Share of Voice (SOV) in the context of AI visibility is the percentage of brand mentions that are yours, across the answers you track. It answers the question: “When an AI engine names brands in my category, how often is the brand it names mine?”
Note that this is a different question from how often you appear at all: that is impression rate, and it has responses in the denominator rather than mentions.
Calculating share of voice
Your SOV = (Your mentions ÷ Total mentions across all tracked brands) × 100
The denominator is the brands you track, not the whole category, and in LLM Metrix that means your brand plus the competitors you have configured on the project. Every other company an answer happens to name is excluded rather than counted against you.
That is deliberate. AI answers vary wildly in how many vendors they list, and counting all of them makes the metric a measure of answer length: an answer naming sixty tools would collapse a genuine 43% share to 7% without anything about your visibility having changed. The trade is that our figure is not comparable to a category-wide SOV from another tool; ours will read higher, for a reason that has nothing to do with performance. Keep the tracked set stable if you want the trend to mean anything, and re-baseline when you add a competitor.
Example: You track 5 brands across 100 queries. The results are:
| Brand | Mentions | SOV |
|---|---|---|
| Your brand | 42 | 42% |
| Competitor A | 35 | 35% |
| Competitor B | 13 | 13% |
| Competitor C | 6 | 6% |
| Competitor D | 4 | 4% |
In this example, your brand leads with 42% SOV, but Competitor A is close behind and worth monitoring.
Why SOV matters more than raw mention count
Raw mentions don’t tell you whether you’re winning or losing in context. A brand with 200 mentions sounds impressive until you learn the category leader has 800 mentions. SOV provides the competitive context raw counts lack.
SOV is also a more stable metric for trend tracking: if overall query volume in your category grows, raw mentions grow for everyone, but SOV reveals whether you’re growing your share or just riding the tide.
SOV across query clusters
One aggregate number hides a lot. A brand might have:
- 70% SOV in “best enterprise tool for X” queries
- 12% SOV in “affordable alternative to Y” queries
That gap is where to invest in content and authority building, so it is worth reading your SOV cut more than one way.
LLM Metrix cuts SOV two ways, and the cluster cut is not one of them. Reading the two as one view is the easy mistake here, so they are worth separating:
- Per engine: your share against your tracked competitors on each surface separately. This is a point-in-time read of one scan, not a series: there is no per-engine share-of-voice trend line.
- Over your scan history: a genuine trend, but it plots you against the whole tracked competitor set as one aggregate, not a line per rival and not a line per engine. “Who gained on us this quarter” is therefore a comparison you make between two scans rather than a chart you open.
Grouping by query cluster is analysis you do yourself, from the per-prompt results.
The relationship between SOV and market share
Research in traditional advertising showed that brands with SOV above their market share tend to grow, while brands with SOV below their market share tend to decline. Early evidence suggests the same dynamic applies to AI share of voice: brands winning in AI answers tend to build consumer preference over time, even without a direct click.