data-storytelling

Data Storytelling

Transform raw data into compelling narratives that drive decisions and inspire action.

When to Use This Skill

• Presenting analytics to executives
• Creating quarterly business reviews
• Building investor presentations
• Writing data-driven reports
• Communicating insights to non-technical audiences
• Making recommendations based on data

Core Concepts

1. Story Structure

Setup → Conflict → Resolution

Setup: Context and baseline
Conflict: The problem or opportunity
Resolution: Insights and recommendations

2. Narrative Arc

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

3. Three Pillars

Story Frameworks

Framework 1: The Problem-Solution Story

# 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
-

Framework 2: The Trend Story

# 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 |

Framework 3: The Comparison Story

# 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
-

Visualization Techniques

Technique 1: Progressive Reveal

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]

Technique 2: Contrast and Compare

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 │
└─────────────────────────────────────┘

Technique 3: Annotation and Highlight

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')

Presentation Templates

Template 1: Executive Summary Slide

┌─────────────────────────────────────────────────────────────┐
│  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           │                                      │
│                      │                                      │
└──────────────────────┴──────────────────────────────────────┘

Template 2: Data Story Flow

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

Template 3: One-Page Dashboard Story

# 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

Writing Techniques

Headlines That Work

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]

Transition Phrases

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..."

Handling Uncertainty

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"

Best Practices

Do's

• Start with the "so what" - Lead with insight
• Use the rule of three - Three points, three comparisons
• Show, don't tell - Let data speak
• Make it personal - Connect to audience goals
• End with action - Clear next steps

Don'ts

• Don't data dump - Curate ruthlessly
• Don't bury the insight - Front-load key findings
• Don't use jargon - Match audience vocabulary
• Don't show methodology first - Context, then method
• Don't forget the narrative - Numbers need meaning

Resources

• Storytelling with Data (Cole Nussbaumer)
• The Pyramid Principle (Barbara Minto)
• Resonate (Nancy Duarte)