.\nTeam morale was low after missed goals.\n\n## What Changed\n\n[Timeline visualization]\n\n- Oct: Launched self-serve pricing\n- Nov: Reduced friction in signup\n- Dec: Added customer success calls\n\n## The Transformation\n\n[Before/after comparison chart]\n| Metric | Q3 | Q4 | Change |\n|----------------|--------|--------|--------|\n| Trial → Paid | 8% | 15% | +87% |\n| Time to Value | 14 days| 5 days | -64% |\n| Expansion Rate | 2% | 8% | +300% |\n\n## Key Insight\n\nSelf-serve + high-touch creates compound growth.\nCustomers who self-serve AND get a success call\nhave 3x higher expansion rate.\n\n## Going Forward\n\nDouble down on hybrid model.\nTarget: $1.8M MRR by Q2.\n```\n\n### Framework 3: The Comparison Story\n\n```markdown\n# Market Opportunity Analysis\n\n## The Question\n\nShould we expand into EMEA or APAC first?\n\n## The Comparison\n\n[Side-by-side market analysis]\n\n### EMEA\n\n- Market size: $4.2B\n- Growth rate: 8%\n- Competition: High\n- Regulatory: Complex (GDPR)\n- Language: Multiple\n\n### APAC\n\n- Market size: $3.8B\n- Growth rate: 15%\n- Competition: Moderate\n- Regulatory: Varied\n- Language: Multiple\n\n## The Analysis\n\n[Weighted scoring matrix visualization]\n\n| Factor      | Weight | EMEA Score | APAC Score |\n| ----------- | ------ | ---------- | ---------- |\n| Market Size | 25%    | 5          | 4          |\n| Growth      | 30%    | 3          | 5          |\n| Competition | 20%    | 2          | 4          |\n| Ease        | 25%    | 2          | 3          |\n| **Total**   |        | **2.9**    | **4.1**    |\n\n## The Recommendation\n\nAPAC first. Higher growth, less competition.\nStart with Singapore hub (English, business-friendly).\nEnter EMEA in Year 2 with localization ready.\n\n## Risk Mitigation\n\n- Timezone coverage: Hire 24/7 support\n- Cultural fit: Local partnerships\n- Payment: Multi-currency from day 1\n```\n\n## Visualization Techniques\n\n### Technique 1: Progressive Reveal\n\n```markdown\nStart simple, add layers:\n\nSlide 1: \"Revenue is growing\" [single line chart]\nSlide 2: \"But growth is slowing\" [add growth rate overlay]\nSlide 3: \"Driven by one segment\" [add segment breakdown]\nSlide 4: \"Which is saturating\" [add market share]\nSlide 5: \"We need new segments\" [add opportunity zones]\n```\n\n### Technique 2: Contrast and Compare\n\n```markdown\nBefore/After:\n┌─────────────────┬─────────────────┐\n│ BEFORE │ AFTER │\n│ │ │\n│ Process: 5 days│ Process: 1 day │\n│ Errors: 15% │ Errors: 2% │\n│ Cost: $50/unit │ Cost: $20/unit │\n└─────────────────┴─────────────────┘\n\nThis/That (emphasize difference):\n┌─────────────────────────────────────┐\n│ CUSTOMER A vs B │\n│ ┌──────────┐ ┌──────────┐ │\n│ │ ████████ │ │ ██ │ │\n│ │ $45,000 │ │ $8,000 │ │\n│ │ LTV │ │ LTV │ │\n│ └──────────┘ └──────────┘ │\n│ Onboarded No onboarding │\n└─────────────────────────────────────┘\n```\n\n### Technique 3: Annotation and Highlight\n\n```python\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nfig, ax = plt.subplots(figsize=(12, 6))\n\n# Plot the main data\nax.plot(dates, revenue, linewidth=2, color='#2E86AB')\n\n# Add annotation for key events\nax.annotate(\n    'Product Launch\\n+32% spike',\n    xy=(launch_date, launch_revenue),\n    xytext=(launch_date, launch_revenue * 1.2),\n    fontsize=10,\n    arrowprops=dict(arrowstyle='->', color='#E63946'),\n    color='#E63946'\n)\n\n# Highlight a region\nax.axvspan(growth_start, growth_end, alpha=0.2, color='green',\n           label='Growth Period')\n\n# Add threshold line\nax.axhline(y=target, color='gray', linestyle='--',\n           label=f'Target: ${target:,.0f}')\n\nax.set_title('Revenue Growth Story', fontsize=14, fontweight='bold')\nax.legend()\n```\n\n## Presentation Templates\n\n### Template 1: Executive Summary Slide\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│  KEY INSIGHT                                                │\n│  ══════════════════════════════════════════════════════════│\n│                                                             │\n│  \"Customers who complete onboarding in week 1              │\n│   have 3x higher lifetime value\"                           │\n│                                                             │\n├──────────────────────┬──────────────────────────────────────┤\n│                      │                                      │\n│  THE DATA            │  THE IMPLICATION                     │\n│                      │                                      │\n│  Week 1 completers:  │  ✓ Prioritize onboarding UX         │\n│  • LTV: $4,500       │  ✓ Add day-1 success milestones     │\n│  • Retention: 85%    │  ✓ Proactive week-1 outreach        │\n│  • NPS: 72           │                                      │\n│                      │  Investment: $75K                    │\n│  Others:             │  Expected ROI: 8x                    │\n│  • LTV: $1,500       │                                      │\n│  • Retention: 45%    │                                      │\n│  • NPS: 34           │                                      │\n│                      │                                      │\n└──────────────────────┴──────────────────────────────────────┘\n```\n\n### Template 2: Data Story Flow\n\n```\nSlide 1: THE HEADLINE\n\"We can grow 40% faster by fixing onboarding\"\n\nSlide 2: THE CONTEXT\nCurrent state metrics\nIndustry benchmarks\nGap analysis\n\nSlide 3: THE DISCOVERY\nWhat the data revealed\nSurprising finding\nPattern identification\n\nSlide 4: THE DEEP DIVE\nRoot cause analysis\nSegment breakdowns\nStatistical significance\n\nSlide 5: THE RECOMMENDATION\nProposed actions\nResource requirements\nTimeline\n\nSlide 6: THE IMPACT\nExpected outcomes\nROI calculation\nRisk assessment\n\nSlide 7: THE ASK\nSpecific request\nDecision needed\nNext steps\n```\n\n### Template 3: One-Page Dashboard Story\n\n```markdown\n# Monthly Business Review: January 2024\n\n## THE HEADLINE\n\nRevenue up 15% but CAC increasing faster than LTV\n\n## KEY METRICS AT A GLANCE\n\n┌────────┬────────┬────────┬────────┐\n│ MRR │ NRR │ CAC │ LTV │\n│ $125K │ 108% │ $450 │ $2,200 │\n│ ▲15% │ ▲3% │ ▲22% │ ▲8% │\n└────────┴────────┴────────┴────────┘\n\n## WHAT'S WORKING\n\n✓ Enterprise segment growing 25% MoM\n✓ Referral program driving 30% of new logos\n✓ Support satisfaction at all-time high (94%)\n\n## WHAT NEEDS ATTENTION\n\n✗ SMB acquisition cost up 40%\n✗ Trial conversion down 5 points\n✗ Time-to-value increased by 3 days\n\n## ROOT CAUSE\n\n[Mini chart showing SMB vs Enterprise CAC trend]\nSMB paid ads becoming less efficient.\nCPC up 35% while conversion flat.\n\n## RECOMMENDATION\n\n1. Shift $20K/mo from paid to content\n2. Launch SMB self-serve trial\n3. A/B test shorter onboarding\n\n## NEXT MONTH'S FOCUS\n\n- Launch content marketing pilot\n- Complete self-serve MVP\n- Reduce time-to-value to < 7 days\n```\n\n## Writing Techniques\n\n### Headlines That Work\n\n```markdown\nBAD: \"Q4 Sales Analysis\"\nGOOD: \"Q4 Sales Beat Target by 23% - Here's Why\"\n\nBAD: \"Customer Churn Report\"\nGOOD: \"We're Losing $2.4M to Preventable Churn\"\n\nBAD: \"Marketing Performance\"\nGOOD: \"Content Marketing Delivers 4x ROI vs. Paid\"\n\nFormula:\n[Specific Number] + [Business Impact] + [Actionable Context]\n```\n\n### Transition Phrases\n\n```markdown\nBuilding the narrative:\n• \"This leads us to ask...\"\n• \"When we dig deeper...\"\n• \"The pattern becomes clear when...\"\n• \"Contrast this with...\"\n\nIntroducing insights:\n• \"The data reveals...\"\n• \"What surprised us was...\"\n• \"The inflection point came when...\"\n• \"The key finding is...\"\n\nMoving to action:\n• \"This insight suggests...\"\n• \"Based on this analysis...\"\n• \"The implication is clear...\"\n• \"Our recommendation is...\"\n```\n\n### Handling Uncertainty\n\n```markdown\nAcknowledge limitations:\n• \"With 95% confidence, we can say...\"\n• \"The sample size of 500 shows...\"\n• \"While correlation is strong, causation requires...\"\n• \"This trend holds for [segment], though [caveat]...\"\n\nPresent ranges:\n• \"Impact estimate: $400K-$600K\"\n• \"Confidence interval: 15-20% improvement\"\n• \"Best case: X, Conservative: Y\"\n```\n\n## Best Practices\n\n### Do's\n\n- **Start with the \"so what\"** - Lead with insight\n- **Use the rule of three** - Three points, three comparisons\n- **Show, don't tell** - Let data speak\n- **Make it personal** - Connect to audience goals\n- **End with action** - Clear next steps\n\n### Don'ts\n\n- **Don't data dump** - Curate ruthlessly\n- **Don't bury the insight** - Front-load key findings\n- **Don't use jargon** - Match audience vocabulary\n- **Don't show methodology first** - Context, then method\n- **Don't forget the narrative** - Numbers need meaning\n\n## Resources\n\n- [Storytelling with Data (Cole Nussbaumer)](https://www.storytellingwithdata.com/)\n- [The Pyramid Principle (Barbara Minto)](https://www.amazon.com/Pyramid-Principle-Logic-Writing-Thinking/dp/0273710516)\n- [Resonate (Nancy Duarte)](https://www.duarte.com/resonate/)\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are 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