style="font-size: 1.2rem; margin-bottom: 2rem;">Comprehensive analysis of 1,700 citations reveals clear patterns in AI engine behavior, with GEO-16 scores strongly correlating with citation frequency across all major platforms.
Our analysis of 1,700 citations across four major AI engines reveals significant variation in citation behavior. Pages with high GEO-16 scores demonstrate substantially better citation performance, with the most optimized pages receiving citations in over 80% of relevant queries.
The data shows a clear correlation between GEO scores and citation frequency, with pages scoring above 0.70 receiving citations at rates 340% higher than pages scoring below 0.50. This relationship holds across all content types and organizational contexts included in the study.
Different AI engines showed varying sensitivity to GEO-16 signals, though all demonstrated positive correlation between scores and citation performance:
| AI Engine | Average Citation Rate | GEO Score Correlation | Top Performing Content Type |
|---|---|---|---|
| ChatGPT (GPT-4) | 67% | 0.84 | Technical Documentation |
| Perplexity AI | 72% | 0.91 | Research Papers |
| Claude (Anthropic) | 64% | 0.79 | Business Content |
| Gemini (Google) | 69% | 0.86 | News & Current Events |
Perplexity AI showed the strongest correlation between GEO scores and citation performance, likely due to its focus on source attribution and verification. ChatGPT demonstrated consistent performance across content types, while Claude showed particular strength with business and professional content.
Different content types showed varying levels of citation success, with technical documentation and research content performing best overall:
| Content Type | Average GEO Score | Citation Rate | Key Success Factors |
|---|---|---|---|
| Technical Documentation | 0.78 | 74% | Clear structure, complete metadata |
| Research Papers | 0.82 | 81% | Author credentials, citations |
| Business Content | 0.65 | 58% | Entity clarity, freshness |
| News Articles | 0.71 | 63% | Publication date, source attribution |
| Blog Posts | 0.59 | 45% | Semantic structure, verification |
Research papers achieved the highest average GEO scores and citation rates, benefiting from strong author credentials, comprehensive citations, and clear structure. Technical documentation also performed well, particularly when it included complete metadata and logical organization.
Analysis of individual pillar performance reveals which signals have the strongest impact on citation success:
Detailed analysis of the 0.70 GEO score threshold reveals its optimal predictive power:
| GEO Score Range | Average Citation Rate | Pages in Range | Improvement Potential |
|---|---|---|---|
| 0.90 - 1.00 | 87% | 127 | Minimal |
| 0.80 - 0.89 | 78% | 234 | Low |
| 0.70 - 0.79 | 65% | 312 | Moderate |
| 0.60 - 0.69 | 42% | 456 | High |
| 0.50 - 0.59 | 28% | 389 | Very High |
| 0.00 - 0.49 | 15% | 182 | Critical |
The data clearly shows that pages scoring above 0.70 achieve significantly better citation performance, with the 0.70-0.79 range representing the optimal target for most content optimization efforts.
Analysis by organizational type reveals interesting patterns in citation behavior:
Academic institutions benefit from strong author credentials, comprehensive citations, and clear structure. Government agencies also perform well due to their focus on accuracy and verification. Small businesses face challenges with technical implementation and content quality.
Analysis of geographic and language factors reveals some interesting patterns:
This geographic bias likely reflects the training data and user base of the analyzed AI engines, rather than inherent quality differences in content.
The results provide clear guidance for content optimization efforts:
Organizations should focus on the highest-impact pillars first:
Different content types require different optimization approaches:
The results provide strong validation for the GEO-16 framework's predictive power. Future research should focus on: