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Bansal",[402],{"type":27,"image":403,"mobileImage":407},[404],{"src":405,"alt":400,"width":406,"height":406},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002FE081GMJV4MU-U082E8CCFKJ-47e2b2e26570-512.jpeg",512,[],"Director of Growth","\u003Cp>Shreshtha is the Director of Marketing and Growth across Pixis and Stellar. An IIM Lucknow alumna with experience at Google, she brings a strong foundation in growth, brand strategy, and performance marketing. Her work focuses on helping brands improve discoverability, build authority, and adapt to the new realities of AI-led marketing.\u003C\u002Fp>",{"url":411},"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fshreshtha-bansal-24453b6b\u002F?skipRedirect=true",[],{"title":414,"description":415,"advanced":416,"keywords":418,"social":419},"Attributed Share of Voice in AI Search: What It Measures and How to Read It | Pixis","",{"canonical":415,"robots":417},[],[],{"facebook":420,"twitter":421},{"description":415,"title":414},{"description":415,"title":414},[423],{"type":27,"image":424,"mobileImage":430},[425],{"src":426,"alt":427,"width":428,"height":429},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fimage-91.png","Attributed Share of Voice in AI Search",1942,1376,[],[432,435,438],{"title":433,"slug":434},"SEO\u002FAEO\u002FGEO","seo-aeo-geo",{"title":436,"slug":437},"Marketing Strategy","marketing-strategy",{"title":439,"slug":440},"Pixis Visibility","pixis-visibility",[442],{"blocks":443},[444],{"type":445,"textBlock":446},"textBlock_Entry","\u003Cp>In July 2025, Ahrefs published a study of 1.9 million citations from a million AI Overviews. It found that 76.1% of cited pages ranked in Google’s top 10 for the same query. That number traveled. It was quoted in decks, built into strategy, and used to argue that AI citation was mostly a rankings problem in a new guise.\u003C\u002Fp>\u003Cp>Seven months later, the same team ran it again across 863,000 SERPs and 4 million cited URLs. The figure came back at 37.9%.\u003C\u002Fp>\u003Cp>Half. In seven months. Same publisher, same metric, same broad method.\u003C\u002Fp>\u003Cp>Read \u003Ca href=\"https:\u002F\u002Fahrefs.com\u002Fblog\u002Fsearch-rankings-ai-citations\u002F\">the two\u003C\u002Fa> \u003Ca href=\"https:\u002F\u002Fahrefs.com\u002Fblog\u002Fai-overview-citations-top-10\u002F\">studies\u003C\u002Fa> side by side, and the story gets more interesting than a collapse. The first examined only the top three most visible citations per response. The second examined every cited URL. Ahrefs also improved its citation parsing in the meantime and says explicitly that the datasets are not cleanly comparable. So part of the drop is Google behaving differently, and part is a change in what was being counted, and the honest answer is that nobody can cleanly separate the two.\u003C\u002Fp>\u003Cp>That is the condition anyone measuring AI search visibility is working in. The most rigorous public research on citation behavior could not hold a stable unit of analysis across two quarters. Which is worth remembering the next time someone hands you a benchmark for what a healthy Attributed Share of Voice looks like.\u003C\u002Fp>\u003Cp>ASOV is a real metric and worth running. It measures how often generative engines cite your domain as a source relative to all citations in your tracked category. What it lacks is a fixed floor, an agreed denominator, or an industry average that means anything. This guide covers what it measures, how to calculate it, why borrowed benchmarks fail, and what to use instead.\u003C\u002Fp>\u003Ch2>\u003Cstrong>Key Takeaways\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cul>\u003Cli>ASOV measures the share of generative responses that cite your domain as a source. It is distinct from traditional share of voice, which counts ad impressions or brand mentions without verifying attribution.\u003C\u002Fli>\u003Cli>There is no published industry benchmark for ASOV. The metric is new, vendors scope the denominator differently, and citation research shifts fast enough that any fixed range describes a moment rather than a standard.\u003C\u002Fli>\u003Cli>Your baseline is your own first measurement against your own probe set. Direction of travel against a named competitor set is the signal that matters, not distance from an abstract average.\u003C\u002Fli>\u003Cli>Being cited is not the same as being clicked. Pew Research found users click links inside AI summaries just 1% of the time. Citation is placement at the moment of decision, and it should be measured on those terms.\u003C\u002Fli>\u003Cli>Engine behavior diverges enough that a blended score hides what you need to act on. Google’s own documentation confirms that AI Overviews and AI Mode use different models and surface different links.