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data-storytelling

data_procSafeIntermediateClaude
🤖 AI Summary

This agent takes structured data (e.g., CSV, JSON) and generates a narrative report with key insights, trends, and actionable recommendations, outputting text suitable for presentations or dashboards.

How to Install

This skill comes from a community source.

Data Storytelling

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

Do not use this skill when

  • The task is unrelated to data storytelling
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

  • 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

Pillar Purpose Components
Data Evidence Numbers, trends, comparisons
Narrative Meaning Context, causation, implications
Visuals Clarity Charts, diagrams, highlights

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
- 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.

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 |
|----------------|--------|--------|--------|
| 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.

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
- 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

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

```markdown Before/After: ┌─────────────────┬─────────────────┐ │ BEFORE │ AFTER │ │ │ │ │ Process: 5 days│ Process: 1 day │ │ Errors: 15% │ Errors: 2% │ │ Cost: $50/unit │ Cost: $20/un

Details

Category Data → data_proc
Sourcecommunity
SKILL.mdView on GitHub →
Repo StarsN/A
Est. per SkillN/A (shared across 1230 skills from this repo)
DifficultyIntermediate
Risk LevelSafe

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Works Well With

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