AI Share of Voice

The metric starts with a prompt set: the real questions buyers ask in your category, "best X for Y," "is brand Z any good," "what should I buy for…." You run those prompts across the engines that matter, repeatedly, and count how often each brand is named or cited. Your share of voice is your count expressed as a percentage of the total across all tracked brands.

A set of prompts is essential because individual AI answers are noisy and non-deterministic, the same question can name different brands on different runs. Averaging across many prompts, engines and runs turns that noise into a stable read you can trend and compare, the way traditional share of voice averages across a media landscape.

AI share of voice answers two questions at once: where you stand against competitors in the AI answer layer right now, and whether your optimization work is actually moving that position over time. It is a competitive, relative metric, there is no universal "good" number, only your share versus the rivals in your category.

For a store, AI share of voice is a category scoreboard: "we appear in 22% of answers for our core buyer questions; our biggest competitor appears in 58%." That single number makes an invisible channel legible to a team and points directly at where the gap, and the work, is.

Tracked over time, it's how you prove GEO is paying off. When you fix the pages behind the prompts you lose and your share climbs while a competitor's slips, you have a defensible, trended measure of progress rather than an anecdote about one good answer.

Illustrative scenario: a coffee-gear store tracks 50 buyer prompts across five engines and finds it appears in 12% of answers for "gifts for coffee lovers." It sets a target, adds sourced gift-guide and comparison content, and watches whether its share of that prompt cluster rises over the next quarter.