\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fproducts\u002Fpixis-visibility\u002F\">Pixis Visibility\u003C\u002Fa> runs 12 sessions per prompt across ChatGPT, Perplexity, Gemini, and Claude, producing citation data with variance reduction rather than single-session snapshots, and feeds each gap into a content brief that closes it.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>\u003Cstrong>What Attributed Share of Voice Actually Measures\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>ASOV is the percentage of tracked generative responses in which your domain appears as a cited source, divided by all brand citations in that same response set.\u003C\u002Fp>\u003Cp>The word doing the work is \u003Ci>attributed\u003C\u002Fi>. Traditional share of voice counts presence: ad impressions, organic rankings, social mentions. It answers “how often are we named?” ASOV counts attribution: an explicit, linked citation that the engine has selected as the basis for a claim. It answers “how often are we the source?”\u003C\u002Fp>\u003Cp>The distinction is not academic. A brand can be discussed constantly in AI answers and cited almost never, if the engine is drawing its facts from a third-party review site that covers your category better than you do. Mentions describe your reputation. Citations describe your authority.\u003C\u002Fp>\u003Cp>Four things separate the two metrics. Traditional SOV measures brand mentions and ad impressions; ASOV measures explicit citations inside AI responses. Traditional SOV captures passive exposure, the fact of having been seen; ASOV captures active source attribution, the fact of having been selected. Traditional SOV runs against a denominator you define in advance, a list of competitors you already know about; ASOV runs against an open set of every domain the engine chose to cite. And where traditional SOV answers how often you are named, ASOV answers whether you are the source the answer rests on.\u003C\u002Fp>\u003Cp>The open denominator is what makes ASOV harder than it looks. In traditional SOV, you decide who your competitors are and measure against that list. In AI search, your citation competitor might be a competitor’s blog, a community thread, or a video. Ahrefs found that among \u003Ca href=\"https:\u002F\u002Fahrefs.com\u002Fblog\u002Fai-overview-citations-top-10\u002F\">AI Overview citations that did not rank in Google’s top 100\u003C\u002Fa> for the same keyword, 18.2% were YouTube URLs, with YouTube accounting for 5.6% of all cited URLs in their dataset. Your share is being taken by sources that were never on your competitive radar.\u003C\u002Fp>\u003Ch2>\u003Cstrong>Why ASOV Matters for Your Brand\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>\u003Cstrong>Authority, not exposure. \u003C\u002Fstrong>A citation is the engine’s judgment that your page is a reliable basis for a claim. That is a different signal from an impression, and a stronger one.\u003C\u002Fp>\u003Cp>\u003Cstrong>Category presence at the decision point. \u003C\u002Fstrong>Buyers now ask complex questions and read the synthesis. If your domain is not in the source set, you are absent from the moment the shortlist forms.\u003C\u002Fp>\u003Cp>\u003Cstrong>Diagnostic precision. \u003C\u002Fstrong>A low ASOV tells you where you are not being selected: which prompts, which engines, which subtopics. That is an actionable map, not a vanity metric.\u003C\u002Fp>\u003Cp>\u003Cstrong>An honest limit. \u003C\u002Fstrong>ASOV does not predict revenue, and the research to establish that link does not yet exist. It is a visibility metric. Treating it as a leading revenue indicator without evidence is exactly the kind of claim that erodes trust with the people you are reporting to.\u003C\u002Fp>\u003Ch2>\u003Cstrong>How to Measure and Calculate ASOV\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>\u003Cstrong>Define your probe set. \u003C\u002Fstrong>Select the queries, long-tail questions, and conversational prompts your buyers actually use. This is the single choice that determines whether your data is useful. Track the wrong prompts, and every downstream number is precise and irrelevant.\u003C\u002Fp>\u003Cp>\u003Cstrong>Identify your engine set. \u003C\u002Fstrong>ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews behave differently enough that they cannot be treated as one channel.\u003C\u002Fp>\u003Cp>\u003Cstrong>Identify your competitor set. \u003C\u002Fstrong>Include direct commercial rivals, but also the informational publishers, communities, and video sources that take citation share in your category.