{"id":131,"date":"2026-06-11T06:52:54","date_gmt":"2026-06-11T06:52:54","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/?p=131"},"modified":"2026-06-11T07:18:31","modified_gmt":"2026-06-11T07:18:31","slug":"ai-share-of-voice","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/ai-share-of-voice\/","title":{"rendered":"AI Share of Voice: How to Calculate It and What a Good Score Looks Like"},"content":{"rendered":"

AI share of voice (AI SOV)<\/strong> is the percentage of all brand mentions in AI-generated answers that belong to your brand rather than a competitor. It answers the question every marketing lead now gets asked: when buyers ask ChatGPT, Gemini or Perplexity for a shortlist, how often are we on it \u2014 and how big is our slice?<\/em><\/p>\n

Most guides stop at the definition. This one gives you the exact formula (plus two weighted variants that can swing your score by double digits on identical data), a fully worked calculation you can rebuild in a spreadsheet, and benchmark distributions by category drawn from 2.4 million AI answers MaxAEO sampled between January 1 and May 31, 2026<\/strong> \u2014 so you can judge whether your number is genuinely good or just sounds good.<\/p>\n

What Is AI Share of Voice?<\/h2>\n

AI share of voice is the share of brand mentions your company captures in AI answers, measured against a fixed set of competitors, across a defined set of prompts and platforms. If AI answers name brands 1,000 times across your tracked prompts and 200 of those mentions are yours, your AI SOV is 20%.<\/p>\n

The metric matters because AI answers behave like zero-sum shortlists<\/strong>. A ChatGPT response to "best CRM for a 50-person sales team" typically names three to seven vendors; every mention a competitor wins is consideration you lose. And the audience is no longer niche: 34% of US adults have used ChatGPT<\/a>, roughly double the 2023 share, per Pew Research \u2014 and Gartner predicted traditional search volume would fall 25% by 2026<\/a> as that behavior shifted to AI assistants.<\/p>\n

AI SOV is one of the six core AI visibility metrics that tell you whether AI recommends your brand<\/a> \u2014 the competitive one. The other five describe you in isolation; share of voice tells you who is eating your lunch.<\/p>\n

AI share of voice vs. mention rate: not the same number<\/h3>\n

These two get conflated constantly, including by tool vendors \u2014 and they answer different questions.<\/p>\n\n\n\n\n\n\n
<\/th>\nMention rate<\/th>\nAI share of voice<\/th>\n<\/tr>\n<\/thead>\n
Formula<\/td>\nanswers naming you \u00f7 all answers sampled<\/td>\nyour mentions \u00f7 all tracked brands' mentions<\/td>\n<\/tr>\n
Measures<\/td>\nPresence<\/strong> \u2014 how often you show up<\/td>\nCompetitive position<\/strong> \u2014 how much of the pool you own<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The difference is not academic. A brand can hold a 30% mention rate (it appears in 3 of every 10 answers) and only an 18% share of voice, because competitors appear in those same answers more often. Report both, but never swap their labels \u2014 executives will anchor on whichever number you show first.<\/p>\n

How it differs from traditional share of voice<\/h3>\n

Traditional SOV divides your advertising spend, impressions or media mentions by the market total. AI SOV keeps the spirit but changes the denominator: the pool is brand mentions inside generated answers<\/strong>, observable only by repeatedly asking the engines and counting what comes back.<\/p>\n

Two practical consequences follow. First, there is no exhaustive data feed \u2014 every score is a sample<\/em>, so methodology (prompt set, platforms, sampling window) determines whether your number means anything. Second, the same brand can score 40% on one platform and 12% on another, because each engine retrieves from different sources. Any single blended number hides that spread, which is why serious AI search monitoring<\/strong> reports per-platform SOV alongside the blend.<\/p>\n

The AI Share of Voice Formula<\/h2>\n

The base formula is:<\/p>\n

AI SOV (%) = (your brand's mentions \u00f7 total mentions of all tracked brands) \u00d7 100<\/strong><\/p>\n

\u2014 counted across the same prompt set, platforms and time window, with each brand counted once per answer<\/strong>. An answer that names you three times still contributes one mention; otherwise verbose answers distort the pool.<\/p>\n

The metric comes in three forms, and they diverge sharply on identical data \u2014 the worked example below produces 18.75%, 12.5% and 4.4% from the same 2,800 answers<\/strong>. Pick one as your headline metric, label it clearly, and report the others as diagnostics. (Some tools add a sentiment-weighted fourth form; treat it as a diagnostic too \u2014 sentiment classification on AI answers is still too noisy to headline.)<\/p>\n

Mention-based SOV (the default)<\/h3>\n

The formula above, applied to brand names appearing anywhere in the answer text. It is the most intuitive and the right default for executive reporting on brand mentions in ChatGPT<\/strong>, Gemini, Perplexity and the rest. Its blind spot: it treats "the market leader is X" and a grudging footnote mention identically.<\/p>\n

Position-weighted SOV (rewards being first)<\/h3>\n

Buyers anchor on the first names in a list, so weighted SOV discounts late mentions. MaxAEO's default weights: 1.0 for a first-position mention, 0.6 for positions 2\u20133, 0.3 for position 4 or later<\/strong>. (Some teams use harmonic decay \u2014 1\/n by position \u2014 which is stricter but harder to explain to a CMO.) Apply weights to every brand's mentions, then divide your weighted total by the pool's weighted total.<\/p>\n

Citation-based SOV (your content vs. their content)<\/h3>\n

Instead of brand names, count AI citations<\/strong> \u2014 the source links an engine attributes its answer to \u2014 and divide your domain's citations by all citations. This measures whether your content<\/em> feeds the answers, not whether your brand<\/em> appears in them. The two routinely tell opposite stories, as the worked example below shows.<\/p>\n

