We just completed our first major original research report. Surveyed 2,000 marketers, got some genuinely interesting findings.
My hypothesis: Original research with proprietary data should get cited more by AI systems because:
What I’m testing: Tracking AI citations before/after publishing the research across relevant queries.
Questions for those who’ve done this:
Kate, we’ve published 6 major research reports over the past 2 years. The AI citation impact is REAL.
Our data:
| Report Type | Sample Size | AI Citations (6 months) | ROI on Production |
|---|---|---|---|
| Industry benchmark | 5,000 | 340+ detected mentions | 12x |
| Trend report | 1,200 | 180+ detected mentions | 8x |
| State of [industry] | 3,500 | 520+ detected mentions | 15x |
| Specific topic survey | 800 | 90+ detected mentions | 5x |
Why original research works for AI:
What we learned:
ROI calculation:
Direct costs:
Tracked returns:
The compounding effect: Our benchmark report from 2024 is STILL being cited in AI answers today. The long tail matters.
We don’t gate the key findings - just the full report. That way AI can cite the published data while we still capture leads.
Journalist perspective on why research gets picked up:
What makes research citable:
What makes research ignorable:
For AI citation specifically:
AI systems pick up what gets covered. If your research gets press coverage, it enters more training data and sources. The research → press → AI pipeline is real.
My advice: Create genuinely interesting research, not just promotional data. Findings that make editors say “huh, interesting” get covered.
I’ve run 50+ research projects for brands. Here’s what I’ve learned about AI-optimized research:
Structural elements that get cited:
Sample size guidance:
| Claim Level | Minimum Sample |
|---|---|
| Industry trends | 500+ |
| Segment comparisons | 200+ per segment |
| Statistical significance | Depends on variance, but 400+ usually |
| Specific audience claims | 100+ in that audience |
Common mistakes:
If you create research and just put it on your site, AI won’t find it. You need distribution.
PR perspective on getting research cited:
The research → citation pipeline:
What gets press coverage:
Distribution strategy:
Budget for promotion. Research without distribution is invisible to AI.
Smaller budget perspective - you don’t need expensive research for AI citations.
Our approach (bootstrapped startup):
Results:
Our “2025 [Niche] Benchmark Report” based on 250 user responses got cited by AI within 8 weeks. It now appears when people ask about benchmarks in our space.
Total cost: Staff time + $0 for survey tools (we used free tier)
The key insight:
You don’t need $50k budgets. You need UNIQUE data. Even a small sample of proprietary data is more citable than no data at all.
Academic/research perspective:
What makes research trustworthy to AI systems:
Why this matters for AI:
AI systems are trained to value authoritative sources. Research that follows academic norms signals authority. It’s more likely to be cited when AI needs to support claims with data.
Practical recommendations:
You don’t need to be academic, but borrowing credibility signals helps.
Format matters as much as content for AI citability:
Research format best practices:
Web version (essential):
PDF version (supplemental):
Key stat format:
Good: “67% of marketing leaders report increasing AI investment in 2026 (Source: [Company] Marketing AI Report 2026, n=2,000)”
Bad: [Infographic with statistic embedded in image]
AI extracts text. Make your best statistics extractable.
I study how AI cites sources. Original research has clear advantages:
Why AI cites research:
When AI answers a question like “What percentage of companies use AI for marketing?” it needs a source. Options:
Original research with specific statistics wins because AI can cite it confidently.
Research characteristics correlated with AI citation:
| Characteristic | Correlation with Citation |
|---|---|
| Specific percentages | 0.72 |
| Clear sample description | 0.65 |
| Methodology section | 0.58 |
| Year-over-year data | 0.54 |
| Third-party coverage | 0.71 |
The third-party coverage multiplier:
Research cited by other publications gets cited even more by AI. The “research → press → AI” pipeline is real. Invest in distribution.
This thread validates our investment in original research. Key takeaways:
Why research works for AI:
Best practices for AI citation:
Our plan for the 2,000-person study:
Budget split:
If this performs like the examples shared (8-15x ROI), it’ll be our best marketing investment this year.
Thanks for the detailed insights everyone.
Get personalized help from our team. We'll respond within 24 hours.
Controlla quando i sistemi AI citano le tue ricerche e dati originali. Scopri quali risultati risuonano e vengono condivisi su ChatGPT, Perplexity e Google AI.
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