\u003C\u002Fp>\u003Cp>\u003Cstrong>Separate citations from mentions. \u003C\u002Fstrong>A linked, attributed source is a different event from an unlinked name-drop. Decide which you are counting and hold that rule.\u003C\u002Fp>\u003Cp>\u003Cstrong>Aggregate, then apply the formula. \u003C\u002Fstrong>Your citations are divided by total category citations, multiplied by one hundred.\u003C\u002Fp>\u003Ch3>\u003Cstrong>Critical measurement choices\u003C\u002Fstrong>\u003C\u002Fh3>\u003Cp>Your prompt set is a parameter, not a detail. So is your citation rule. So is any weighting scheme you apply, and position weighting is worth considering: a source named in the opening lines of an answer carries a different value from one in a trailing list, because that is where reader attention concentrates.\u003C\u002Fp>\u003Cp>These choices are why two brands can report the same ASOV and mean different things. Whatever you decide, write it down and hold it. A metric whose definition drifts is not a metric.\u003C\u002Fp>\u003Ch3>\u003Cstrong>Engine-specific behavior\u003C\u002Fstrong>\u003C\u002Fh3>\u003Cp>Perplexity attaches sources to nearly every claim. ChatGPT surfaces links inconsistently. Google AI Overviews sit above the organic results rather than replacing them.\u003C\u002Fp>\u003Cp>The divergence exists even within a single company. Google’s documentation states plainly that \u003Ca href=\"https:\u002F\u002Fdevelopers.google.com\u002Fsearch\u002Fdocs\u002Fappearance\u002Fai-features\">AI Mode and AI Overviews may use different models and techniques\u003C\u002Fa>, so the set of responses and links they show will vary. Two surfaces, one vendor, different citation behavior. Any measurement that combines engines into a single score averages out the very thing you need to see. This is also why \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fchatgpt-vs-perplexity-vs-gemini-how-each-ai-engine-cites-differently-and-how-to-optimize-for-each\u002F\">each engine rewards a slightly different content emphasis\u003C\u002Fa>.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fproducts\u002Fpixis-visibility\u002F\">Pixis Visibility\u003C\u002Fa> tracks each engine separately rather than reporting a blended figure, and runs 12 sessions per prompt because a single response is a single sample from a probabilistic system, not a reading of it.\u003C\u002Fp>\u003Ch3>\u003Cstrong>Variants worth tracking\u003C\u002Fstrong>\u003C\u002Fh3>\u003Cp>\u003Cstrong>Citation SOV\u003C\u002Fstrong> is the raw count. Your baseline.\u003C\u002Fp>\u003Cp>\u003Cstrong>Position SOV\u003C\u002Fstrong> weights by placement in the answer. Worth building once your baseline is stable.\u003C\u002Fp>\u003Cp>\u003Cstrong>Sentiment SOV\u003C\u002Fstrong> captures the context of the mention, not just its presence. Being cited as the cautionary example is not a win.\u003C\u002Fp>\u003Cp>\u003Cstrong>Revenue-weighted SOV\u003C\u002Fstrong> links citation to pipeline. Useful if your attribution can support it, and honest reporting means acknowledging when it cannot.\u003C\u002Fp>\u003Ch3>\u003Cstrong>Worked example\u003C\u002Fstrong>\u003C\u002Fh3>\u003Cp>Ten prompts, three engines, thirty responses. The system extracts every cited source across all thirty and counts 100 total category citations. Your domain appears in 30 of them. Your ASOV is 30%.\u003C\u002Fp>\u003Cp>That number is not good or bad. It is the mark you measure movement from, and it is only comparable to your own future readings using the same probe set, the same engines, and the same citation rule.\u003C\u002Fp>\u003Ch2>\u003Cstrong>Benchmarking: Why Borrowed Numbers Fail\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>The two Ahrefs studies that open this piece are the argument in miniature. A team with a large dataset and a transparent method could not hold a stable unit of analysis across two quarters, and said so themselves. A number imported from someone else’s study carries their scope, their parsing, and their moment. Yours will differ on all three.\u003C\u002Fp>\u003Cp>So treat any confidence range for a healthy ASOV the way you would treat any number with no dataset behind it. No engine publishes citation share by category. No independent body has fixed a denominator. The useful comparison is against yourself.\u003C\u002Fp>\u003Ch3>\u003Cstrong>Benchmark against yourself instead\u003C\u002Fstrong>\u003C\u002Fh3>\u003Cp>\u003Cstrong>Set a baseline before you optimize. \u003C\u002Fstrong>Run the probe set once, record it, and date it.\u003C\u002Fp>\u003Cp>\u003Cstrong>Hold the probe set steady. \u003C\u002Fstrong>Changing prompts mid-program resets your baseline. If you expand coverage, run new prompts as a separate cohort so the original trend line survives.