{"id":192393,"date":"2025-10-29T02:13:15","date_gmt":"2025-10-29T07:13:15","guid":{"rendered":"https:\/\/ahrefs.com\/blog\/?p=192393"},"modified":"2026-02-26T09:41:25","modified_gmt":"2026-02-26T14:41:25","slug":"brand-radar-methodology","status":"publish","type":"post","link":"https:\/\/ahrefs.com\/blog\/brand-radar-methodology\/","title":{"rendered":"Ahrefs Brand Radar Methodology: How we collect and model AI visibility data"},"content":{"rendered":"<p>Brand Radar lets you monitor and explore how visible brands are across AI and search. In this post, we break down how data is collected, modeled, and kept current.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"1. Short version\" data-section=\"short\">\n<h2><span style=\"font-weight: 400;\">Short version: The quick overview&nbsp;<\/span><\/h2>\n<\/div><\/div>\n<blockquote>\n<p data-path-to-node=\"6\"><b data-path-to-node=\"6\" data-index-in-node=\"0\">Ahrefs Brand Radar = Real Demand + Semantic Coverage<\/b><\/p>\n<\/blockquote>\n<p>Most AI visibility tools force a choice between behavioral relevance (what users actually ask) or semantic completeness (logical topic expansion).<\/p>\n<p>Ahrefs Brand Radar gives you both. You capture what users actually ask (real search demand) AND cover what the topic structurally requires (semantic completeness).<\/p>\n<blockquote><p><strong>Breadth + Depth = Maximum AI Surface Area.<\/strong><\/p><\/blockquote>\n<p>Here\u2019s how it works in practice: we collect keywords and SERPs from Ahrefs\u2019 database with over 100 billion keywords.<\/p>\n<p>To model how people naturally ask questions online, we expand queries using two different systems: Google\u2019s People Also Ask (PAA) and semantic fanout.<\/p>\n<p>Then run millions of these questions across AI platforms like ChatGPT, Perplexity, Gemini, Copilot, and Google\u2019s AI Overviews (+ AI Mode) and store their responses, so you can search through the text and links to see where your brand name (or any term) appears.<\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">Question sets are updated and tested in chatbots monthly, using a 90-day reporting window.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Metrics like AI Share of Voice (SOV) and Estimated Impressions model how visible a brand is across popular topics, based on real search interest. They show potential visibility, not actual audience reach.<\/span><\/li>\n<\/ul>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"1. Extended version\" data-section=\"extended\">\n<h2><span style=\"font-weight: 400;\">Extended version<\/span><\/h2>\n<\/div><\/div>\n<p><span style=\"font-weight: 400;\">Brand Radar helps companies understand how their brand shows up across AI and search.&nbsp;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It calculates AI Share of Voice (SOV) based on how often brands are mentioned or cited in responses from ChatGPT, Perplexity, Gemini, Microsoft Copilot, and Google\u2019s AI Overviews and AI&nbsp;Mode.<\/span><\/p>\n<h3><b>1. Data collection<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Brand Radar models real-world user behavior, rather than fabricating prompts.<\/span><span style=\"font-weight: 400;\"><br>\n<\/span><span style=\"font-weight: 400;\"><br>\n<\/span><span style=\"font-weight: 400;\">Queries are collected from Google\u2019s \u201cPeople Also Ask\u201d corpus and Ahrefs\u2019 110 billion keyword database (<a href=\"https:\/\/ahrefs.com\/big-data\">28.7 billion keywords tracked with positive search volume<\/a>), then<\/span>&nbsp;expanded into related sub-questions using two different systems: PAA and Fanout. They overlap sometimes, but they serve different purposes:<\/p>\n<ul data-path-to-node=\"17\">\n<li>\n<p data-path-to-node=\"17,0,0\"><strong>PAA (People Also Ask)<\/strong>: PAA is based on real keywords searched by real people. It reflects how users refine their queries and what follow-up questions they actually click on in Google. So it\u2019s optimized for behavioral relevance and engagement. It surfaces questions that users are genuinely curious about and frequently explore (read: real search demand).<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"17,1,0\"><strong>Fanout<\/strong>: Fanout is based on semantic relationships. It expands a query by analyzing meaning and topic structure, aiming to improve information retrieval and ensure broader topic coverage. Its goal is logical completeness rather than behavioral popularity.<\/p>\n<\/li>\n<\/ul>\n<p>For example, PAA may include questions that are popular but not directly helpful for answering the original question (e.g., for \u201cwhat is the first sign of kidney problems,\u201d PAA might suggest: \u201cWhat foods help repair kidneys and liver?\u201d or \u201cWhat not to drink if you have kidney problems?\u201d).<\/p>\n<p>Fanout, on the other hand, may include semantically important sub-questions that structurally help answer the core query but aren\u2019t frequently searched by&nbsp;users.<\/p>\n<div id=\"attachment_195490\" style=\"width: 2104px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-195490\" class=\"wp-image-195490 size-full\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-35-44-1.png\" alt=\"ChatGPT fanout queries in Ahrefs Brand Radar\" width=\"2094\" height=\"806\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-35-44-1.png 2094w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-35-44-1-680x262.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-35-44-1-768x296.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-35-44-1-1536x591.png 1536w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-35-44-1-2048x788.png 2048w\" sizes=\"auto, (max-width: 2094px) 100vw, 2094px\"><p id=\"caption-attachment-195490\" class=\"wp-caption-text\">ChatGPT fanout queries in Ahrefs Brand&nbsp;Radar<\/p><\/div>\n<p>By combining them, <strong>Ahrefs Brand Radar gives you the full picture of your AI visibility funnel<\/strong>.<\/p>\n<p><span style=\"font-weight: 400;\">Each query is executed in supported AI interfaces. We store the raw responses, and users can then search this corpus to surface citations (linked URLs) and mentions (string matches) for any&nbsp;term.