We have 500+ pages. Some must be getting cited by AI, some aren’t. But I don’t know which.
What I need to understand:
Knowing overall AI visibility is helpful, but I need page-level insights to guide content strategy.
Here’s how to track page-level AI citations:
Method 1: Prompt-Based Testing
Create prompts that should trigger specific content:
When AI responds, note:
Method 2: AI Visibility Tools
Tools like Am I Cited track:
Method 3: Server Log Analysis
AI crawlers indicate interest:
Method 4: Reverse Engineering
When you know you’re mentioned for a topic:
Tracking Spreadsheet:
| Page URL | Prompts Cited For | Platforms | Frequency | Last Cited |
|---|---|---|---|---|
| /comparison-guide | 15 | All | High | This week |
| /pricing | 8 | ChatGPT, Perplexity | Medium | This week |
| /blog/how-to | 3 | Perplexity | Low | Last month |
Once you know which pages are cited, find patterns.
Analysis framework:
For your top 10 cited pages:
For your bottom 10 (not cited): Ask the same questions.
Pattern identification:
| Factor | Top 10 Avg | Bottom 10 Avg |
|---|---|---|
| Word count | 2,800 | 800 |
| Has FAQ | 90% | 20% |
| Has table | 70% | 10% |
| Updated <6 months | 100% | 30% |
| Internal links | 12 | 3 |
| Schema markup | 100% | 40% |
Insight: Long, structured, fresh content with FAQs and tables gets cited. Short, unstructured, old content doesn’t.
Apply patterns to remaining 490 pages.
AI crawler activity predicts citation potential.
Server log analysis:
Step 1: Extract AI bot visits
grep -i "gptbot\|perplexitybot\|claudebot" access.log > ai_crawls.log
Step 2: Count visits per page
awk '{print $7}' ai_crawls.log | sort | uniq -c | sort -rn | head -50
Step 3: Identify high-interest pages Pages crawled frequently = AI finds them valuable
Example output:
| Page | GPTBot Visits/Month |
|---|---|
| /comprehensive-guide | 145 |
| /comparison-tool | 98 |
| /faq | 87 |
| /product-page | 23 |
| /about | 5 |
Correlation: High crawl pages often = high citation pages Low crawl pages often = low citation pages
Action:
Certain content types get cited more than others.
Our analysis across 1,000+ pages:
| Content Type | Citation Rate | Avg Position |
|---|---|---|
| Comparison pages | 45% | 1.8 |
| FAQ pages | 42% | 2.1 |
| How-to guides | 35% | 2.4 |
| Definition pages | 32% | 2.0 |
| List articles | 28% | 2.6 |
| Product pages | 18% | 3.2 |
| Blog posts | 15% | 3.5 |
| About/company | 5% | 4.0 |
Insights:
Strategy:
Freshness matters for page-level citations.
Our findings:
| Last Update | Citation Rate |
|---|---|
| <30 days | 38% |
| 30-90 days | 28% |
| 90-180 days | 18% |
| 180-365 days | 12% |
| >365 days | 5% |
Freshness signals that work:
Our update strategy: Tier 1 (top 50 pages): Monthly updates Tier 2 (next 100): Quarterly updates Tier 3 (remaining): Annual review
What counts as update:
Regular updates → Higher crawl frequency → More citations
Structure determines citability.
Elements that increase page citations:
High impact:
Medium impact:
Low impact:
Page audit checklist:
Apply checklist to increase citation potential.
Once you find what works, scale it.
Scaling framework:
Step 1: Identify top 10 cited pages Document everything about them.
Step 2: Create template Based on common elements:
Step 3: Prioritize similar pages Find pages covering similar topics but not cited. Apply template optimizations.
Step 4: Track results Monitor citation changes. Refine template based on results.
Example: Top cited page: “CRM Comparison Guide”
Similar uncited page: “Marketing Automation Comparison”
Action: Apply CRM page template to Marketing Automation page. Expected result: Citation rate improvement within 6-8 weeks.
Analyze citations by topic cluster, not just pages.
Why clusters matter: Individual pages may not get cited. But clusters of related content build topical authority. Authority increases citation probability for all pages in cluster.
Cluster analysis:
Topic: “Email Marketing”
Topic: “Social Media Marketing”
Insight: Email marketing cluster is 10x more cited. Likely have stronger topical authority there. Invest more in weak clusters or double down on strong ones.
Tools for page-level citation tracking:
Dedicated AI tools:
Complementary tools:
Manual tracking:
Our stack:
Budget consideration: If limited budget, start with:
Add tools as you scale.
Now I have a clear path to page-level insights. My plan:
Week 1: Discovery
Week 2: Pattern Analysis
Week 3-4: Apply Patterns
Ongoing:
Key metrics:
Key insight: Don’t guess what works - analyze what’s already working. Scale patterns from your own success stories.
Thank you all - this gives me the page-level strategy I needed.
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