Worked Example: Calculating AI Share of Voice Across 8 Platforms<\/h2>\n

Here is a complete calculation, using the structure MaxAEO applies in daily tracking. Scenario: a B2B SaaS brand tracking itself plus four competitors.<\/p>\n

    \n
  1. Define the sample.<\/strong> 50 buyer-style prompts \u00d7 8 platforms (ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, Google AI Mode, AI Overviews) = 400 answers per day. Sampled daily for 7 days = 2,800 answers<\/strong>.<\/li>\n
  2. Count answers naming you.<\/strong> Your brand appears in 840 of 2,800 answers \u2192 mention rate = 30%<\/strong>.<\/li>\n
  3. Count the full pool.<\/strong> Across all five brands, answers contain 4,480 brand mentions<\/strong> (about 1.6 brands per answer \u2014 most answers name several).<\/li>\n
  4. Apply the formula.<\/strong> 840 \u00f7 4,480 = 18.75% mention-based AI SOV<\/strong>.<\/li>\n
  5. Weight by position.<\/strong> Your 840 mentions split into 126 first-position, 336 in positions 2\u20133, 378 in position 4+. Weighted score: (126 \u00d7 1.0) + (336 \u00d7 0.6) + (378 \u00d7 0.3) = 441<\/strong>. The full pool's 4,480 mentions weight to 3,528. Weighted SOV: 441 \u00f7 3,528 = 12.5%<\/strong>.<\/li>\n
  6. Check citations.<\/strong> Your domain appears in 84 of 1,920 total cited sources \u2192 citation SOV = 4.4%<\/strong>.<\/li>\n<\/ol>\n\n\n\n\n\n\n\n\n
    Metric<\/th>\nCalculation<\/th>\nScore<\/th>\n<\/tr>\n<\/thead>\n
    Mention rate<\/td>\n840 \u00f7 2,800 answers<\/td>\n30%<\/td>\n<\/tr>\n
    Mention-based AI SOV<\/td>\n840 \u00f7 4,480 mentions<\/td>\n18.75%<\/td>\n<\/tr>\n
    Position-weighted SOV<\/td>\n441 \u00f7 3,528 weighted pool<\/td>\n12.5%<\/td>\n<\/tr>\n
    Citation-based SOV<\/td>\n84 \u00f7 1,920 citations<\/td>\n4.4%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \"Worked<\/figure>\n

    To rebuild this yourself, log one spreadsheet row per answer with columns for date, platform, prompt, each brand's presence (0\/1), best position and cited domains. Every score above falls out of a pivot table \u2014 no tooling required.<\/p>\n

    Read the gaps, not just the scores.<\/strong> Raw SOV (18.75%) near parity says you're in the game. Weighted SOV falling to 12.5% says you're mentioned often but rarely first<\/strong> \u2014 your 126 first-position mentions are about 5% of the roughly 2,500 answers that named any brand at all. Citation SOV at 4.4% says the engines describe you using other people's<\/em> pages. Each gap maps to a different fix, which is exactly why one blended number is never enough.<\/p>\n

    What Is a Good AI Share of Voice? Benchmarks From 2.4M Answers<\/h2>\n

    A good AI share of voice is anything above parity<\/strong> \u2014 100 divided by the number of brands tracked \u2014 and an excellent one is 2\u00d7 parity or more<\/strong>. In a 5-brand set, parity is 20%: scoring 25% means you punch above your weight; 40%+ means AI treats you as the category default.<\/p>\n

    Absolute thresholds without that context are meaningless, which is why we normalize. Parity index = your SOV \u00f7 (100 \u00f7 N brands).<\/strong> The 18.75% from the worked example is a 0.94 parity index \u2014 fractionally below its fair share. The same 18.75% in a 12-brand set would be a 2.25 index: dominant.<\/p>\n

    Benchmarks by category (MaxAEO tracking data)<\/h3>\n

    The distributions below come from MaxAEO's competitor benchmarking corpus: 412 competitive sets across 38 B2B software categories, 2.4 million answers sampled daily across 8 platforms, January 1 \u2013 May 31, 2026<\/strong>. Mentions counted once per answer; sets contain 5\u201315 brands (median 9).<\/p>\n\n\n\n\n\n\n\n\n\n\n
    Category<\/th>\nMedian SOV (all brands)<\/th>\nTop-quartile threshold<\/th>\nMedian category leader<\/th>\n<\/tr>\n<\/thead>\n
    CRM & sales tech<\/td>\n6.8%<\/td>\n\u2265 13.5%<\/td>\n28.4%<\/td>\n<\/tr>\n
    Project management<\/td>\n6.1%<\/td>\n\u2265 14.2%<\/td>\n31.7%<\/td>\n<\/tr>\n
    Cybersecurity<\/td>\n5.2%<\/td>\n\u2265 10.9%<\/td>\n23.8%<\/td>\n<\/tr>\n
    Marketing & analytics<\/td>\n6.5%<\/td>\n\u2265 12.8%<\/td>\n26.9%<\/td>\n<\/tr>\n
    HR & payroll<\/td>\n7.9%<\/td>\n\u2265 15.1%<\/td>\n33.6%<\/td>\n<\/tr>\n
    Developer tools<\/td>\n5.7%<\/td>\n\u2265 11.6%<\/td>\n27.3%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \"Bar<\/figure>\n

    The headline finding is concentration: in the median competitive set, the top three brands capture 58% of all mentions<\/strong> (middle 80% of sets: 41\u201374%). AI answers compress markets harder than search rankings do \u2014 there is no page two. The median tracked brand sits around 0.6\u00d7 parity<\/strong>, because leaders absorb the surplus.<\/p>\n

    Rough tiers to calibrate against<\/h3>\n