How to Measure Your AI Visibility: The KPIs That Matter
You can't improve what you don't measure. Here are the practical KPIs for AI visibility — from your GeoPageScan score to real AI-citation tracking.
Start with an AI-visibility score
A single 0–100 score across GEO/AEO/SEO categories — like the one GeoPageScan produces — gives you a baseline and a trend to manage. Re-scan after each change to measure the lift.
Track real AI citations
Periodically ask the major engines (ChatGPT, Claude, Gemini, Perplexity) the questions your customers ask, and record whether — and how — you're cited. Watch your referral analytics for AI sources too.
Monitor the inputs
Track the signals that drive visibility: llms.txt present, schema coverage, AI bots allowed, and the share of pages with answer-first content. These move before citations do.
Frequently asked questions
How do I measure AI visibility?⌄
Use an AI-visibility score (0–100) as your baseline, track real citations by querying the major AI engines for your key questions, and monitor inputs like schema coverage and crawler access.
What's a good AI-visibility KPI?⌄
The overall score tracked over time, the number of resolved critical/high findings, and the count of AI citations for your priority queries.
How often should I check?⌄
Re-scan after each fix to confirm the lift, and review citations and score monthly as the AI landscape shifts.
