The Complete Guide to GEO Metrics

Your competitor just got recommended by ChatGPT. You didn't.

Someone asked "best [your category] tool" and the AI listed three brands. Yours wasn't one of them. This happened today. It will happen tomorrow. And you have no idea how often it's happening-or why.

Traditional analytics won't help. Google Analytics tracks page views and clicks. But when ChatGPT recommends your competitor, there's no click to track. The user got their answer. They never visited your site. They never knew you existed.

GEO requires its own metrics. This guide covers the essential measurements for understanding and improving your brand's AI visibility-including benchmarks from analyzing 500+ brands across 12 industries.

Why Traditional Metrics Fall Short

When someone asks ChatGPT "best project management tool," there's no ranking page. There's an answer that either mentions you or doesn't. The question isn't where you rank-it's whether you're mentioned at all.

The Core GEO Metrics

1. Brand Mention Visibility

What it measures: How often your brand appears in AI responses to relevant queries.

Why it matters: Visibility is the foundation metric. Everything else-position, sentiment, citations-only matters if you're visible in the first place.

What to track:

• Overall visibility across all providers
• Provider-specific visibility (ChatGPT vs Claude vs Perplexity)
• Topic-specific visibility (by query category)
• Trend over time (weekly/monthly changes)
• Position within responses (first mention vs later)

2. Share of Voice

What it measures: Your brand's mention frequency relative to competitors across all tracked queries.

Why it matters: Visibility in isolation doesn't tell you if you're winning. Share of voice reveals your competitive position.

How it works:

• If AI mentions 5 project management tools across 100 queries
• And your brand appears in 25 of those responses
• While your top competitor appears in 40
• Your share of voice is lower-you're losing the AI recommendation battle

What to track:

• Overall share of voice vs competitor set
• Share of voice by topic/query category
• Share of voice by provider
• Trend over time

3. Source Citation Visibility

What it measures: How often AI platforms cite your content as a source.

Why it matters: There's a crucial difference between being mentioned and being cited. Mentions mean AI knows about you. Citations mean AI trusts your content enough to reference it as a source.

The distinction:

What to track:

• Citation frequency across providers
• Which pages/content gets cited most
• Citation position (earlier citations carry more weight)
• Comparison: your citations vs competitor citations

4. Sentiment

What it measures: Whether AI describes your brand positively, negatively, or neutrally.

Why it matters: Being mentioned doesn't help if AI says "X is known for poor customer service." Sentiment determines whether visibility helps or hurts.

Sentiment categories:

• Positive: Favorable language, recommendations, praise
• Neutral: Factual mentions without emotional valence
• Negative: Critical language, warnings, unfavorable comparisons

What to track:

• Overall sentiment score
• Sentiment by provider (Claude may describe you differently than ChatGPT)
• Sentiment by topic (positive for "features" but negative for "pricing")
• Sentiment trends over time

5. Relevance

What it measures: The contextual quality and appropriateness of your mentions.

Why it matters: Not all mentions are equal. Being mentioned for the wrong reasons or in irrelevant contexts can dilute your positioning.

Examples:

What to track:

• Relevance score per mention
• Alignment between query intent and mention context
• Topics where relevance is high vs low

6. Question Coverage

What it measures: How comprehensively you appear across the range of relevant queries.

Why it matters: High visibility on 10% of queries isn't as valuable as moderate visibility across all relevant queries. Coverage reveals gaps.

Coverage analysis example:

This reveals a pricing content gap to address.

What to track:

• Coverage by query category/topic
• Coverage by funnel stage (awareness vs consideration vs decision)
• Gaps where competitors dominate

Industry Benchmarks

What does "good" look like? Based on our analysis of 500+ brands across B2B SaaS, e-commerce, fintech, and other verticals, here's what separates leaders from laggards:

Visibility Benchmarks

Key finding: The top 10% of brands in any category capture 60% of total AI mentions. The gap between #1 and #5 is often larger than the gap between #5 and #50.

Share of Voice Benchmarks

Key finding: In 73% of categories analyzed, the brand with highest share of voice also had the highest citation rate. Authority compounds.

Sentiment Benchmarks

Key finding: Sentiment varies significantly by provider. Claude tends to be more neutral (avg 0.25), while ChatGPT shows stronger sentiment variance (avg 0.38 with higher standard deviation).

Citation Benchmarks

Key finding: Brands with structured, data-rich content (statistics, original research, clear definitions) are cited 3.2x more often than brands with purely promotional content.

Brand Gap Benchmarks

Provider-Specific Considerations

Different AI platforms behave differently. Track metrics by provider to understand platform-specific performance.