\u003C\u002Fp>\u003Cp>\u003Cstrong>Compare against a named competitor set. \u003C\u002Fstrong>ASOV is zero-sum inside your category. The question is whether your share grows while a specific rival’s share shrinks.\u003C\u002Fp>\u003Cp>\u003Cstrong>Watch the direction over the level. \u003C\u002Fstrong>A brand at 12% climbing three points a quarter is better positioned than one at 35% sliding.\u003C\u002Fp>\u003Cp>\u003Cstrong>Segment by engine before concluding anything. \u003C\u002Fstrong>If your citations concentrate in one engine, that is a risk, and a blended number hides it.\u003C\u002Fp>\u003Ch2>\u003Cstrong>Why the Mechanism Rewards Coverage Over Position\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>Google confirms that both AI features may use a \u003Ca href=\"https:\u002F\u002Fdevelopers.google.com\u002Fsearch\u002Fdocs\u002Fappearance\u002Fai-features\">query fan-out technique, issuing multiple related searches across subtopics and data sources\u003C\u002Fa> while a response is generated, surfacing a wider set of links than a classic search would.\u003C\u002Fp>\u003Cp>The practical consequence is that you can be selected for a sub-query you never targeted and missed for the one you did. Per-keyword rank tracking underdescribes this by design, which is part of why \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fgeo-vs-seo-whats-actually-different-and-what-still-applies\u002F\">GEO and SEO measure success differently\u003C\u002Fa> even when they draw on the same content.\u003C\u002Fp>\u003Cp>It also explains why coverage beats position. A page that answers one question well competes for one sub-query. A cluster that answers the surrounding questions competes for many.\u003C\u002Fp>\u003Cp>One caution on tactics. Google’s guidance is explicit that \u003Ca href=\"https:\u002F\u002Fdevelopers.google.com\u002Fsearch\u002Fdocs\u002Fappearance\u002Fai-features\">there are no additional requirements to appear in AI Overviews or AI Mode\u003C\u002Fa>, nor are any other special optimizations necessary, and that no special schema.org structured data is needed for these features. Structured data remains worth implementing for the search features it drives, but a strategy sold on AI-specific markup is selling something Google says does not exist.\u003C\u002Fp>\u003Ch2>\u003Cstrong>What Being Cited Is Actually Worth\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>Pew Research Center \u003Ca href=\"https:\u002F\u002Fwww.pewresearch.org\u002Fshort-reads\u002F2025\u002F07\u002F22\u002Fgoogle-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results\u002F\">tracked 68,879 real Google searches from 900 US adults\u003C\u002Fa> in March 2025. Users who encountered an AI summary clicked a traditional result in 8% of visits, against 15% for those who did not. Sessions ended outright on 26% of summary pages, compared with 16% without.\u003C\u002Fp>\u003Cp>The same study found something a citation-first argument has to sit with honestly: users clicked links inside the summary itself just 1% of the time.\u003C\u002Fp>\u003Cp>Being cited is not a traffic channel. It is a placement at the moment a buyer decides, and it should be defended on those terms rather than dressed up as a click substitute; it is not. The teams reporting ASOV as a proxy for sessions are setting up a disappointment.\u003C\u002Fp>\u003Cp>Two limits on that data are worth stating. Pew covers Google AI Overviews only, not ChatGPT or Perplexity. And the summaries were collected in April 2025 for searches run in March, so they reflect a single surface at a singl moment.\u003C\u002Fp>\u003Ch2>\u003Cstrong>Strategies to Grow Your ASOV\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>\u003Cstrong>Publish what cannot be found elsewhere. \u003C\u002Fstrong>Proprietary data and original research give engines a reason to reach for you specifically. Everything else is available from whoever wrote it first.\u003C\u002Fp>\u003Cp>\u003Cstrong>Build coverage, not pages. \u003C\u002Fstrong>Fan-out rewards clusters that answer the surrounding questions, not isolated pages that answer one.\u003C\u002Fp>\u003Cp>\u003Cstrong>Answer before you elaborate. \u003C\u002Fstrong>Put the direct answer near the top. \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fseo-geo-and-aeo-what-they-are-how-they-differ-and-why-your-search-strategy-needs-all-three\u002F\">Answer-first structure\u003C\u002Fa> is one of the few requirements genuinely distinct to GEO rather than inherited from SEO.\u003C\u002Fp>\u003Cp>\u003Cstrong>Keep entity descriptions consistent. \u003C\u002Fstrong>How your brand is described across your own site and third-party sources feeds the model’s confidence in citing you.