--- title: "AI Search Analytics: How to Measure LLM Visibility (2026 Guide)" url: https://alicelabs.ai/en/insights/ai-search-analytics description: "Learn how to measure AI search visibility with 8 actionable steps: prompt sets, baseline citation audits, GA4, GSC, server logs, and LLMO tracking tools. Research-backed guide." lang: en --- Alice Labs (https://alicelabs.ai/en/) Services What we do (https://alicelabs.ai/#welcome)About Alice (https://alicelabs.ai/#who-we-are)Case (https://alicelabs.ai/en/case)Insights (https://alicelabs.ai/en/insights)Contact (https://alicelabs.ai/#email-form) AI Search & LLMO How-to Guide Fresh Last reviewed: 19 September 2026 · 18d ago # AI Search Analytics: How to Measure LLM Visibility ## TL;DR Quick Answer Cited by AI > Measure AI search visibility with five methods: manual prompt audits across 50-100 representative prompts, LLMO tracking tools (Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, LLMrefs), GA4 referrer analysis for chatgpt.com and perplexity.ai, Google Search Console for AI Overviews-adjacent queries, and server log analysis of AI crawler bot traffic. Traditional SEO analytics stop at the click. AI search analytics measure something different: whether ChatGPT, Perplexity, Claude, and Google AI Overviews cite your brand when users ask questions in your domain. This guide shows you the five measurement methods, the tools that automate them, and the methodology Alice Labs uses across 100+ Nordic enterprise implementations. AI search analytics is the practice of measuring brand visibility, citation frequency, and referral traffic across generative AI search systems including ChatGPT, Perplexity, Claude, and Google AI Overviews. It combines manual prompt audits, dedicated LLMO tracking tools, GA4 referrer analysis, Google Search Console monitoring, and server log analysis of AI crawlers to build a complete picture of AI-driven visibility. Time 1-2 days for setup, ongoing Difficulty Intermediate Tools Google Search Console (search.google.com/search-console), Google Analytics 4 (analytics.google.com), LLMO tracking tool (Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, or LLMrefs)… ## Before you start - Admin access to Google Analytics 4 and Google Search Console - Server log access or coordination with your hosting/devops team - Defined audience and product context to write a representative prompt set ## What you'll have at the end A working AI search analytics system with a baseline citation audit, automated tracking, configured GA4/GSC views, server log monitoring, and a quarterly review cadence — covering the same dimensions Alice Labs uses with 100+ Nordic enterprises. Image: Linus Ingemarsson - Author at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Written by Linus Ingemarsson: https://www.linkedin.com/in/linus-ingemarsson/ Image: Eric Lundberg - Reviewer at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Reviewed by Eric Lundberg: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Published May 6, 2026 · Updated September 19, 2026 12 min read ## 8-step process 0/8 complete 1. 01 #### Step 1: Define your prompt set (50-100 representative prompts) Build a structured set of 50-100 prompts your audience actually types into ChatGPT, Perplexity, Claude, and Google AI Overviews. Cover three layers: brand prompts (your name, your products), category prompts (the problem space you solve), and competitive prompts (alternatives, comparisons). Store them in a spreadsheet with columns for prompt text, intent, target page, and priority. 2. 02 #### Step 2: Run a baseline citation audit Manually query each prompt across the LLMs you care about and log the results. Record which sources are cited, your citation position if any, which competitors appear, and which pages on your site (if any) the LLM references. The baseline is the snapshot you compare every future quarter against. Without it, trend analysis is impossible. 3. 03 #### Step 3: Set up GA4 referrer filters for AI sources In Google Analytics 4, create a custom segment or comparison that filters traffic by source. Capture chatgpt.com, perplexity.ai, claude.ai, and gemini.google.com as referrers. ChatGPT, Perplexity, and Claude already pass referrer headers when users click through inline citations. Most teams already have this data — they just have not surfaced it. 4. 04 #### Step 4: Set up Google Search Console monitoring Use Google Search Console to track query-level performance for terms that surface AI Overviews. GSC has historically not broken out AI Overviews appearance separately as of 2024-2025. Watch for declining CTR on queries that still rank well — this often signals AI Overviews is now answering the query upstream of your blue link. 5. 