AI Visibility
Share of voice in AI search measures how often AI chatbots recommend your brand versus competitors. Learn how to calculate it across ChatGPT, Claude, Gemini, and Perplexity.
Every time someone asks ChatGPT for a product recommendation, your brand either shows up or it doesn’t. Multiply that across millions of daily queries on ChatGPT, Claude, Gemini, and Perplexity, and you start to see the problem. If AI isn’t mentioning you, it’s mentioning someone else.
Share of voice in AI search puts a number on that gap. It tells you how visible your brand is when AI answers the questions your customers are asking, and how you compare to competitors fighting for that same space. Honestly, most teams have no idea what their number is. That’s a problem worth fixing.
Forget what you know about traditional share of voice from advertising and PR. That metric tracks ad impressions, media mentions, and social buzz. Useful, but not what we’re talking about here.
Share of voice in AI search measures the percentage of AI-generated responses that mention or recommend your brand when users ask questions relevant to your category.
Here’s the simplest way to think about it. A user asks “what’s the best project management tool?” 100 times across different AI platforms. Your brand appears in 25 of those responses. Your AI share of voice is 25%.
That number captures something no traditional metric can: how often AI chooses to recommend you over everyone else in your space. Think of it as market share of AI recommendations.
Traditional SEO share of voice measures your visibility in organic search results. It looks at keyword rankings, search volume, and estimated click-through rates to calculate how much of the organic search traffic you own. If you rank #1 for a high-volume keyword, your traditional SOV is strong.
But here’s the disconnect. You can rank #1 on Google for “best CRM software” and still have 0% share of voice in AI search. ChatGPT might never mention you. Claude might recommend three competitors instead. Gemini might not know you exist.
Why? Because AI models pull from different signals than Google’s ranking algorithm. They weigh training data, cited sources, and brand authority in ways that don’t map neatly to PageRank. A brand with mediocre SEO but strong presence on comparison sites and authoritative publications might dominate AI recommendations. Meanwhile, the SEO champion gets ignored.
The two metrics can diverge wildly. Tracking both gives you the full picture. Ignoring AI share of voice means ignoring a channel that already influences a growing share of information-seeking searches.
The formula is straightforward. Getting the data is the hard part.
AI Share of Voice = (AI responses mentioning your brand / Total relevant AI responses) x 100Say you track 50 queries that your ideal customers would ask. You run each one across 4 AI platforms (ChatGPT, Claude, Gemini, Perplexity). That gives you 200 total responses. Your brand appears in 30 of them.
Your AI share of voice: 15%.
Now do the same count for each competitor. If Competitor A appears in 80 responses, their SOV is 40%. They’re getting mentioned nearly 3x more than you.
Not all AI platforms are equal. Break your SOV down by model. For each platform, divide your brand mentions by total responses on that platform. So if you ran 50 queries on ChatGPT and your brand appeared in 4 responses, your ChatGPT SOV is 8%. Do the same for Claude, Gemini, and Perplexity.
This per-platform view matters more than the aggregate number. You might discover you have 35% SOV on Perplexity but only 8% on ChatGPT. That tells you exactly where to focus your optimization efforts.
Different customer types ask different questions. A startup founder and an enterprise CTO search for “best CRM” with very different framing and expectations. Your share of voice likely varies across these segments.
Calculate SOV separately for each customer persona or query category. You might dominate “best CRM for small teams” queries but vanish on “enterprise CRM with Salesforce integration” queries. That kind of granularity turns a single metric into a strategic roadmap.
Numbers make this concrete. Imagine two competitors in the cybersecurity space. Brand A has 20% AI share of voice. Brand B has 60%.
When a CISO asks ChatGPT “what are the best endpoint protection platforms?”, Brand B gets mentioned three times more often. When a security analyst asks Claude to compare options, Brand B shows up consistently. This is the metric most brands are ignoring right now.
That 40-point gap compounds. Every AI-assisted research session where Brand B appears and Brand A doesn’t is a potential customer who never even considers Brand A. As AI search adoption grows, that compounding effect accelerates.
The brands tracking their competitor share of voice in ChatGPT and other AI platforms right now can see the gap, diagnose why it exists, and close it before it widens further. The ones not tracking it? They don’t even know they’re losing.
Share of voice gives you the macro view. Share of Model tracking zooms in further by measuring your visibility on each specific AI model independently and comparing the patterns.
