We have 800+ pages. I analyzed which get cited by AI.
The surprising findings:
| Page Type | # of Pages | Avg Citations/Month | Citation Rate |
|---|---|---|---|
| Definition pages | 45 | 8.2 | 42% |
| Comparison pages | 28 | 7.1 | 38% |
| Research reports | 12 | 11.4 | 52% |
| How-to guides | 120 | 2.8 | 18% |
| Product pages | 200 | 0.4 | 3% |
| Blog posts | 380 | 1.2 | 8% |
The pattern:
Our research reports (only 12 pages, 1.5% of content) drive 14% of our AI citations.
Our product pages (200 pages, 25% of content) drive only 1% of AI citations.
Questions:
Your pattern matches what I see across clients. Here’s why.
What AI needs to cite:
| Need | Page Type That Satisfies |
|---|---|
| Define something | Definition pages |
| Compare options | Comparison pages |
| Provide data/facts | Research reports |
| Explain how | How-to guides |
| Recommend products | Review/comparison content |
What AI rarely needs:
| Need | Why Product Pages Fail |
|---|---|
| Specific product info | Goes directly to source |
| Promotional content | No citation value |
| Generic descriptions | Not unique |
The formula:
Citation Value = (Unique Information) x (Query Match) x (Authority)
Your research ROI:
12 pages driving 14% of citations = 116x efficiency vs product pages.
Product pages CAN get cited if restructured:
Traditional product page:
Citation rate: 3%
AI-optimized product page:
Citation rate: 18%
The difference:
Traditional: “This is our product” Optimized: “This is how our product solves [problem]”
What we added:
Product pages can work, but they need informational value.
The research ROI is real:
Our analysis:
| Content Type | Creation Cost | Lifetime Citations | Cost per Citation |
|---|---|---|---|
| Blog post | $500 | 15 | $33 |
| Definition page | $800 | 45 | $18 |
| Comparison page | $1,200 | 52 | $23 |
| Research report | $5,000 | 180 | $28 |
Research costs more but:
Our shift:
Before: 80% blog posts, 5% research After: 50% blog posts, 25% research
The result:
Total citations up 180% with same content budget.
The traffic vs citation disconnect:
Our data:
| Page | Monthly Traffic | Monthly AI Citations |
|---|---|---|
| General industry guide | 12,000 | 8 |
| Niche technical doc | 400 | 24 |
| Popular blog post | 8,500 | 3 |
| Obscure data page | 150 | 31 |
The insight:
Traffic measures: How many people find you via search Citations measure: How valuable your content is to AI
Different metrics, different optimization.
Our niche technical documentation gets 3% of traffic but 22% of citations.
The strategy implication:
If AI visibility is your goal, optimize for citation value, not traffic volume.
Sometimes your lowest-traffic pages are your highest-value AI assets.
Page structure predicts citations:
We analyzed 200 pages:
| Structure Element | Citation Impact |
|---|---|
| Clear H1 answering query | +45% |
| FAQ section | +38% |
| Data tables | +32% |
| Bullet point summaries | +28% |
| Expert author attribution | +25% |
| Recent update date | +22% |
The anti-patterns:
| Structure Issue | Citation Impact |
|---|---|
| Answer buried in content | -40% |
| No clear sections | -35% |
| Wall of text | -30% |
| Generic intro | -25% |
The checklist:
For any page you want AI to cite:
Great insights. Here’s my content prioritization framework:
The AI Citation Priority Matrix:
| Priority | Content Type | Investment | Expected Citation Rate |
|---|---|---|---|
| 1 | Original research | High | 45-55% |
| 2 | Definition pages | Medium | 35-45% |
| 3 | Comparison content | Medium | 30-40% |
| 4 | How-to guides | Medium | 15-25% |
| 5 | Blog posts | Low-Medium | 5-15% |
| 6 | Product pages* | Low | 3-8% |
*Unless restructured for informational value
Our content investment shift:
| Type | Before | After |
|---|---|---|
| Research | 5% | 25% |
| Definitions | 5% | 15% |
| Comparisons | 10% | 15% |
| How-to | 30% | 20% |
| Blog posts | 40% | 15% |
| Product | 10% | 10% |
Key structural elements:
Every high-citation page has:
Monitor with Am I Cited to see what’s actually working.
Thanks everyone for validating the patterns!
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