05 #### Step 5: Choose an LLMO tracking tool Evaluate dedicated tools that automate prompt audits at scale. Real options include Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, and LLMrefs. Each tracks citation share, brand mentions, and competitor benchmarks across multiple LLMs. Run trials on your prompt set before committing — coverage of LLMs and pricing models vary significantly. 6. 06 #### Step 6: Analyze server logs for AI crawler traffic Pull server access logs and filter for known AI crawler user agents: GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended, and Bingbot. The volume and frequency of crawler hits indicate how aggressively each LLM is fetching your content. Pages crawled often but not cited are a signal to investigate entity clarity or extractability. 7. 07 #### Step 7: Build a measurement dashboard Consolidate citation share, GA4 AI referrer traffic, GSC query performance, and crawler activity into a single dashboard. Looker Studio, Metabase, or a simple Google Sheet all work for the first version. The point is one shared view — not best-in-class tooling. Stakeholders need a single number trend they can act on. 8. 08 #### Step 8: Run a quarterly review cycle Re-run the prompt audit every 90 days against the same prompt set. Track changes in citation share, new competitors appearing, prompts where you lost ground, and prompts where optimization moved the needle. The 90-day cadence matches typical SEO content refresh cycles and gives enough signal to separate noise from real movement. ## Key Takeaways - Aggarwal et al. (2024) found citation-rich content lifted generative-engine visibility by up to 40%. You cannot improve what you do not measure. - SparkToro's 2024 zero-click study reported roughly 60% of Google searches end without a click. Citation visibility — not just clicks — is now the primary KPI. - Google Search Console has historically not broken out AI Overviews appearance separately (as of 2024-2025). You need additional measurement layers to see AI visibility. - GA4 already shows ChatGPT, Perplexity, and Claude as referrers when users click through. Most teams have not configured the filters to surface this traffic. - Real LLMO tracking tools (Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, LLMrefs) automate prompt audits and citation tracking across multiple LLMs. - A measurement system without a stable prompt-set baseline is noise. The baseline is what makes quarterly trend analysis possible. 01 / 05 Step ## Why AI Search Analytics Is Different From SEO Analytics In short SEO analytics measure clicks, impressions, and rankings on result pages. AI search analytics measure citation frequency, brand mentions inside generated answers, and referral traffic from AI systems — most of which never appears in Google Search Console. Traditional SEO analytics centre on the search engine results page. You measure impressions, clicks, position, and CTR — all in Google Search Console or equivalent. AI search analytics measure something different: whether your brand is cited inside a generated answer. The user may never click. The value is the mention itself, in front of an audience asking a question you can answer. Three structural differences make AI search analytics its own discipline: - **No native search console.** ChatGPT, Perplexity, and Claude do not publish a webmaster tool that tells you when your site was cited. You have to build the measurement yourself. - **Citation share replaces ranking.** A single LLM answer cites 3-8 sources. You are competing for inclusion in that shortlist, not for a numbered position. - **Zero-click is the norm, not the exception.** The SparkToro 2024 study found roughly 60% of Google searches already end without a click. AI search amplifies this further. This is why measurement requires multiple methods stacked together. No single tool sees the full picture today. 02 / 05 Step ## The Five Measurement Methods That Cover AI Search Visibility In short Five complementary methods together cover AI search visibility: manual prompt audits, LLMO tracking tools, GA4 referrer analysis, Google Search Console monitoring, and server log analysis of AI crawlers. No single method captures the full picture. Each measures a different layer: 1. **Manual prompt audits.** The ground truth. You query each LLM with your prompt set and log what it cites. Slow, but it shows you exactly what your audience sees. 2. **LLMO tracking tools.