This matters because different AI models behave differently. Perplexity actively searches the web and cites sources in real time, so if your content appears on sites Perplexity indexes, you’ll likely get mentioned. ChatGPT leans on training data and (with browsing enabled) recent web content. Claude draws from its training data and tends toward nuanced, detailed responses, so getting mentioned usually requires strong presence in authoritative, well-structured content. Gemini integrates with Google’s search index, giving it a different source profile than the others.
A practical example: a B2B analytics company might have 40% SOV on Perplexity (because they publish data-rich reports that get cited everywhere) but only 5% on ChatGPT (because competitors have stronger training data presence). That gap tells you where your content strategy is working and where it isn’t.
Share of Model tracking also reveals citation behavior differences. Perplexity shows its sources. ChatGPT usually doesn’t. Claude provides detailed reasoning. Gemini pulls from Google’s ecosystem. Once you see these patterns, you can tailor your content strategy to each model instead of guessing.
Manually checking your share of voice is possible but painful. You’d need to run dozens of queries across multiple platforms, record every brand mention, and repeat the process regularly to spot trends. A spreadsheet works for a one-time snapshot, but nobody keeps that up for long.
RivalSee takes a persona-driven approach: it simulates the specific questions your ideal customers ask, then measures which brands AI recommends in response. You get SOV broken down by customer segment, by platform, and over time so you can see whether your numbers are moving in the right direction. You also see exactly which competitors are capturing the share you’re missing.
The persona-driven approach matters because “best project management tool” and “best project management tool for remote marketing teams” can produce very different AI responses. Generic queries miss the nuance that actually drives customer decisions.
For more on monitoring competitors specifically, see our guide on how to monitor competitors in AI search.
Tracking is step one. Here’s how to actually move the needle.
AI models love well-organized comparisons. “Product A vs Product B” pages, feature comparison tables, and category roundups are exactly the kind of content AI draws from when generating recommendations. Make sure your brand is present and well-represented in these formats.
Pay attention to which sources AI models cite when recommending your competitors. Those are the publications, review sites, and directories that AI trusts. Getting featured on those same sources directly increases your chances of being mentioned. G2, Capterra, and niche-specific review sites carry outsized weight with AI models.
Original research and proprietary data give AI models a reason to cite you specifically. If you publish the definitive benchmark report in your industry, AI will reference it. Generic content that rehashes what everyone else says won’t earn citations.
Your Share of Model tracking will reveal where you’re strong and where you’re weak. Don’t treat all AI platforms the same. If Perplexity is your strength because of web citations, double down on publishable, linkable content. If ChatGPT is your weakness, focus on building broader brand presence across training data sources.
AI models update frequently. Your share of voice can shift after a model update, a competitor’s content push, or changes in the sources AI references. Track your SOV at least monthly. When you see a drop, investigate what changed and adjust.
AI models construct understanding from structured data about your brand: Wikipedia entries, Wikidata, Crunchbase profiles, and schema markup on your site. Make sure your brand information is accurate across these sources. When AI has clean, consistent data about what your company does and who it serves, it’s more likely to surface you in relevant recommendations.
For a deeper look at how traditional SEO and AI optimization differ, check out our SEO vs AI SEO guide. And for a complete walkthrough of tracking brand mentions across AI platforms, see how to track AI mentions.
It’s the percentage of relevant AI-generated responses that mention or recommend your brand. If you ask ChatGPT, Claude, Gemini, or Perplexity questions your customers would ask, and count how often your brand appears versus competitors, that percentage is your AI share of voice. It’s different from traditional share of voice, which tracks ad spend or media mentions.
Run relevant queries across multiple AI platforms and count how often each brand is mentioned. You can do this manually with a spreadsheet, but it gets tedious fast. Tools like RivalSee automate the process by running persona-driven queries across ChatGPT, Claude, Gemini, and Perplexity, then calculating your SOV per platform and customer segment.
Your competitor may have stronger presence on authoritative sources that AI models reference, better structured comparison content, or more citations in industry publications. It could also be that they simply show up on more of the sites AI models draw from. Share of Model tracking can help you identify exactly which platforms and query types your competitor dominates, so you can target those gaps.
Share of Model tracking breaks your overall AI share of voice down by individual AI platform. Instead of one blended percentage, you see how it varies per model (for example, 40% on Perplexity but 5% on ChatGPT). This matters because each AI model has different citation behaviors and data sources, so your optimization strategy should account for those differences.
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