Measurement9 min de leituraTime de estratégia da OwnlyReal

How to measure AI share of voice for an enterprise brand

A working definition, the four metrics teams confuse, and a worked example across engines, markets and languages.

THE FORMULA
SoA =
answers that name your brandall tracked answers for the prompt set
Share of answer (SoA). Count per engine, market and language — never one blended number.

When a buyer asks an assistant "which developer should I choose in the UAE?", there is no page two. There's one answer, which usually names two to four brands. Share of answer asks a simple question: out of every relevant answer, how many include you?

AI share of voice is now a standard GEO metric, but teams define it in incompatible ways and then argue over the numbers. Here's how to fix the definition before you optimise anything.

Four metrics that get mixed up

Each of these measures something different. A mature dashboard tracks all four for a fixed list of competitors[1][2].

MetricWhat it countsUse it for
Mention rateShare of answers that name your brand at allBaseline visibility
Citation rateShare of answers that link or footnote your domainOwned-content influence
Recommendation rateShare of answers where you are the suggested choice, not just listedCommercial impact
Share of voiceYour mentions ÷ all brand mentions (you + tracked competitors)Competitive position
Fact accuracyShare of mentions where key facts are correctBrand and legal risk

Build the prompt set before the dashboard

Engines answer questions, not keywords, so the unit is a prompt library: realistic buyer questions grouped by intent. For one market, 15–50 prompts across brand, category and comparison clusters is a workable start[2]. At enterprise scale, multiply by markets and languages.

Category40%
"Best developer in Dubai for off-plan"
Comparison25%
"Developer A vs Developer B handover record"
Problem20%
"How to check a developer's escrow account"
Brand15%
"Is [your brand] reliable?"
Suggested mix of the prompt library by intent cluster. Weight it towards category and comparison prompts, where buying decisions happen.

A worked example

An illustrative UAE developer brand, 200 prompts per week, run on five surfaces. The spread between engines is normal. One published example shows a single brand at 22% on Perplexity, 18% on ChatGPT and 6% on Gemini in the same week[1]. That's why a blended average hides the real work.

Answer engine
24%
AI chat
17%
AI overview
11%
App store
31%
Property portal
8%
Illustrative data, not a client result. Share of answer by surface, one market, one week.

Cadence and hygiene

  • Weekly. Daily sampling is noisy and monthly loses signal[1].
  • Freeze the competitor set. Share of voice is only comparable if the denominator stays the same.
  • Log the answer, not just the score. Keep the full text and the citations behind every data point so a change can be traced.
  • Split by language. Arabic and English answers for the same market often disagree about who leads.

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