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Analytics·Last updated January, 2026·1 min read

Measuring AI Visibility: What to Track

The metrics that matter when you want to be the recommended answer: AI visibility, citation quality, and share of model.

Coverage Across High-Intent Prompts

Track presence across queries that represent high-intent use cases in your category. These prompts map directly to revenue outcomes and buying stages.

The goal is not volume. It is consistent presence where decisions are made. This is the foundation of strong AI visibility.

Quality and Accuracy Signals

Monitor precision of brand descriptions, sentiment, and source attribution. The right citations and consistent wording indicate stronger visibility.

Pay particular attention to model citations, which show whether the AI is relying on your sources or a competitor's narrative.

  • Entity accuracy and differentiation
  • Citation quality and relevance
  • Sentiment stability over time

Measure Share of Model

Share of Model is the percentage of AI recommendations in your category that mention your brand versus competitors.

Tracking this over time shows whether your optimisation work is shifting AI attention in your favour.

Detect Shifts Early

Changes in model outputs can indicate improvements or new competitive threats. Regular benchmarking reduces surprise volatility and helps you respond quickly.

Key Takeaways

  • 1Measure prompts that map to real intent and revenue.
  • 2Track accuracy, citations, and sentiment to gauge trust.
  • 3Share of Model reveals competitive movement in AI outputs.
  • 4Frequent checks reduce visibility drift.
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