** Automate the prompt audit at scale. Verified tools include Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, and LLMrefs. Tracking is only half the loop — pair it with our comparison of ai content optimization tools (https://alicelabs.ai/en/insights/ai-content-optimization-tools) to act on the citation gaps you find. 3. **GA4 referrer analysis.** When users click through an LLM citation, the referrer often passes through. Filter chatgpt.com, perplexity.ai, claude.ai, and gemini.google.com. 4. **Google Search Console.** Track queries where AI Overviews now appears in Google. Watch for impressions stable but CTR declining — a classic AI Overviews fingerprint. 5. **Server log analysis.** Filter access logs for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended, and Bingbot to see which AI systems are actively fetching your content. Methods 1 and 2 measure citation visibility (did the LLM mention you). Method 3 measures referral traffic (did anyone click through). Methods 4 and 5 measure indirect signals from Google and from crawler activity. Image: Linus Ingemarsson (https://alicelabs.ai/images/linus-ingemarsson.png) Image: Eric Lundberg (https://alicelabs.ai/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. Book a Discovery Call 03 / 05 Step ## Building a Prompt Set of 50-100 Representative Prompts In short A representative prompt set has three layers: brand prompts (your name and products), category prompts (the problem space), and competitive prompts (alternatives and comparisons). 50-100 prompts is enough to detect trends without becoming unmaintainable. The prompt set is the single most important artefact in your AI search analytics system. It defines what you measure. Get it wrong and the data is meaningless. Structure your prompt set in three layers: 1. **Brand prompts (15-25 prompts).** Your company name, your product names, "is [brand] legit", "[brand] vs [competitor]", "[brand] reviews", "[brand] pricing". These tell you whether the LLM knows you exist and what it thinks about you. 2. **Category prompts (25-50 prompts).** The questions your audience asks before they know any vendor exists. "How do I measure AI visibility", "best LLMO tools", "what is llms.txt". These are where you compete for citation share. 3. **Competitive prompts (10-25 prompts).** Direct comparisons and alternatives. "[competitor] alternatives", "best [category] tools", "[competitor] vs [other competitor]". These show whether you appear when users are evaluating options. Store the set in a spreadsheet with columns for prompt text, layer, intent, target page on your site, priority, and last-audit result. Version it. The prompt set will evolve — keep a changelog. Alice Labs practitioner team ## Not sure where you stand in AI search today? We benchmark your brand against your top competitors across 50-100 representative prompts on ChatGPT, Perplexity, Claude, and Google AI Overviews — the same methodology used in the Alice Labs LLMO Citation Benchmark. Request an AI search baseline audit 04 / 05 Step ## Choosing LLMO Tracking Tools In short Six verified LLMO tracking tools dominate the market in 2026: Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, and LLMrefs. Each automates prompt audits and citation tracking across multiple LLMs, with different strengths in coverage, pricing, and reporting depth. The LLMO tracking tool category emerged in 2024-2025 as brands realised they needed automated measurement. Six tools are consistently named in practitioner discussions: - **Profound.** Enterprise-focused citation tracking across major LLMs with brand and competitor benchmarking. Strong fit for organisations running large prompt sets. - **Otterly.ai.** Prompt audit automation with dashboards for citation share and source tracking. Often used as a first step into automated LLMO measurement. - **Ziptie.** Tracks brand visibility and citation patterns across LLMs, with a focus on actionable insights for content teams. - **AthenaHQ.** Citation and visibility tracking with competitive benchmarking across multiple generative engines. - **Peec AI.** LLMO measurement focused on prompt-set audits and trend analysis over time. - **LLMrefs.** Tracks references and citations across LLM responses, surfacing how brands appear in AI-generated answers. Run trials on your prompt set before committing. The tools differ on which LLMs they cover, how often they re-run prompts, how they handle brand mention vs explicit citation, and on pricing model. 