LLM-SEO Metrics
Track and optimize for LLM SEO visibility and performance
12 Essential LLM-SEO Metrics
Track how often your brand appears in AI-generated answers
Brand Mentions in AI Responses
Focus: How often your brand appears in AI-generated answers
Otterly, Peec AIAI Model Crawl Success Rate
Focus: How often your content is successfully crawled by AI systems
Server LogsAI Citation Count
Focus: Total number of times AI cites your content as a source
OtterlyEngaged Sessions Metrics
Focus: Quality of visits from AI-referred traffic
Google AnalyticsAttribution Rate in AI Outputs
Focus: How often AI properly attributes your content when citing
AI Visibility ToolsZero-Click Surface Presence
Focus: Appearances in AI Overviews and featured snippets
AI Visibility ToolsMachine-Validated Authority
Focus: How AI systems rate your topical authority
AI Visibility ToolsSentiment in LLM Outputs
Focus: Positive/negative tone when AI mentions your brand
AI Visibility ToolsPrompt-Triggered Visibility
Focus: Which prompts trigger AI to cite your content
Manual TestingVector Index Presence Rate
Focus: How much of your content exists in AI knowledge bases
OtterlyAssisted Conversions
Focus: Conversions where AI touchpoints contributed
Search Console, GA4Brand Search Lift
Focus: Increase in branded searches after AI exposure
Search Console, GA4Frequently Asked Questions
What are LLM-SEO metrics?
LLM-SEO metrics are key performance indicators specifically designed to track your visibility and performance in AI search systems like ChatGPT, Perplexity, Claude, and Google's AI Overviews. Unlike traditional SEO metrics (rankings, traffic, conversions), LLM-SEO metrics measure things like AI citation frequency, brand mention sentiment, prompt-triggered visibility, and AI-referred conversion rates. These metrics help you understand and optimize your presence in the emerging AI search ecosystem.
Which LLM-SEO metric should I track first?
Start with "Brand Mentions in AI Responses"—it's the most fundamental metric. Use tools like Otterly or Peec AI, or run manual prompt tests by asking AI systems questions in your niche. Track how often your brand appears compared to competitors. This baseline reveals your current AI visibility status. Once you understand brand mentions, expand to citation count, sentiment analysis, and prompt-triggered visibility for deeper insights.
How do I track AI model crawl success rate?
Monitor your server logs for AI-specific user agents: ChatGPT-User, PerplexityBot, ClaudeBot, GoogleOther (for AI Overviews). Track how often these bots successfully crawl your pages (200 responses) vs. fail (4xx/5xx errors). High failure rates indicate technical issues blocking AI from accessing your content. Most log analysis tools can filter by user agent—set up dashboards tracking AI bot activity specifically.
What is "prompt-triggered visibility"?
Prompt-triggered visibility measures which specific user prompts cause AI systems to cite your content. For example, you might be cited when users ask "best CRM for small business" but not "CRM software comparison." Understanding which prompts trigger citations helps you optimize content for high-value queries. Track this through manual testing: systematically test variations of prompts in your niche and document which ones cite you.
How do I measure AI-assisted conversions?
AI-assisted conversions require multi-touch attribution. In GA4, set up a custom channel for AI traffic (filter by ChatGPT/Perplexity referrers and AI user agents). Use the "Model Comparison" report to see how AI touchpoints contribute to conversions. Also track "assisted conversions" where AI was part of the journey but not the last click. Monitor brand search lift after AI exposure—users who see your brand in AI responses often convert via branded search later.
What tools can track LLM-SEO metrics?
The LLM-SEO tool landscape is evolving rapidly. Key tools include: Otterly (brand monitoring across AI platforms), Peec AI (AI citation tracking), traditional tools with AI features (Semrush AIO, Ahrefs). For free options: Google Analytics 4 for AI traffic and conversions, Search Console for crawl data, and manual prompt testing for citation tracking. Server log analysis tools can track AI bot behavior. Budget permitting, combine dedicated AI tools with enhanced GA4 tracking.
Why does sentiment in LLM outputs matter?
AI systems don't just cite you—they characterize you. Positive sentiment ("highly recommended," "industry leader") drives clicks and trust. Negative sentiment ("some users report issues," "limited features") harms perception even when you're cited. Track sentiment by systematically testing prompts and categorizing AI responses as positive, neutral, or negative. If you find negative sentiment, identify the source (likely negative reviews or competitor content) and address it with counter-content.
How often should I measure LLM-SEO metrics?
Establish different cadences for different metrics: Weekly—check brand mentions and citation count for major keywords. Monthly—run comprehensive prompt tests across ChatGPT, Perplexity, and Claude; analyze sentiment trends; review AI bot crawl rates. Quarterly—deep dive on attribution and conversion impact; compare performance vs. competitors; adjust strategy based on findings. AI systems update frequently, so monthly monitoring minimum is recommended for active optimization.
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