Transform Raw Data Into Compelling Visual Stories
Analytics & ReportingintermediateClaude 3.5 Sonnet or GPT-4o. Claude excels at narrative structure and logical flow, making it ideal for crafting the story arc and design rationale. GPT-4o is equally strong and slightly faster for this task. Both handle multi-step reasoning well.
When to Use This Prompt
Use this prompt when you need to present marketing metrics (campaign performance, customer acquisition, engagement trends) to executives or stakeholders and want to ensure the data tells a clear, actionable story rather than just displaying numbers. Essential for board presentations, quarterly reviews, and cross-functional alignment meetings.
The Prompt
You are a data storytelling expert helping marketing leaders communicate insights to executives and stakeholders.
I have the following marketing data that needs to be transformed into a compelling visual story:
## Data Summary
- Metric: [METRIC NAME]
- Time Period: [DATE RANGE]
- Key Numbers: [PROVIDE 3-5 KEY DATA POINTS]
- Data Source: [WHERE THIS DATA COMES FROM]
- Audience: [WHO WILL SEE THIS (e.g., C-suite, board, sales team)]
## Current Challenge
[DESCRIBE WHAT YOU WANT THE AUDIENCE TO UNDERSTAND OR DECIDE]
## Context
- Business Goal: [WHAT ARE WE TRYING TO ACHIEVE]
- Previous Performance: [BASELINE OR HISTORICAL CONTEXT]
- Industry Benchmark: [IF APPLICABLE]
## Your Task
1. Identify the single most important insight from this data
2. Suggest 2-3 specific visualization types that would best communicate this insight
3. For each visualization, explain why it works for this audience
4. Provide a narrative arc: What's the setup, the conflict/insight, and the resolution/action?
5. Recommend color, design, and layout choices that emphasize the key message
6. Suggest 2-3 supporting data points that strengthen the main story
7. Draft a 2-3 sentence verbal narrative to accompany the visual
Focus on clarity over complexity. Assume the audience has 30 seconds to understand the core message.
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Tips for Best Results
- 1.Provide actual numbers, not ranges. 'CAC increased from $45 to $55' is more actionable than 'CAC increased significantly.' Specificity enables better visualization recommendations.
- 2.Always specify your audience's technical literacy. A data analyst needs different visualizations than a CMO—the AI will tailor complexity accordingly.
- 3.Include one sentence about what decision or action this story should drive. This forces the AI to prioritize insights that matter, not just interesting data points.
- 4.Ask for 'one primary insight' first. This prevents the AI from overwhelming you with multiple competing narratives. You can always ask for secondary stories in a follow-up.
Example Output
## Primary Insight
Customer acquisition cost increased 23% YoY while conversion rates dropped 8%, signaling a shift in channel efficiency that requires immediate strategy adjustment.
## Recommended Visualizations
1. **Dual-Axis Line Chart** (Primary Recommendation)
- Left axis: CAC trend over 12 months
- Right axis: Conversion rate trend
- Why it works: Shows the inverse relationship clearly; executives immediately see the correlation problem
2. **Waterfall Chart**
- Breaks down CAC increase by channel (paid search +15%, social +35%, organic -5%)
- Why it works: Pinpoints which channels are driving inefficiency
3. **Scatter Plot with Quadrants**
- X-axis: CAC, Y-axis: Conversion Rate
- Each bubble = channel
- Why it works: Shows which channels are in the "efficient" quadrant vs. problematic
## Narrative Arc
**Setup:** "Our acquisition engine delivered strong volume last year, but efficiency metrics tell a different story."
**Conflict:** "CAC rose 23% while conversions fell 8%—we're paying more to acquire customers who are less likely to convert."
**Resolution:** "By reallocating budget from high-CAC/low-conversion channels to organic and referral programs, we can recover 15% margin by Q3."
## Design Recommendations
- Use red for the CAC line (cost/warning), blue for conversion (positive metric)
- Highlight the intersection point where trends diverged (Month 4)
- Add annotation boxes for key decisions or external factors
- Use white space to avoid overwhelming the executive audience
## Supporting Data Points
- Channel breakdown: Paid search 45% of budget, social 30%, organic 25%
- Conversion rate by stage: Click-to-landing 72%, landing-to-trial 34%, trial-to-paid 18%
- Competitor benchmark: Industry average CAC $45 (ours: $62)
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Courses, workshops, frameworks, daily intelligence, and 6 proprietary tools — built for marketing leaders adopting AI.
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