05 / 05 Step ## The Alice Labs LLMO Citation Benchmark Methodology In short The Alice Labs LLMO Citation Benchmark tracks 100 SaaS brands quarterly across a fixed prompt set. The methodology — prompt-set baseline, quarterly audit, trend analysis — is the same we use across 100+ Nordic enterprise implementations. Alice Labs runs the LLMO Citation Benchmark across 100 SaaS brands on a quarterly cadence as part of our enterprise ai seo (https://alicelabs.ai/en/ai-seo) practice. The methodology is deliberately simple so it produces signal you can compare period-over-period. The three steps: 1. **Prompt-set baseline.** A fixed set of category and competitive prompts. The set does not change quarter to quarter — if it did, the data would not be comparable. 2. **Quarterly audit.** Every 90 days, each prompt is re-run across the same set of LLMs. Citations are logged with source URL, position, and competitive context. 3. **Trend analysis.** Quarter-over-quarter changes show which brands gained citation share, which lost it, and which new entrants started appearing in answers they previously did not. The same methodology underpins our enterprise client work. The difference at the brand level is the prompt set is bespoke to the client's category and competitive context. Two principles run through all of this. First, a stable prompt set is non-negotiable — change it and you lose comparability. Second, quarterly is the right cadence for trend analysis; weekly creates noise, annual is too slow to act on. Alice Labs practitioner team ## Want a baseline AI search audit on your own brand? Alice Labs runs the LLMO Citation Benchmark across 100 SaaS brands quarterly. We can run the same methodology — prompt-set baseline, quarterly audit, trend analysis — on your category and competitive set. Talk to Alice Labs about AI search analytics ## About the Authors & Reviewers Written by Image: Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Linus Ingemarsson (https://www.linkedin.com/in/linus-ingemarsson/) CEO & Co-Founder, Alice Labs CEO & Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements View profile: https://www.linkedin.com/in/linus-ingemarsson/ Linus Ingemarsson on LinkedIn: https://www.linkedin.com/in/linus-ingemarsson/ Email Linus Ingemarsson: linus@alicelabs.ai Reviewed by September 19, 2026 Image: Eric Lundberg - Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Eric Lundberg (https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs View profile: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Eric Lundberg on LinkedIn: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Email Eric Lundberg: eric@alicelabs.ai Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is AI search analytics? AI search analytics is the practice of measuring brand visibility, citation frequency, and referral traffic across generative AI search systems including ChatGPT, Perplexity, Claude, and Google AI Overviews. It combines manual prompt audits, LLMO tracking tools, GA4 referrer analysis, Google Search Console monitoring, and server log analysis. ### Does Google Search Console show AI Overviews data? Google Search Console has historically not broken out AI Overviews appearance separately as of 2024-2025. GSC remains useful for query-level performance and for spotting CTR declines that may indicate AI Overviews is now answering queries upstream, but it does not provide direct AI Overviews visibility data. ### How do I track ChatGPT, Perplexity, and Claude in GA4? Google Analytics 4 already captures ChatGPT, Perplexity, and Claude as referrers when users click through inline citations. Create a custom segment or comparison filtering by source for chatgpt.com, perplexity.ai, claude.ai, and gemini.google.com. Most teams have this data already and have simply not surfaced it. ### Which LLMO tracking tools are real? Verified LLMO tracking tools in 2026 include Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, and LLMrefs. Each automates prompt audits and citation tracking across multiple LLMs. Coverage of specific LLMs, refresh frequency, and pricing vary — trial on your own prompt set before committing. ### How many prompts should I track? 