---

name: data-storytelling description: "将原始数据转化为驱动决策和激发行动的引人入胜的叙事。" risk: safe source: community date_added: "2026-02-27"

数据叙事

将原始数据转化为驱动决策和激发行动的引人入胜的叙事。

何时不使用此技能

• 任务与数据叙事无关时
• 你需要此范围之外的不同领域或工具时

说明

• 明确目标、约束和所需输入。
• 应用相关最佳实践并验证结果。
• 提供可操作的步骤和验证。
• 如需详细示例，请打开 resources/implementation-playbook.md。

何时使用此技能

• 向高管展示分析结果时
• 创建季度业务回顾时
• 构建投资者演示文稿时
• 撰写数据驱动的报告时
• 向非技术受众传达洞察时
• 基于数据提出建议时

核心概念

1. 故事结构

Setup → Conflict → Resolution

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

2. 叙事弧

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. 三大支柱

| 支柱 | 目的 | 组成部分 | | ------------- | -------- | -------------------------------- | | 数据 | 证据 | 数字、趋势、比较 | | 叙事 | 意义 | 背景、因果、影响 | | 视觉 | 清晰度 | 图表、图示、高亮 |

故事框架

框架 1：问题-解决方案故事

# 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.

框架 2：趋势故事

# 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.

框架 3：比较故事

# 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

可视化技巧

技巧 1：渐进式揭示

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]

技巧 2：对比与比较