Your AI share of voice is 12%. Of what?

Start with two fractions

Use an answer–brand pair as the counting unit: a brand gets one appearance per answer, even if the answer repeats its name six times. That keeps verbose answers from inflating the score. This is a proposed counting convention, so write it down rather than assuming every tool uses it.

Your mention rate is 30 ÷ 100 answers = 30%. Your share of tracked brand appearances is 30 ÷ 100 appearances = 30%. The matching percentages are a coincidence. An answer can name several brands, or none.

Now discover another product, Rival C, named in 50 of those same answers. Your mention rate remains 30%. Your share across all four brands falls to 30 ÷ 150 = 20%. Your brand did not become less visible. The comparison became more complete.

Try the denominator yourself

Change the counts below. “Other brands” means their combined answer–brand appearances, so it can exceed the number of answers. These numbers are illustrative; the calculator does not query any model.

Agree on what counts before running the test

• Keep valid answers separate from failed requests. A timeout is missing data, not a vote against your brand.
• Count aliases consistently. Decide whether a parent company and a product are one entity or two, and retain the mapping.
• Distinguish a mention from a recommendation. “Avoid Brand A for this use case” contains the name but is hardly a sales opportunity.
• Show results by provider before pooling them. Equal API request counts are an experimental choice, not evidence of equal audience sizes.

Keep both a fixed competitor view and a discovered-brand view if you can. The fixed view supports comparisons with an earlier run. The discovered view catches the awkward possibility that the market is discussing companies your spreadsheet forgot.

There is no reason to expect two products with different questions and scoring rules to return interchangeable scores. For example, Semrush’s description of its AI share-of-voice methodology includes mention position in some of its product metrics. Read the definition before comparing the number.

So what is a good score?

There is no useful universal target without a question set and a competitive context. A niche specialist can reasonably appear less often across a broad category and still be the clear recommendation for the buyers it serves best.

Look first at commercially relevant questions. If your product is built for small Swiss teams, an enterprise procurement prompt may tell you very little. Then compare like with like over time, preserving the prompt wording, model settings and competitor set. Our guide to choosing prompts covers that groundwork.

The number earns its place when you can open the answers behind it, identify who appeared instead and decide what deserves attention. A percentage without that trail is a very polished dead end.