<\/span><\/p>\n<div id=\"attachment_195477\" style=\"width: 2140px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-195477\" class=\"wp-image-195477 size-full\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-19-37.jpg\" alt=\"Ahrefs Brand Radar AI database size\" width=\"2130\" height=\"970\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-19-37.jpg 2130w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-19-37-680x310.jpg 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-19-37-768x350.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-19-37-1536x699.jpg 1536w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/10\/2026-02-26_16-19-37-2048x933.jpg 2048w\" sizes=\"auto, (max-width: 2130px) 100vw, 2130px\"><p id=\"caption-attachment-195477\" class=\"wp-caption-text\">Ahrefs AI Visibility Index<\/p><\/div>\n<p><span style=\"font-weight: 400;\"><b>Monthly query volume (approx.):<\/b><b><br>\n<\/b>ChatGPT \u2013 13.3 million<br>\nPerplexity \u2013 13.3 million<br>\nGemini \u2013 12.4 million<br>\nCopilot \u2013 13.3 million<br>\nAI Overviews \u2013 143 million<br>\nAI Mode \u2013 41 million<br>\n<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All prompts run through the free, publicly available web interfaces of ChatGPT, Gemini, Perplexity, Copilot, and other supported platforms to reflect typical user experiences.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Locale parameterization mirrors the ratio of queries by country and language in our keyword database, allowing proportional representation across markets.<\/span><\/p>\n<h3><b>2. Data modeling<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Because AI prompts are effectively infinite, Brand Radar focuses on high-demand, recurring topics that mirror search interest. Metrics are directional indicators, not exact traffic counts \u2013 best understood as modeled visibility signals, and not performance metrics.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Estimated Impressions<\/b><span style=\"font-weight: 400;\"> weight mentions by Google search volume to model potential exposure.<\/span><\/li>\n<\/ul>\n<p><b>Update cadence varies by platform:<\/b><\/p>\n<ul>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">ChatGPT, Perplexity, Gemini, and Copilot are refreshed monthly, using a 90-day reporting window for stability and consistency. Each report includes all questions still valid within the 90 days before the selected date.<\/span><span style=\"font-weight: 400;\"><br>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Google AI Overviews and AI Mode update continuously, aligning with keyword database refresh cycles.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Aggregated \u201cAll platforms\u201d reporting combines data from both groups.<\/span><\/li>\n<\/ul>\n<h3><b>3. Transparency and limitations<\/b><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Coverage bias<\/b><span style=\"font-weight: 400;\"> \u2013 Strongest in English; non-English markets represented proportionally.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Scope<\/b><span style=\"font-weight: 400;\"> \u2013 Chatbot usage is highly personalized, and the number of possible AI queries is effectively infinite. We prioritize the most common and high-demand questions based on popularity in our 110B keyword database and Google\u2019s People Also Ask corpus. This ensures coverage of the kinds of questions most likely to surface in AI search results, even though long-tail or niche prompts may not always be included.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Anomalies \u2013 <\/b><span style=\"font-weight: 400;\">LLMs occasionally generate hallucinated or malformed links. We do not filter out hallucinated or malformed links, as they reflect real model output.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cadence<\/b><span style=\"font-weight: 400;\"> \u2013 Update frequency by platform is described in the Data Modeling section.<\/span><\/li>\n<\/ul>\n<h3><b>4. How to interpret the&nbsp;data<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Brand Radar is best suited for:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Benchmarking brand visibility and AI Share of Voice (SOV)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Comparing competitor coverage across AI platforms<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identifying co-citation patterns and visibility gaps<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">It is not a substitute for audience measurement or traffic analytics. Think of it as a media-visibility audit, showing what appears in AI and search \u2013 not <\/span><i><span style=\"font-weight: 400;\">who<\/span><\/i><span style=\"font-weight: 400;\"> saw&nbsp;it.<\/span><\/p>\n<h3><b>5. Data foundation<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Brand Radar builds on Ahrefs\u2019 data infrastructure:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">28.7 billion keywords filtered from 110 billion discovered<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This foundation ensures Brand Radar combines verified search data with transparent, modeled AI visibility \u2013 staying true to Ahrefs\u2019 focus on accuracy and real-world behavior.<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Brand Radar lets you monitor and explore how visible brands are across AI and search. In this post, we break down how data is collected, modeled, and kept current. Ahrefs Brand Radar = Real Demand + Semantic Coverage Most AI<span class=\"ellipsis\">\u2026<\/span><\/p>\n<div class=\"read-more\">Read more \u203a<\/div>\n<p><!-- end of .read-more --><\/p>\n","protected":false},"author":136,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[333],"tags":[],"coauthors":[345],"class_list":["post-192393","post","type-post","status-publish","format-standard","hentry","category-product-blog","odd"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Ahrefs Brand Radar Methodology: How we collect and model AI visibility data<\/title>\n<meta name=\"description\" content=\"Brand Radar lets you monitor and explore how visible brands are across AI and search. 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