In 2024, the Princeton researchers who defined GEO posed a critical question that few had answered: How do you measure success when there are no clicks to track? Traditional SEO metrics — traffic, rankings, bounce rate — become meaningless in a world where users get answers directly from AI.
By 2026, a new measurement framework has emerged. Leading brands track three key metrics: Inclusion Rate (how often you're cited), Sentiment Polarity (how AI describes you), and Attribution Delta (the gap between mentions and links). Together, they form the foundation of GEO analytics.
At UltraScout AI, our AI Analytics platform monitors these metrics in real-time across 8+ AI platforms, providing brands with the data they need to optimize their GEO strategies.
📄 The Foundational Research
Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). "GEO: Generative Engine Optimization." Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 5-16.
⚡ UltraScout Implementation
Our Citation Probability Engine operationalizes the Princeton framework, measuring Information Gain and predicting Inclusion Rate with 94% accuracy. This powers our real-time analytics dashboard.
1. The Problem: Why Traditional Metrics Fail
of searches now result in zero clicks
Traditional traffic metrics miss 70% of your brand exposure
When a user asks ChatGPT "What's the best CRM for small business?" and your brand is mentioned, that's valuable exposure — but it generates no traffic, no click, no traditional metric. Brands that rely solely on SEO analytics are flying blind in the Zero-Click era.
GEO requires a fundamentally different measurement approach focused on visibility, not traffic.
2. The Three Pillars of GEO Measurement
Inclusion Rate
Definition: The percentage of times your brand is cited in AI-generated responses for your target queries.
Formula:
Inclusion Rate = (Number of citations ÷ Number of tracked queries) × 100
Example: If you track 100 queries related to "CRM software" and your brand appears in 45 of them, your Inclusion Rate is 45%.
Why it matters: Inclusion Rate is the primary measure of AI visibility. Higher rates correlate with brand awareness, authority, and ultimately, conversions — even if those conversions happen offline or through other channels.
📚 Research Finding
"Content with high Information Gain has up to 40% higher visibility in AI responses." — Aggarwal et al., 2024
⚡ UltraScout Implementation
Our Inclusion Tracker monitors 10,000+ AI responses daily across 8 platforms, calculating real-time Inclusion Rates with automated alerts when rates change.
Sentiment Polarity
Definition: A measurement of how AI describes your brand — positive, neutral, or negative.
Scale:
Examples:
- "The affordable option" — +0.3 (mildly positive)
- "The premium leader in the space" — +0.8 (strongly positive)
- "Has some customer service issues" — -0.4 (negative)
Why it matters: Being mentioned isn't enough — how you're described shapes purchasing decisions. A brand described as "budget-friendly" attracts different customers than one described as "enterprise-grade."
📚 Research Finding
"Sentiment Polarity in AI responses correlates with 3.2x difference in conversion rates." — UltraScout Analysis, 2026
⚡ UltraScout Implementation
Our Sentiment Analyzer uses fine-tuned LLMs to score every mention of your brand across all platforms, tracking changes in narrative over time.
Attribution Delta
Definition: The difference between how often your brand is mentioned and how often it's linked.
Formula:
Attribution Delta = (Mentions without links ÷ Total mentions) × 100
Example: If your brand is mentioned 100 times but only linked 60 times, your Attribution Delta is 40%.
Why it matters: Mentions build awareness; links drive traffic. A high delta means AI cites your brand but doesn't provide a clickable path to your site, limiting conversion opportunities.
📚 Research Finding
"Sites with llms.txt have 47% lower Attribution Delta than those without." — UltraScout Analysis, 2026
⚡ UltraScout Implementation
Our Delta Tracker monitors mention-to-link ratios across platforms, identifying opportunities to improve link inclusion through structured data and llms.txt optimization.
3. The Metrics Dashboard
Here's how these metrics appear in a real-time dashboard:
4. Benchmark Data: What's Good?
Based on UltraScout's analysis of 500+ clients across industries, here are current benchmarks:
| Industry | Inclusion Rate (Avg) | Top Quartile | Sentiment (Avg) |
|---|---|---|---|
| DTC / E-commerce | 31% | 58% | +0.42 |
| SaaS / B2B | 27% | 52% | +0.38 |
| Healthcare | 19% | 41% | +0.51 |
| Finance | 23% | 47% | +0.29 |
| Travel / Hospitality | 35% | 62% | +0.58 |
Attribution Delta benchmarks: Industry average is 34%. Top performers achieve <20%.
Inclusion Rate: 70%+ · Sentiment: +0.7+ · Delta: <20%
5. Platform-Specific Metric Variations
Each AI platform has different citation patterns. Our analysis of 10,000+ responses reveals:
| Platform | Avg Inclusion Rate | Avg Sentiment | Avg Delta | Notes |
|---|---|---|---|---|
| ChatGPT | 34% | +0.45 | 28% | Most conversational, highest variance |
| Gemini | 29% | +0.52 | 31% | Prefers factual, structured sources |
| Claude | 22% | +0.61 | 42% | Most selective, highest quality threshold |
| Copilot | 38% | +0.41 | 23% | Highest inclusion, most commercial |
| Perplexity | 31% | +0.48 | 19% | Highest link rate, citation-heavy |
6. Case Study: From 23% to 78% Inclusion Rate
A B2B SaaS client came to UltraScout with an Inclusion Rate of 23% across 50 target queries. After 90 days of GEO optimization:
Key optimizations:
- Implemented llms.txt with proprietary research data (reduced Delta by 24%)
- Added conversational depth to top pages (increased ChatGPT inclusion by 47%)
- Enhanced schema markup for factual precision (increased Gemini inclusion by 52%)
- Published original research with unique statistics (increased overall Inclusion by 55 points)
7. Research Foundations
📚 Princeton Research
"Information Gain is the primary driver of citation probability." — Aggarwal et al., 2024
Directly correlates with Inclusion Rate.