50-100 prompts is the right range for most brands. It is large enough to detect trends across brand, category, and competitive prompts, and small enough to manage and audit consistently every quarter. Larger sets create maintenance burden; smaller sets do not produce enough signal. ### How often should I re-run my AI visibility audit? Quarterly (every 90 days) is the right cadence for most brands. Weekly creates noise that obscures real trends, and annual is too slow to act on. The 90-day cycle aligns with typical content refresh cadences and gives enough signal to separate trend from noise. ### Can I do AI search analytics without a paid tool? Yes. A manual prompt audit, GA4 referrer filters, Google Search Console review, and server log analysis can all be done without paid tooling. Paid LLMO tools add automation, scale, and competitive benchmarking but are not strictly required for a working measurement system. ### What is the difference between a brand mention and a citation? A brand mention is any reference to your brand in an LLM response — even just by name. A citation is an explicit attributed source link, typically rendered as an inline reference in tools like ChatGPT search and Perplexity. Citations are higher-value because users can click through; mentions still build awareness and category association. Previous in AI Search & LLMO ### Site Architecture for AI Crawlers: Hub-and-Spoke for LLMs https://alicelabs.ai/en/insights/site-architecture-for-ai-crawlers Next in AI Search & LLMO ### AI Search Optimization for Legal & Professional Services 2026 https://alicelabs.ai/en/insights/ai-search-optimization-legal ## Further reading - GEO: Generative Engine Optimization (Aggarwal et al., 2024) (https://arxiv.org/abs/2311.09735)· arxiv.org - Google Search Console (https://search.google.com/search-console)· search.google.com - Google Analytics 4 (https://analytics.google.com/)· analytics.google.com - llms.txt — Answer.AI proposal (Jeremy Howard, Sep 2024) (https://llmstxt.org/)· llmstxt.org ## Related reading pillar ### AI Search Optimization: Complete Guide for 2026 Full playbook covering ChatGPT, Perplexity, Claude, and Google AI Overviews. 14 min https://alicelabs.ai/en/insights/ai-search-optimization-guide listicle ### Best LLMO Tools 2026 Reviews of Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, LLMrefs, and other LLMO tracking tools. 10 min https://alicelabs.ai/en/insights/best-llmo-tools-2026 comparison ### LLMO vs SEO: What's the Difference? Side-by-side comparison of LLMO and SEO — analytics, tactics, and measurement. 8 min https://alicelabs.ai/en/insights/llmo-vs-seo ## Sources 1. Aggarwal et al. — GEO: Generative Engine Optimization (arXiv:2311.09735, 2024) (https://arxiv.org/abs/2311.09735)(accessed 2026-05-06) 2. SparkToro / Datos — 2024 zero-click search analysis (https://sparktoro.com/blog/in-2024-we-finally-satisfyingly-measured-how-much-of-google-search-traffic-goes-to-google-properties/)(accessed 2026-05-06) 3. Jeremy Howard / Answer.AI — llms.txt proposal (September 2024) (https://llmstxt.org/)(accessed 2026-05-06) 4. Google — Search Console product documentation (https://search.google.com/search-console)(accessed 2026-05-06) 5. Google — Analytics 4 product documentation (https://analytics.google.com/)(accessed 2026-05-06) 6. Profound — LLMO citation tracking platform (https://www.tryprofound.com/)(accessed 2026-05-06) 7. Otterly.ai — AI search visibility tracking (https://otterly.ai/)(accessed 2026-05-06) 8. Alice Labs — LLMO Citation Benchmark methodology (100 SaaS brands, quarterly) (https://alicelabs.ai/en/ai-search)(accessed 2026-05-06) Next scheduled review: 2026-12-18 Alice Labs practitioner team Share Share on LinkedIn (https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fai-search-analytics)Share on X (https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fai-search-analytics&text=AI%20Search%20Analytics%3A%20How%20to%20Measure%20LLM%20Visibility%20(2026%20Guide)) ## Get in Touch! The lab usually responds within 24 hours. Need help with AI? Get in touch (https://alicelabs.ai/en/ai-consulting-services#contact-form) ## Structured data ```json [ { "@context": "https://schema.org", "@graph": [ { "@type": "Organization", "@id": "https://alicelabs.ai/#organization", "name": "Alice Labs", "alternateName": [ "Alice Labs AB", "AliceLabs" ], "legalName": "Alice Labs AB", "identifier": "559443-5470", "foundingLocation": { "@type": "Place", "name": "Stockholm, Sweden" }, "url": "https://alicelabs.ai", "logo": { "@type": "ImageObject", "@id": "https://alicelabs.ai/#logo", "url": "https://alicelabs.ai/images/alice-logo.png", "contentUrl": "https://alicelabs.ai/images/alice-logo.png", "width": 2000, "height": 2027, "caption": "Alice Labs" }, "image": { "@id": "https://alicelabs.ai/#logo" }, "description": "Alice Labs är en svensk AI-byrå som hjälper företag implementera AI - från strategi till