Creating Data Storytelling Posts with AI

Data storytelling posts transform complex information into compelling narratives that inform and inspire action. They combine analytical rigor with narrative techniques to make data meaningful for your audience.

This prompt has been tested and works effectively with leading AI models including ChatGPT, Claude, and Gemini. You can expect consistent results across these LLMs, with each potentially offering slightly different stylistic variations.

Core Prompt Template #

Create a data storytelling post about {DATA_INSIGHT} for {TARGET_AUDIENCE}. Write from my perspective as {YOUR_ROLE}.

Structure to include:
1. Hook that highlights the surprising/important data point
2. Context explaining why this data matters
3. Breakdown of {NUMBER} key insights:
   - What the numbers show
   - Why it's significant
   - Real-world implications
4. Actionable conclusions from the data
5. Forward-looking implications for {INDUSTRY}

Style: {TONE} and data-driven
Length: {LENGTH}

Additional context: {DATA_BACKGROUND}

How to Use This Prompt #

Required Variables #

  • {DATA_INSIGHT}: Main data point or finding to explore
  • {TARGET_AUDIENCE}: Who needs to understand this data
  • {YOUR_ROLE}: Your professional position
  • {NUMBER}: Number of key insights (2-4 recommended)
  • {INDUSTRY}: Relevant industry or sector
  • {TONE}: Clear, engaging, or analytical
  • {LENGTH}: Recommended 400-600 words
  • {DATA_BACKGROUND}: Context about the data source/analysis

Example Inputs #

{
  DATA_INSIGHT: "67% drop in customer acquisition costs",
  TARGET_AUDIENCE: "B2B marketing leaders",
  YOUR_ROLE: "Growth Marketing Director",
  NUMBER: "3",
  INDUSTRY: "SaaS marketing",
  TONE: "analytical but accessible",
  LENGTH: "500 words",
  DATA_BACKGROUND: "Analysis of 50+ B2B SaaS companies over 12 months"
}

Variations #

Data Deep Dive Version #

[Base prompt] + Structure as an in-depth analysis with:
- Executive summary
- Methodology overview
- Detailed findings
- Industry benchmarks
- Strategic implications

LinkedIn Data Story #

[Base prompt] + Format as a LinkedIn post with:
- Strong data hook
- Clear narrative arc
- Visual data points
- Business impact
- Call to action
Maximum 1300 characters

Newsletter Version #

[Base prompt] + Format as a newsletter story with:
- Key statistic headline
- Quick context
- Main insights
- Action items
- Further reading

Tips for Best Results #

Do's #

  • Start with a compelling statistic
  • Provide clear context
  • Use clear comparisons
  • Explain why it matters
  • Include actionable insights

Don'ts #

  • Avoid data overwhelm
  • Don't skip methodology
  • Avoid jargon
  • Don't force conclusions

Example Output #

Here's a sample output using the LinkedIn post format:

📊 The Hidden Story Behind a 67% Drop in CAC

Analyzing 50+ B2B SaaS companies revealed something surprising about customer acquisition costs (CAC) in 2024:

3 Key Insights:

1. Community-First Companies Win
Companies with active communities saw 67% lower CAC
Traditional ads-first companies? Only 12% reduction

2. The Content Multiplier Effect
Each piece of community content drives 3.8x more conversions
Why? 92% higher trust in peer recommendations

3. Scale Economics Reversed
Traditional CAC increases with scale
Community CAC decreases by 23% per thousand members

The Implications:
- Community isn't just about engagement
- It's a fundamental growth lever
- 2024 is the year to pivot

For my fellow B2B marketers: Your Q3 planning should question traditional CAC assumptions.

What's your experience with community-driven growth? 👇

#B2BMarketing #SaaS #GrowthStrategy
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