Share of Model: The Metric That Replaces Share of Voice for AI
Share of Model measures how often AI engines mention your brand across a defined query set. It is the clearest way to benchmark AI visibility against competitors and track whether your content and structural changes are moving the right pages into AI answers.
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What Share of Model measures
Share of Model measures the percentage of relevant AI responses that mention your brand. Instead of asking where you rank in a list of links, it asks how often your brand appears at all when users ask category, problem, and comparison questions across answer engines.
That makes it the most useful top-line metric for AI visibility. If your Share of Model is low, your brand is absent from recommendation moments. If it rises after you publish better category pages or comparison content, you have direct evidence that your site is becoming more usable to models.
Why it is more useful than vanity monitoring
Many teams get trapped in anecdotal screenshots: one good answer from ChatGPT, one bad answer from Perplexity, and no sense of the broader pattern. Share of Model forces disciplined measurement because it is based on a repeated query bank rather than isolated examples.
It also puts competitors in the frame. A low score matters more when a direct competitor is repeatedly recommended for the same query set. That is often the signal that a competitor has clearer category pages, stronger comparisons, or more citable supporting content than you do.
How to calculate it well
The quality of the metric depends on the query bank. Good measurement mixes commercial category terms, informational questions, comparison queries, and problem-led prompts. The goal is not to inflate a score with easy branded searches. The goal is to understand discoverability where users are actually researching the category.
Segment the query bank by intent and by engine. A healthy AI visibility program should know whether it is winning on commercial category queries, educational questions, and alternative-intent prompts separately, not only as one rolled-up percentage.
- Use category, comparison, and problem-led queries together.
- Measure per engine, not only as an overall blended score.
- Track brand mention quality and citation sources alongside mention frequency.
What to do when the score is low
A low Share of Model score is a diagnosis, not a strategy. The next step is to inspect the pages and patterns behind it: which pages are being cited, which competitors dominate, and which missing assets keep appearing in the comparison.
In practice, the fixes are usually structural. Publish a clearer category page, add direct comparison content, build glossary and FAQ support for ambiguous terms, and strengthen the pages that answer the exact research questions buyers ask before they evaluate vendors.
Turn the guidance into a site update
Run the free audit if you want proof of what is blocking AI visibility now, or start a trial if you need ongoing monitoring, citation tracking, and competitor reporting.
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