ChatGPT

• Increasingly uses live web search
• Citations becoming more common
• Longer responses often include more brands
• High user volume makes visibility here critical

Claude

• Relies heavily on training data
• Newer content may not appear immediately
• Prioritizes accuracy over recency
• Different citation behavior than ChatGPT

Perplexity

• Always cites sources
• Multiple sources per response
• Earlier citations carry more weight
• Strong emphasis on authoritative sources

Google AI Overviews

• Integrated with traditional search
• Impacts organic CTR significantly
• Different optimization signals than pure LLMs

For platform-specific optimization, see our guides on ChatGPT optimization and Perplexity optimization.

Competitive Analysis

GEO isn't just about your performance-it's about relative performance against competitors.

Head-to-Head Comparison

For each query where you're not visible:

• Which competitors appear?
• At what position?
• What sources are cited?
• What's their sentiment?

This reveals exactly what you need to do to win.

Competitive Gap Analysis

This tells you where you win, where you lose, and where you're invisible.

Visibility Gaps

Beyond comparing positions, gap analysis quantifies exactly how far behind you are:

Brand Gap (0-100%) measures how often competitors appear in AI responses without your brand being mentioned.

Source Gap (0-100%) measures how often competitor sources are cited vs yours.

A high Brand Gap + high Source Gap means you're invisible AND competitors have authoritative content. These are your highest-priority opportunities.

A low Brand Gap + high Source Gap means you appear in responses but aren't cited as a source. You need to create content worth citing.

Advanced Metrics: Deeper Insights

Beyond core visibility metrics, advanced analysis reveals how and why AI platforms perceive your brand the way they do.

Brand Perception Analysis

AI platforms don't just mention your brand-they describe it. Brand Perception measures alignment between what you want AI to say and what it actually says.

How it works:

1. Define 5 key brand attributes (e.g., "enterprise-grade security," "24/7 support," "affordable pricing")
2. Query AI platforms about your brand
3. Measure how often each attribute appears in responses
4. Calculate attribute alignment percentage

Attribute alignment benchmarks:

Key finding: Brands with 70%+ attribute alignment have 2.4x higher conversion rates from AI-referred traffic. When AI accurately describes your value proposition, users arrive pre-qualified.

Sentiment Trend Analysis

A single sentiment score is a snapshot. Trends reveal trajectory.

What to watch:

• Sudden drops: Often correlate with negative press, product issues, or competitor attacks
• Gradual improvement: Indicates successful content and PR efforts
• Provider divergence: When Claude's sentiment differs significantly from ChatGPT's, investigate which sources each relies on

Response patterns:

• Sentiment shifts typically lag real-world events by 2-4 weeks on ChatGPT (web search dependent)
• Claude's sentiment is more stable but slower to update (training data dependent)
• Perplexity reflects real-time source sentiment most accurately

Social Signal Impact

AI platforms increasingly incorporate social signals-Reddit discussions, Twitter mentions, community forums-into their responses.

Key finding: In our analysis, 34% of negative sentiment instances traced back to social media sources, particularly Reddit threads ranking highly for "[brand] review" or "[brand] problems" queries.

What to monitor:

• Reddit threads mentioning your brand (especially in subreddits AI frequently cites)
• Review aggregator sentiment (G2, Capterra, Trustpilot)
• Community forum discussions
• Social proof signals that AI might surface

Response Stability

AI responses aren't deterministic. Ask the same question twice, you might get different brand recommendations. Stability measures how consistently you appear.

How it works:

• Query the same prompt multiple times across sessions
• Calculate Jaccard similarity of brand mentions across responses
• Higher similarity = more stable presence

Stability benchmarks:

Why it matters:

Key finding: Brands with >70% stability convert AI-referred traffic 1.8x better. Users who see you recommended consistently develop stronger brand recall.

Position Within Responses

Being mentioned isn't enough - where you're mentioned matters.

Position scoring:

• Position 1: First brand mentioned-highest recall and click probability
• Position 2-3: Strong presence, often considered alongside leader
• Position 4+: Included but not top-of-mind
• "Also mentioned": Afterthought positioning-minimal impact

Key finding: Position 1 captures 45% of user attention. Position 2 captures 25%. Positions 3-5 split the remaining 30%. Being mentioned 5th is worth roughly 1/7th of being mentioned first.

Position varies by query type:

• Comparison queries ("X vs Y") have more balanced position distribution
• Recommendation queries ("best tool for...") heavily favor position 1-2
• Educational queries may not have brand positioning at all

From Metrics to Action

Metrics are only valuable if they drive action. Here's the playbook for translating GEO data into optimization priorities:

Low Visibility (<30%)

Diagnosis: AI doesn't know you exist or doesn't consider you relevant to the queries being tracked.

