The formula
For one workspace, one engine, one day: Share of Voice = (answers naming your brand ÷ total answers collected) × 100, rounded to one decimal. Run 10 prompts across 4 engines and you have 40 answers; if 17 name you, that's 42.5% for the day. Workspace SOV aggregates prompt-level results upward, and trend lines compare the same prompts week over week — so a rising score means genuinely more citations, never a changing prompt mix.
A worked example
Take an illustrative workspace tracking 10 buyer prompts on ChatGPT and Gemini — 20 answers a day. Monday: ChatGPT names the brand in 6 of 10 (60%), Gemini in 3 of 10 (30%). Overall: 9 of 20, 45%. By Friday, after publishing one FAQ targeting the two weakest prompts, it's 7 and 5 — 12 of 20, 60%. The +15pt move decomposes cleanly: which prompts flipped, on which engine, after which fix. That decomposition is the whole point of measuring daily instead of monthly.
Why per-engine splits beat the headline
A single blended number hides the actionable shape. 45% overall could be 90% on ChatGPT and 0% on Claude — and the fix for 'invisible on Claude' (different retrieval, different sources trusted) has nothing in common with 'slipping on ChatGPT'. Our dashboard reports per-engine SOV first and the aggregate second, and every gap in Aim names the engines where you're missing. Aggregate for the executive summary; split for the work.
What SOV doesn't capture (honestly)
Being named isn't being recommended — that's why every citation also gets a sentiment classification, and why a negatively-framed mention triggers our crisis watch instead of counting as a win. SOV also says nothing about position (first sentence vs buried paragraph six), which is why the citation board records first-mention position separately. One number opens the conversation; it never closes it. The full model, including what we deliberately don't score, is published openly in our scoring methodology.
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