You get:
- “Studies show…” (no citation, no credibility)
- generic statistics without context
- no original analysis (just repackaged data)
- opinions presented as facts
- posts that don’t stand up to scrutiny
But research-based content is not about dumping data.
It is about drawing insights that others miss.
- Lead with a surprising finding
- Cite specific sources (not “studies show”)
- Explain what the data means (interpretation)
- Connect findings to practical action
- Add your own analysis (don’t just quote)
Without evidence, your claims are opinions.
This framework forces AI to write data-backed posts that build authority.
Assume the role of a data-driven content writer who backs every claim with evidence. Your task is to write a research-based blog post. Generate: 1. HEADLINE (data-driven or surprising finding) 2. KEY STATISTICS HIGHLIGHT BOX - 3-5 most surprising stats from the research 3. FULL POST (1,200-1,800 words) - Opening with a surprising data point - Context for each statistic (what it means) - Analysis (your interpretation) - Practical implications (what to do) 4. SOURCES CITED (list) 5. KEY TAKEAWAY (one sentence) INPUTS: Research Topic: [WHAT ARE YOU INVESTIGATING?] Key Data Points (3-5, with sources): [LIST STATS + SOURCES] The Surprising Finding: [WHAT GOES AGAINST CONVENTIONAL WISDOM?] Target Audience: [WHO NEEDS TO KNOW THIS?] Practical Application: [HOW SHOULD READERS USE THIS DATA?] RULES: - Every claim must have a source (no "studies show" without citation) - Lead with the most surprising finding - Explain what the data means (don't just quote numbers) - Include your own analysis (not just data dump) - Connect data to practical action - Use data visualization descriptions (charts, graphs)
- Original data (your own surveys) is more valuable than citing others.
- Visualize data with charts or graphs (described or embedded).
- Cite specific, credible sources (not “according to experts”).
- The surprising finding is what gets shared — lead with it.
- Update research posts annually with new data.
Research Topic: Email marketing effectiveness for small businesses in 2024
Key Data Points: “Email ROI averages $36 for every $1 spent (Litmus, 2024)”; “Only 20% of small businesses use email segmentation (HubSpot, 2024)”; “Segmented campaigns drive 30% more opens and 50% more clicks (Mailchimp, 2024)”
The Surprising Finding: Most small businesses know email is effective, but very few use the tactics that actually drive results
Target Audience: Small business owners who send email but aren’t seeing results
Practical Application: Start with basic segmentation (by interest or purchase history) to see immediate lifts in open and click rates
This framework improves outcomes by forcing:
- cited sources (credibility)
- data interpretation (not just numbers)
- surprising findings (shareability)
- analysis (value beyond the data)
- practical action (utility)
Great research-based content doesn’t just report data — it reveals insights competitors miss.
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