\u003C\u002Fp>\u003Cp>\u003Cstrong>Check that retrieval bots can reach you. \u003C\u002Fstrong>Access is the floor. Nothing above it matters if \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Frobots-txt-ai-crawlers-gptbot-perplexity-geo-audit-pixis-visibility\u002F\">crawlers are blocked in robots.txt\u003C\u002Fa>.\u003C\u002Fp>\u003Ch2>\u003Cstrong>90-Day Rollout\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>\u003Cstrong>Days 1 to 30: Baseline. \u003C\u002Fstrong>Build your probe set from real buyer language. Run it across your engine set. Record ASOV per engine, dated, with your citation rule written down. Note which domains are taking your share, including the ones that are not competitors. \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fhow-to-audit-your-ai-search-visibility-in-15-minutes\u002F\">A fast first-pass audit\u003C\u002Fa> is enough to establish where you stand before you invest in tooling.\u003C\u002Fp>\u003Cp>\u003Cstrong>Days 31 to 60: Close the largest gaps. \u003C\u002Fstrong>Take the prompts where competitors are cited and you are not. Restructure the answer to the top. Add the specific, verifiable claims that give an engine something to attribute. Fix retrieval access on the pages that matter.\u003C\u002Fp>\u003Cp>\u003Cstrong>Days 61 to 90: Re-run and compare. \u003C\u002Fstrong>Same probes, same engines, same rule. Compare against your day-30 baseline. Report direction, per engine, against your named competitors.\u003C\u002Fp>\u003Cp>\u003Cstrong>KPIs: \u003C\u002Fstrong>ASOV per engine, citation position within answers, share movement against a named competitor set, sentiment of cited mentions, coverage rate across the probe set.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fproducts\u002Fpixis-visibility\u002F\">Pixis Visibility’s GEO module\u003C\u002Fa> runs this cycle in one place: 12 sessions per prompt across four engines, a Citation Bank of what is being cited and why, a Competitor Matrix showing where share is moving, and briefs generated from each gap so the analysis produces a published page rather than a dashboard.\u003C\u002Fp>\u003Ch2>\u003Cstrong>Frequently Asked Questions\u003C\u002Fstrong>\u003C\u002Fh2>\u003Cp>\u003Cstrong>How is ASOV different from traditional share of voice?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>Traditional SOV measures presence: ad impressions, rankings, mentions. ASOV measures attribution: explicit citations where an engine has selected your domain as the basis for a claim. Presence tells you that you exist in the conversation. Attribution tells you that you are the source it rests on.\u003C\u002Fp>\u003Cp>\u003Cstrong>Which engines should I track?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>Track every engine your buyers use, separately. Google’s own documentation confirms that \u003Ca href=\"https:\u002F\u002Fdevelopers.google.com\u002Fsearch\u002Fdocs\u002Fappearance\u002Fai-features\">AI Overviews and AI Mode may use different models and techniques\u003C\u002Fa>, producing different links from the same company. Treating five engines as one channel produces a number that describes none of them.\u003C\u002Fp>\u003Cp>\u003Cstrong>What is a good ASOV score for my industry?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>There is no published benchmark, and anyone quoting a range is quoting something without a dataset behind it. The useful target is directional: your ASOV growing against your named competitor set on a stable probe set. If someone hands you an industry average, ask what the denominator was and when it was measured.\u003C\u002Fp>\u003Cp>\u003Cstrong>How do I increase citations in AI answers?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>Publish what cannot be sourced elsewhere, cover the topic broadly enough to survive fan-out, put answers before elaboration, keep entity descriptions consistent, and confirm retrieval bots can reach the pages. Then measure which of those moved your number, because the general advice is not the same as what works in your category.\u003C\u002Fp>\u003Cp>\u003Cstrong>Is ASOV a reliable ROI metric?\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>Not on its own, and claiming otherwise is not defensible yet. Pew’s data shows users click links inside AI summaries \u003Ca href=\"https:\u002F\u002Fwww.pewresearch.org\u002Fshort-reads\u002F2025\u002F07\u002F22\u002Fgoogle-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results\u002F\">just 1% of the time\u003C\u002Fa>, so citation is not a traffic proxy. It measures presence at the decision moment, which matters, and which the research has not yet connected to revenue with the kind of longitudinal evidence that claim would require.\u003C\u002Fp>\u003Cp> \u003C\u002Fp>\u003Cp> \u003C\u002Fp>",[],1784190355613]