When GEO was new, nobody agreed on what to measure. Everyone had their own framework, their own dashboards, their own definition of "AI visibility."
That's changing. After a year of the industry iterating, testing, and comparing notes, we're converging on a core set of metrics that actually predict business outcomes. Here's what matters in 2026—and what you can stop tracking.
The Four Core Metrics
1. Share of Voice (SoV)
What it is: The percentage of AI-generated responses in your category that mention your brand.
Why it matters: SoV is the single best predictor of AI-driven revenue. Incremys found that brands need to hit a 30% mention frequency threshold before AI recommendations start driving measurable business results. Below that, you're appearing occasionally but not consistently enough to influence purchase decisions.
How to measure it:
- Define your target query set (50-200 queries that your ideal customer would ask)
- Run those queries regularly across ChatGPT, Perplexity, Gemini, and Google AI Overviews
- Track: (queries where your brand appears / total queries) × 100
- Segment by platform, query category, and intent type
Benchmark: Top brands in competitive categories average 25-40% SoV. If you're below 10%, you have significant work to do. If you're at 0%, you're invisible.
2. Sentiment Accuracy
What it is: Whether AI accurately represents your brand's strengths, positioning, and value proposition.
Why it matters: Being mentioned isn't enough if the AI gets your story wrong. We've seen brands with decent SoV but terrible sentiment accuracy—the AI mentions them but positions them incorrectly (wrong pricing tier, wrong target audience, outdated features).
Sequencr's research showed that 62% of brands had at least one major inaccuracy in how AI models described them. These inaccuracies directly hurt conversion—users arrive with wrong expectations and bounce.
How to measure it:
- For each AI mention of your brand, evaluate:
- Is the core positioning correct?
- Are features/capabilities accurately described?
- Is pricing/tier information current?
- Is the target audience correctly identified?
- Score each mention on a 1-5 accuracy scale
- Track trends over time (are inaccuracies getting fixed or persisting?)
Benchmark: You want 80%+ of mentions to be substantially accurate. Below 60% and AI visibility might actually be hurting you.
3. Query Coverage
What it is: The breadth of queries for which your brand appears in AI responses.
Why it matters: Many brands optimize for a few key queries but miss the long tail. If you show up for "best CRM software" but not for "CRM for nonprofit organizations" or "simple CRM for solopreneurs," you're leaving entire customer segments on the table.
How to measure it:
- Map your full query universe (all queries where your brand SHOULD appear)
- Group by: intent type, customer segment, use case, comparison queries
- Track coverage percentage for each group
- Identify gap clusters—groups of related queries where you're consistently absent
Benchmark: Best-in-class brands cover 60-70% of their relevant query universe. Most brands cover less than 20%. The gap is your opportunity.
4. Factual Alignment
What it is: Whether AI's factual claims about your brand are verifiable and up to date.
Why it matters: This is different from sentiment accuracy. Factual alignment is about specific, verifiable claims: "Company X was founded in 2018" (correct or not?), "Product Y supports Salesforce integration" (true or false?), "Plan Z costs $49/month" (current or outdated?).
Superlines found that factual errors in AI responses about brands lead to a 23% increase in support ticket volume as users arrive with incorrect expectations. It also erodes trust—if a user fact-checks one claim and finds it wrong, they distrust everything the AI said about you.
How to measure it:
- Extract all factual claims AI makes about your brand
- Verify each against current reality
- Categorize errors: outdated info, completely wrong, partially correct
- Prioritize corrections based on impact (pricing errors > founding date errors)
Benchmark: Aim for 90%+ factual accuracy. Anything below 75% requires immediate intervention.
What You Can Stop Tracking
Raw mention count without context. Knowing you were mentioned 47 times this month means nothing if you don't know the sentiment, accuracy, and query relevance.
Vanity "AI scores" from tools that give you a single number. GEO is too multi-dimensional for one score. Any tool that reduces your AI visibility to a single metric is oversimplifying.
Model-specific rankings. "We rank #2 in ChatGPT for our main keyword" is useful directionally but misleading as a KPI. AI responses are non-deterministic—the same query gives different results each time. Focus on frequency (SoV) not position.
How GEO Metrics Compare to SEO Metrics
| GEO Metric | SEO Equivalent | Key Difference |
|---|---|---|
| Share of Voice | Keyword rankings | Frequency vs position |
| Sentiment Accuracy | Brand SERP management | AI narrative vs search snippets |
| Query Coverage | Keyword coverage | Query universe vs keyword list |
| Factual Alignment | Knowledge Panel accuracy | Dynamic responses vs static panels |
The biggest difference: SEO metrics are relatively stable (rankings change gradually). GEO metrics can shift dramatically with model updates. This means more frequent monitoring is essential.
Building Your Measurement System
Step 1: Define your query universe
Start with 50 core queries. Expand to 200 over time. Include:
- Category queries ("best [your category] software")
- Use case queries ("how to [solve problem you solve]")
- Comparison queries ("[your brand] vs [competitor]")
- Recommendation queries ("what should I use for [your use case]")
Step 2: Establish monitoring cadence
Weekly for core queries. Monthly for the full query set. After every major model update, run the full set immediately.
Step 3: Set baselines and targets
Measure where you are now. Set 90-day targets for each metric. Review and adjust quarterly.
Step 4: Connect to business outcomes
The ultimate validation: correlate your GEO metrics with actual business metrics (traffic from AI sources, conversion rates, revenue). This closes the loop and proves ROI.
The measurement frameworks are maturing. The brands that adopt disciplined GEO measurement now will have a significant data advantage over competitors who are still guessing.
Originally published on GeoBuddy Blog.
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