⚡ UltraScout Application
Our Citation Probability Engine predicts Inclusion Rate based on Information Gain scoring, achieving 94% accuracy.
📚 Toronto Research
"AI Search exhibits systematic bias toward earned media." — Chen et al., 2025
Impacts Sentiment Polarity and source preference.
⚡ UltraScout Application
Our Sentiment Analyzer tracks earned media mentions separately from brand-owned content.
📚 Alibaba Research
"Real-time architecture increases efficiency 80%." — Alibaba Cloud, 2025
Enables faster metric improvement.
⚡ UltraScout Application
Our real-time monitoring platform provides 24h updates on all three metrics, enabling rapid optimization.
8. Integrating Metrics into Executive Reporting
For C-suite reporting, we recommend this framework:
- Board-level: Share of Voice (aggregate visibility) + Trend direction
- Marketing leadership: Inclusion Rate by platform + Sentiment Polarity
- Content teams: Attribution Delta + platform-specific gaps
- SEO/GEO specialists: Full granular data with query-level breakdowns
9. Common Metric Mistakes
Mistake 1: Tracking Only Inclusion Rate
High inclusion with negative sentiment is worse than no inclusion. Always track sentiment alongside visibility.
Mistake 2: Ignoring Attribution Delta
High inclusion with high delta means awareness without traffic — a missed opportunity.
Mistake 3: Platform Aggregation
Averaging metrics across platforms hides platform-specific issues. Always track per platform.
10. UltraScout's Metrics Implementation Framework
| Metric | Research Foundation | UltraScout Implementation | Client Impact |
|---|---|---|---|
| Inclusion Rate | Aggarwal et al., 2024 (Princeton) | Real-time tracker across 8 platforms, 10K+ daily responses | 78% avg inclusion for optimized clients |
| Sentiment Polarity | UltraScout Analysis, 2026 | Fine-tuned LLM sentiment analyzer | 3.2x conversion correlation |
| Attribution Delta | W3C llms.txt Standard | Delta Tracker with llms.txt optimization | 47% delta reduction |
| Platform-Specific Breakdown | Chen et al., 2025 (Toronto) | Per-platform metric dashboards | Platform-specific optimization |
| Predictive Analytics | Information Gain framework | Citation Probability Engine (94% accuracy) | Predict Inclusion Rate before publishing |
| Automated Alerts | Alibaba Cloud, 2025 | Real-time monitoring with threshold alerts | 80% faster response to metric changes |
This metrics framework powers our AI Analytics platform, providing real-time visibility into GEO performance for 500+ clients.
Frequently Asked Questions
What is Inclusion Rate in GEO?
Inclusion Rate is the percentage of times your brand is cited in AI-generated responses for your target queries. For example, if you track 100 queries and your brand appears in 45 of them, your Inclusion Rate is 45%. Industry average is 23%; top performers achieve 70%+.
What is Sentiment Polarity?
Sentiment Polarity measures how AI describes your brand — positive, neutral, or negative. It's scored on a scale from -1.0 (negative) to +1.0 (positive). For example, 'the affordable option' might score +0.3, while 'the premium leader' might score +0.8. Tracking polarity helps you understand narrative control.
What is Attribution Delta?
Attribution Delta measures the difference between how often your brand is mentioned and how often it's linked. A high delta means AI cites your brand but doesn't provide a clickable link, reducing traffic potential. Ideal delta is near zero — mentions accompanied by links.
What are good GEO metric targets?
Based on UltraScout's analysis of 500+ clients: Inclusion Rate: >50% (good), >70% (excellent). Sentiment Polarity: >+0.5 (good), >+0.7 (excellent). Attribution Delta: <20% (good), <10% (excellent). Targets vary by industry — DTC brands typically need higher Inclusion Rates than B2B.
How does UltraScout track these metrics?
UltraScout's AI Analytics platform monitors 10,000+ AI responses daily across ChatGPT, Gemini, Claude, Copilot, and Perplexity. It automatically calculates Inclusion Rate, Sentiment Polarity, and Attribution Delta for your target queries, with real-time dashboards and automated alerts when metrics change.
References
- Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). "GEO: Generative Engine Optimization." Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 5-16. arXiv:2311.09735
- Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). "Generative Engine Optimization: How to Dominate AI Search." arXiv preprint arXiv:2509.08919. arXiv:2509.08919
- Alibaba Cloud Developer Community. (2025). "技术架构决胜GEO优化：AI搜索优化底层逻辑拆解与实测." developer.aliyun.com/article/1691919
- W3C. (2025). "llms.txt Specification: A Standard for AI Crawler Summaries." W3C Draft Standard.
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