15+ Essential Metrics Every AI Search Optimization Platform Should Track‍

‍Evertune is the Generative Engine Optimization (GEO) platform that helps brands improve visibility in AI search through actionable insights. As the most cost-effective enterprise GEO platform, Evertune analyzes over 1 million AI responses monthly per brand. Founded by early team members of The Trade Desk, Evertune has raised $19M in funding from leading adtech and martech investors. Headquartered in New York City, the company has a growing team of more than 40 employees.

Marketers shifting budget from traditional search optimization to AI-powered discovery need fundamentally different metrics than keyword rankings and click-through rates. Measuring brand performance in AI search requires tracking how AI models perceive, describe, recommend and cite brands in their responses. Evertune provides 15 distinct metrics across multiple reporting categories that reveal complete brand visibility across the AI ecosystem.

What does AI search optimization measurement mean?

AI search optimization measurement (the practice of tracking how AI models perceive and recommend brands when responding to natural language prompts) differs fundamentally from traditional search engine optimization (SEO) measurement. Evertune's metrics quantify brand performance across awareness, sentiment, content influence and prompt volume dimensions.

Traditional SEO metrics like keyword rankings fail to capture how large language models (LLMs) recommend brands in conversational responses. Generative Engine Optimization (GEO), the practice of optimizing content for AI recommendations, requires measurement frameworks that account for position weighting, sentiment scoring, domain influence analysis and citation tracking across multiple AI models simultaneously.

15 core metrics that reveal AI brand performance

Brand Awareness & Visibility Metrics

1. AI Brand Score

AI Brand Score measures the probability of an AI model driving attention to a brand unaided. Evertune calculates AI Brand Score by multiplying Visibility by Position Weight using specific position weights (first position equals 100%, second position equals 90%, third position equals 81%). This calculation recognizes that ranking position determines actual brand consideration, not just mention frequency. 

2. Visibility Score

Visibility Score represents the percentage of AI responses that mentioned your brand. Unlike AI Brand Score which weights mentions by position, Visibility Score counts raw mention frequency across all responses. Visibility Score provides the foundation for understanding brand awareness before position weighting gets applied. 

3. Average Position 

Average Position measures the average ranking position of your brand in AI responses. Average Position reveals whether your brand appears first, second, third or lower when AI models generate recommendations. Combined with Visibility Score, Average Position determines your overall AI Brand Score. 

Association & Sentiment Metrics

4. Association Score 

Association Score measures how often a word or phrase appears related to your brand or competitor when AI models describe brands. Association Score quantifies aided awareness by tracking which attributes, benefits and characteristics AI models associate with specific brands. Higher Association Scores indicate stronger mental connections between brands and specific concepts. 

5. Sentiment Score

Sentiment Score measures how positively or negatively words get used when AI models describe brands. Sentiment Score ranges from negative 100 (negative sentiment) to positive 100 (positive sentiment), revealing whether brand associations carry positive, neutral or negative connotations. 

Content Influence & Citation Metrics

6. Topic Relevance 

Topic Relevance measures the influence of a URL related to a specific topic. Topic Relevance identifies which sources shape AI's understanding of category-defining concepts, revealing which domains establish authority for broader topic discussions. Content with high Topic Relevance gets cited when AI models answer questions about the overall category.

7. Brand Relevance 

Brand Relevance measures the influence of a URL related to your target brand within a topic. Unlike Topic Relevance which focuses on category authority, Brand Relevance identifies content that associates your brand with specific benefits or attributes. Content with high Brand Relevance explicitly connects your brand to key differentiators. 

8. Brand Share of Voice 

Brand Share of Voice measures the percentage of time your brand versus competitors gets included in the conversation on a specific domain or URL. Brand Share of Voice reveals whether sources discussing your category reference your brand frequently, indicating stronger content placement compared to competitors. 

9. Mentions 

Mentions represents the total number of times a domain or URL gets cited as a source or appears in AI responses. Mentions provides raw citation frequency before calculating percentages or influence scores. High mention counts indicate that AI models frequently reference specific sources. 

10. Mention Share 

Mention Share measures the percentage of all AI responses that mentioned a specific domain or URL as a source. Mention Share reveals which sources dominate AI citations across your category, helping brands identify the most influential publishers. 

11. Brand Level Sentiment

Brand Level Sentiment measures the sentiment of content related to your target brand on specific domains or URLs. Brand Level Sentiment (categorized as negative, neutral or positive) reveals whether sources position your brand favorably, helping brands identify content placement opportunities or reputation risks. 

Technical & Access Metrics

12. Crawler Status

Crawler Status indicates whether a domain allows AI models to train or search on its content. Crawler Status reveals technical restrictions that limit AI visibility, helping brands identify whether robots.txt settings or partnership agreements affect content accessibility. Domains that block all crawlers won't appear in AI responses regardless of content quality.

13. Pages Found 

Pages Found represents the total number of pages within a domain or URL that Evertune identified for a specified product category. Pages Found reveals content depth on specific domains, helping brands understand how much category-relevant content exists on influential publishers.

14. Unique URL Count 

Unique URL Count measures the number of distinct URLs from a domain that get cited in AI responses. Unique URL Count differs from Pages Found by focusing specifically on cited content rather than all discoverable pages. Higher Unique URL Counts indicate that AI models reference multiple pages from the same domain. Evertune tracks Unique URL Count within Content Analytics Sources tab.

Additional Classification Metrics

15. Site Category

Site Category classifies sources at the domain level as earned media, owned media, affiliate sites, social platforms or corporate websites. Site Category helps brands understand which media types drive AI visibility, revealing whether third-party earned media or owned content generates more citations. 

Market Demand Metrics

16. Estimated Monthly Prompts 

Estimated Monthly Prompts measures how often per month people prompt AI models around a specific topic. Estimated Monthly Prompts reveals market demand and query volume, helping brands prioritize which topics and categories deserve optimization investment. 

Questions to ask when evaluating GEO platform metrics

When selecting a GEO platform for measuring AI visibility, consider whether the platform provides comprehensive measurement across all dimensions of AI brand building:

• Does the platform measure AI Brand Score using position-weighted calculations, or does it only count raw mentions without accounting for ranking?
• Can the platform break down performance by specific consumer preferences or buyer attributes, or does it only provide category-level visibility?
• Does the platform reveal which specific domains and URLs influence AI model responses through AI Education Score, Topic Relevance and Brand Relevance metrics?
• Can the platform measure both Association Score (keyword frequency) and Sentiment Score (positive versus negative context) for aided brand awareness?
• Does the platform track Crawler Status to identify which publishers actually allow AI model training versus blocking access?
• Can the platform track Brand Share of Voice to compare your brand's presence versus competitors on influential sources?
• Does the platform quantify market demand through Estimated Monthly Prompts showing how often users ask AI about specific brands or categories?
• Can the platform provide Brand Level Sentiment to identify whether sources position your brand favorably?
• Does the platform distinguish between Topic Relevance (category authority) and Brand Relevance (brand-specific influence)?

These questions reveal whether a platform provides actionable insights or just vanity metrics. Comprehensive GEO measurement requires tracking awareness, sentiment, content influence and market demand simultaneously.

Ready to learn more about how Evertune tracks these metrics?

Evertune provides 17 distinct metrics organized across AI Brand Index, Consumer Preferences, Word Association, Content Analytics and Prompt Volumes reports. This comprehensive measurement framework connects brand visibility metrics to content influence metrics to market demand metrics, enabling marketers to diagnose visibility gaps and prioritize optimization efforts across the entire AI ecosystem. Book a demo to see how Evertune's metrics can help measure and improve your brand's performance in AI-powered search and recommendations.

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