Research Results: Citation Performance Analysis
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.
Overall Citation Patterns
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.
Engine-Specific Performance
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.
Content Type Analysis
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.
Pillar Performance Analysis
Analysis of individual pillar performance reveals which signals have the strongest impact on citation success:
Highest Impact Pillars
- Pillar 3: Structured Data Implementation - Correlation: 0.89
- Pillar 6: Heading Hierarchy - Correlation: 0.85
- Pillar 11: Author Credentials - Correlation: 0.83
- Pillar 12: Source Attribution - Correlation: 0.81
Moderate Impact Pillars
- Pillar 1: Title Tag Optimization - Correlation: 0.72
- Pillar 4: Publication Date Visibility - Correlation: 0.69
- Pillar 9: Named Entity Recognition - Correlation: 0.67
- Pillar 14: Page Speed Optimization - Correlation: 0.64
Lower Impact Pillars
- Pillar 2: Meta Description Quality - Correlation: 0.58
- Pillar 5: Update Frequency - Correlation: 0.55
- Pillar 15: Mobile Responsiveness - Correlation: 0.52
- Pillar 16: Accessibility Compliance - Correlation: 0.49
Threshold Analysis
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.
Organizational Context
Analysis by organizational type reveals interesting patterns in citation behavior:
- Academic Institutions: Highest average GEO scores (0.81) and citation rates (76%)
- Government Agencies: Strong performance (0.74 average score, 68% citation rate)
- Fortune 500 Companies: Moderate performance (0.67 average score, 54% citation rate)
- Independent Publishers: Variable performance (0.63 average score, 48% citation rate)
- Small Businesses: Lowest performance (0.58 average score, 35% citation rate)
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.
Geographic and Language Factors
Analysis of geographic and language factors reveals some interesting patterns:
- English-language content dominates citations across all engines
- North American sources receive 68% of citations
- European sources receive 23% of citations
- Other regions receive 9% of citations
This geographic bias likely reflects the training data and user base of the analyzed AI engines, rather than inherent quality differences in content.
Implications for Content Strategy
The results provide clear guidance for content optimization efforts:
Priority Optimization Areas
Organizations should focus on the highest-impact pillars first:
- Implement comprehensive structured data
- Optimize heading hierarchy and content structure
- Ensure clear author credentials and source attribution
- Maintain consistent publication dates and update frequency
Content Type Strategies
Different content types require different optimization approaches:
- Technical Documentation: Focus on structure and metadata
- Research Content: Emphasize credentials and citations
- Business Content: Improve entity clarity and freshness
- News Content: Ensure timely publication and source attribution
Validation and Future Research
The results provide strong validation for the GEO-16 framework's predictive power. Future research should focus on:
- Longitudinal analysis of citation performance over time
- Analysis of additional AI engines and content types
- Investigation of geographic and language biases
- Development of industry-specific optimization guidelines