Root causes:

• Insufficient brand mentions in AI training sources
• Weak presence on sites AI trusts (Wikipedia, industry publications, review platforms)
• Content doesn't match query intent

Action plan:

1. Audit your source presence: Are you mentioned on Wikipedia, industry publications, and major review sites?
2. Create definitional content: Glossaries, "what is X" guides, and educational content that AI references for context
3. Build brand mentions: Guest posts, podcast appearances, industry report inclusions, expert quotes
4. Optimize for query intent: Ensure your content directly answers the questions being asked

Expected timeline: 4-8 weeks to see initial visibility improvements on web-search-enabled AI; 2-4 months for training-data-dependent platforms.

Poor Share of Voice (Below competitor average)

Diagnosis: You're visible, but competitors dominate the conversation.

Root causes:

• Competitors have stronger authority signals
• Competitors cover more query variations
• Competitors have more/better citations

Action plan:

1. Identify dominant competitors: Which brands appear when you don't? What sources do they have that you lack?
2. Match their coverage: Create content for query categories where they appear and you don't
3. Outperform on depth: Create more comprehensive content on topics where you both appear
4. Build unique authority: Original research, proprietary data, expert perspectives competitors can't replicate

Low Citation Rate (<10%)

Diagnosis: AI mentions you but doesn't cite your content as a source.

Root causes:

• Content not structured for AI extraction
• Lack of unique data or insights
• Poor technical SEO fundamentals

Action plan:

1. Structure content for extraction: Clear headings, bulleted lists, definition boxes, stat callouts
2. Add citable elements: Original statistics, research findings, expert quotes with attribution
3. Create resource pages: Comprehensive guides that serve as reference material
4. Technical optimization: Fast loading, mobile-friendly, clean markup, proper schema

Key insight: AI citations favor content with clear, extractable facts. "Our platform helps businesses grow" won't get cited. "87% of users report 3x faster onboarding" will.

Negative Sentiment (-0.1 or lower)

Diagnosis: AI describes your brand unfavorably.

Root causes:

• Negative reviews ranking highly
• Unaddressed complaints on social platforms
• Competitor comparison content positioning you negatively
• Past PR issues still surfacing

Action plan:

1. Source audit: Find exactly which sources are driving negative sentiment (often Reddit, review sites, comparison articles)
2. Address at source: Respond to reviews, engage with complaints, update outdated information
3. Create counter-content: Publish positive case studies, testimonials, and success stories
4. Monitor social signals: Proactively engage on Reddit and forums before complaints go viral

Warning: Negative sentiment is easier to acquire than to fix. A single viral complaint can take months of positive content to counterbalance.

Coverage Gaps (Missing from key query categories)

Diagnosis: You appear for some topics but are invisible for others.

Root causes:

• Content gaps in your library
• Positioning misalignment with query intent
• Competitors owning specific sub-categories

Action plan:

1. Map your gaps: Use Brand Gap analysis to identify highest-priority missing categories
2. Prioritize by impact: Focus on high-volume, high-intent queries first
3. Create targeted content: Build content specifically designed to appear for gap queries
4. Link internally: Connect new content to existing authority pages

How Qwairy Measures GEO Performance

Qwairy tracks all these metrics across ChatGPT, Claude, Perplexity, Gemini, and 10+ AI providers. Here's how Qwairy's metrics map to this guide:

Qwairy's Global Score

Qwairy calculates a weighted Global Score combining five core metrics:

Beyond the Global Score

Qwairy also tracks:

• Question Coverage: Which queries include your brand vs competitors
• Provider Breakdown: Performance differences across ChatGPT, Claude, Perplexity, etc.
• Brand Gap & Source Gap: Visibility gaps revealing highest-priority content opportunities
• Response Stability: How consistently you appear across repeated queries
• Source Intelligence: Which domains get cited and why
• Trend Analysis: Performance changes over time

Start measuring your GEO performance with a free trial, or book a demo to see how Qwairy tracks these metrics for your brand.

Key Takeaways

1. Visibility is foundational-You can't optimize position or sentiment if you're not visible
2. Share of voice reveals competition-Your metrics only matter relative to competitors
3. Citations > Mentions-Being cited as a source carries more authority than being named
4. Sentiment affects conversion-Positive mentions drive action
5. Relevance ensures alignment-Being mentioned in the right context matters
6. Provider differences exist-Track and optimize for each AI platform separately
7. Coverage reveals gaps-Comprehensive visibility beats concentrated visibility

GEO metrics are still evolving as AI platforms mature. The brands that establish measurement practices now will have the data advantage as this space grows.