A GEO Metrics Framework: What to Actually Measure

The measurement gap in AI search

Traditional SEO has clear metrics. Rankings. Click-through rates. Impressions. Decades of tooling.

AI search has none of that infrastructure yet.

When someone asks ChatGPT "what's the best project management tool for remote teams," there's no equivalent of a Google Search Console telling you whether your product appeared in the response. No impression count. No position tracking.

This creates a measurement gap. And gaps create two problems: either you ignore AI search entirely, or you chase metrics that don't translate.

A practical framework: four tiers of AI visibility

Before measuring, define what matters. AI visibility isn't one thing—it's a stack.

Each tier has different value. A mention without recommendation may be neutral or cautionary. A citation without mention means your content informed the answer without brand attribution. Primary positioning in a competitive query signals strong model affinity.

Core metrics worth tracking

1. Mention Rate

The percentage of relevant queries where your brand appears in the response.

This requires defining your query set first. What questions should surface your brand? Product categories. Use cases. Competitor comparisons. Problem statements your product solves.

Mention rate is your baseline. If it's zero, nothing else matters yet.

2. Citation Rate

How often AI engines link to your URLs when answering. Not all mentions include citations—models often synthesize without sourcing.

Citation matters for two reasons. First, it's the closest proxy to potential traffic. Second, it signals that models treat your content as authoritative reference material, not just brand awareness.

Perplexity displays citations prominently. Claude and ChatGPT show them when web search is enabled. Tracking citation rate across engines reveals where your content has source credibility.

3. Share-of-Voice (SoV)

Within your query set, how does your mention rate compare to competitors?

Query Category	Your Brand	Competitor A	Competitor B	Others
Product comparison	45%	60%	35%	20%
Use case: [specific]	70%	40%	25%	15%
Problem: [pain point]	30%	55%	50%	30%
Pricing queries	25%	45%	40%	35%

Note: percentages can exceed 100% because multiple brands appear in single responses.

SoV reveals competitive dynamics invisible in traditional search. A competitor might rank lower on Google but dominate AI recommendations—or vice versa.

4. Mention Context & Sentiment

Where and how does your brand appear in the response?

• Position: First mentioned, middle of list, last
• Framing: Recommended, compared neutrally, cautioned against
• Qualifiers: "popular choice," "budget option," "enterprise-focused"

A brand mentioned first with positive framing differs enormously from one mentioned last with caveats.

5. Engine Variance

The same query produces different results across ChatGPT, Claude, and Perplexity. Each model has different training data, different retrieval approaches, different tendencies.

Track metrics per engine. You might have strong presence in Claude but weak visibility in ChatGPT. That variance guides where to focus optimization efforts.

What not to measure (yet)

Direct traffic attribution remains unreliable. AI referral traffic is a small portion of overall web traffic today, and most AI interactions don't result in clicks.

A 2024 analysis found that AI chatbot referrals accounted for roughly 0.1% of global website traffic, though growing month-over-month (Rand Fishkin / SparkToro, 2024). The number is rising, but building your entire measurement system around click attribution sets wrong expectations.

Conversion from AI traffic requires clean attribution that most analytics setups can't provide. AI users often arrive through complex paths—conversation, then separate browser search, then direct navigation.

Focus on visibility intelligence. Measure whether you're present and how you're framed. Traffic will follow visibility, but not in ways current tools can cleanly attribute.

Building your measurement practice

Start with these steps:

1. Define your query set: 20-50 queries that represent your category, use cases, and competitive landscape
2. Establish baselines: Run each query across ChatGPT, Claude, and Perplexity. Document mention/citation presence
3. Track weekly: AI responses change as models update. Establish cadence
4. Segment by intent: Informational queries, comparison queries, and transactional queries behave differently

The discipline matters more than the tooling. Manual tracking works for small query sets. Automation becomes necessary as you scale.

Where Mentio fits

Mentio automates this measurement. It probes AI engines with your query set, detects mentions and citations, calculates share-of-voice against competitors, and tracks changes over time.

Self-hostable. API and CLI access. No lock-in.

The goal isn't to promise traffic miracles. It's to give you the visibility intelligence that lets you understand where you stand—and what to do about it.