skalning.", "slogan": "From AI strategy to measurable results.", "foundingDate": "2023", "email": "hej@alicelabs.ai", "telephone": "+46734157476", "address": { "@type": "PostalAddress", "streetAddress": "Hammarbybacken 27", "addressLocality": "Stockholm", "postalCode": "120 30", "addressCountry": "SE" }, "contactPoint": [ { "@type": "ContactPoint", "contactType": "customer service", "email": "hej@alicelabs.ai", "telephone": "+46734157476", "areaServed": [ "SE", "EU" ], "availableLanguage": [ "Swedish", "English" ] } ], "areaServed": [ { "@type": "Country", "name": "Sweden" }, { "@type": "Place", "name": "Europe" } ], "knowsAbout": [ "AI strategy", "AI implementation", "AI agents", "AI automation", "Generative AI", "AI governance", "AI training", "Machine learning", "Large language models", "RAG", "AI consulting", "Digital transformation", "AI search optimization", "LLMO", "AI for enterprise" ], "founder": [ { "@id": "https://alicelabs.ai/#linus" }, { "@id": "https://alicelabs.ai/#eric" } ], "sameAs": [ "https://www.linkedin.com/company/alicelabsai", "https://www.trustpilot.com/review/alicelabs.ai", "https://www.wikidata.org/wiki/Q140369570" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#linus", "name": "Linus Ingemarsson", "givenName": "Linus", "familyName": "Ingemarsson", "jobTitle": "Co-Founder", "description": "Co-founder of Alice Labs. Architects AI agent systems and automation in production for clients across financial services, media, and the public sector.", "url": "https://alicelabs.ai/en/linus-ingemarsson", "sameAs": [ "https://www.linkedin.com/in/linus-ingemarsson/", "https://www.wikidata.org/wiki/Q140369914" ], "knowsAbout": [ "AI agents", "agent orchestration", "AI implementation", "LangGraph", "RAG systems", "AI strategy", "enterprise AI", "AI search optimization", "LLMO", "Nordic AI ecosystem" ], "worksFor": { "@id": "https://alicelabs.ai/#organization" } }, { "@type": "Person", "@id": "https://alicelabs.ai/#eric", "name": "Eric Lundberg", "givenName": "Eric", "familyName": "Lundberg", "jobTitle": "Co-Founder", "description": "Co-founder of Alice Labs. Designs AI automation systems and agent workflows that remove repetitive work and make day-to-day operations more reliable.", "url": "https://alicelabs.ai/en/eric-lundberg", "sameAs": [ "https://www.linkedin.com/in/eric-lundberg-3530451bb/", "https://www.wikidata.org/wiki/Q140369978" ], "knowsAbout": [ "AI automation", "agent workflows", "AI integrations", "process automation", "knowledge systems", "AI engineering", "enterprise AI", "Nordic AI ecosystem" ], "worksFor": { "@id": "https://alicelabs.ai/#organization" } }, { "@type": "Person", "@id": "https://alicelabs.ai/#alice", "name": "Alice Holmgren", "givenName": "Alice", "familyName": "Holmgren", "jobTitle": "CEO", "description": "CEO of Alice Labs. Leads strategy and growth across the Nordic AI consulting market.", "url": "https://alicelabs.ai/en/alice-holmgren", "knowsAbout": [ "AI strategy", "AI consulting leadership", "business development", "Nordic AI ecosystem", "enterprise AI adoption", "AI program management" ], "worksFor": { "@id": "https://alicelabs.ai/#organization" } }, { "@type": [ "LocalBusiness", "ProfessionalService" ], "@id": "https://alicelabs.ai/#localbusiness", "name": "Alice Labs", "description": "AI-konsult i Stockholm. Vi hjälper företag implementera AI - från strategi till skalning. Boka möte för en kostnadsfri AI-genomgång.", "url": "https://alicelabs.ai", "logo": { "@id": "https://alicelabs.ai/#logo" }, "image": { "@id": "https://alicelabs.ai/#logo" }, "telephone": "+46734157476", "email": "hej@alicelabs.ai", "priceRange": "$$$", "currenciesAccepted": "SEK, EUR, USD", "paymentAccepted": "Invoice", "address": { "@type": "PostalAddress", "streetAddress": "Hammarbybacken 27", "addressLocality": "Stockholm", "postalCode": "120 30", "addressRegion": "Stockholms län", "addressCountry": "SE" }, "geo": { "@type": "GeoCoordinates", "latitude": 59.3018, "longitude": 18.1003 }, "areaServed": [ { "@type": "City", "name": "Stockholm" }, { "@type": "City", "name": "Göteborg" }, { "@type": "City", "name": "Malmö" }, { "@type": "City", "name": "Uppsala" }, { "@type": "Country", "name": "Sweden" } ], "openingHoursSpecification": [ { "@type": "OpeningHoursSpecification", "dayOfWeek": [ "Monday", "Tuesday", "Wednesday", "Thursday", "Friday" ], "opens": "08:00", "closes": "18:00" } ], "hasOfferCatalog": { "@type": "OfferCatalog", "name": "AI-tjänster", "itemListElement": [ { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-konsult" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-strategi" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-implementation" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-utbildning" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-agenter" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-automation" } } ] }, "knowsAbout": [ "AI-konsult", "AI-strategi", "AI-implementation", "AI-utbildning", "AI-agenter", "AI-automation", "Generative AI", "Machine learning", "RAG", "Large language models", "AI governance" ], "parentOrganization": { "@id": "https://alicelabs.ai/#organization" }, "sameAs": [ "https://www.linkedin.com/company/alicelabsai" ] }, { "@type": "WebSite", "@id": "https://alicelabs.ai/#website", "url": "https://alicelabs.ai", "name": "Alice Labs", "alternateName": [ "Alice Labs AB" ], "description": "AI consulting, implementation and training for businesses.", "publisher": { "@id": "https://alicelabs.ai/#organization" }, "inLanguage": [ "sv-SE", "en-US" ], "potentialAction": { "@type": "SearchAction", "target": { "@type": "EntryPoint", "urlTemplate": "https://alicelabs.ai/?q={search_term_string}" }, "query-input": "required name=search_term_string" } } ] }, { "@context": "https://schema.org", "@graph": [ { "@type": "HowTo", "@id": "https://alicelabs.ai/en/insights/ai-search-analytics#article", "headline": "AI Search Analytics: How to Measure LLM Visibility", "description": "Learn how to measure AI search visibility with 8 actionable steps: prompt sets, baseline citation audits, GA4, GSC, server logs, and LLMO tracking tools. Research-backed guide.", "url": "https://alicelabs.ai/en/insights/ai-search-analytics", "datePublished": "2026-05-06", "dateModified": "2026-09-19", "expires": "2026-12-18", "author": { "@id": "https://alicelabs.ai/#linus" }, "reviewedBy": { "@id": "https://alicelabs.ai/#eric" }, "dateReviewed": "2026-09-19", "publisher": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai", "logo": { "@type": "ImageObject", "url": "https://alicelabs.ai/images/alice-logo.png" } }, "image": { "@type": "ImageObject", "@id": "https://alicelabs.ai/en/insights/ai-search-analytics#hero-image", "url": "https://alicelabs.ai/images/og/og-home.jpg", "contentUrl": "https://alicelabs.ai/images/og/og-home.jpg", "width": 1600, "height": 900, "caption": "AI Search Analytics: How to Measure LLM Visibility (2026 Guide)", "creator": { "@id": "https://alicelabs.ai/#organization" }, "representativeOfPage": true, "license": "https://alicelabs.ai/terms" }, "mainEntityOfPage": { "@type": "WebPage", "@id": "https://alicelabs.ai/en/insights/ai-search-analytics" }, "inLanguage": "en", "articleSection": "ai-search", "keywords": "ai search analytics, measure ai visibility, track llm citations, ai search reporting, llmo measurement, llm analytics, ai citation tracking", "about": [ { "@type": "Thing", "name": "Why AI Search Analytics Is Different From SEO Analytics", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#ai-vs-seo-analytics" }, { "@type": "Thing", "name": "The Five Measurement Methods That Cover AI Search Visibility", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#five-measurement-methods" }, { "@type": "Thing", "name": "Building a Prompt Set of 50-100 Representative Prompts", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#building-prompt-set" }, { "@type": "Thing", "name": "Choosing LLMO Tracking Tools", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#choosing-llmo-tools" }, { "@type": "Thing", "name": "The Alice Labs LLMO Citation Benchmark Methodology", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#alice-labs-methodology" } ], "mentions": [ { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, { "@type": "Organization", "name": "Google", "url": "https://google.com" }, { "@type": "Product", "name": "ChatGPT", "url": "https://chatgpt.com" }, { "@type": "Product", "name": "Claude", "url": "https://claude.ai" }, { "@type": "Product", "name": "Google Gemini", "url": "https://gemini.google.com" }, { "@type": "Person", "name": "Eric Lundberg", "url": "https://linkedin.com/in/eric-lundberg-3530451bb" }, { "@type": "Organization", "name": "Linus Ingemarsson" }, { "@type": "Organization", "name": "Run a baseline citation audit" }, { "@type": "Organization", "name": "Set up GA4 referrer filters for AI sources" }, { "@type": "Organization", "name": "Set up Google Search Console monitoring" } ], "hasPart": [ { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Why AI Search Analytics Is Different From SEO Analytics", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#ai-vs-seo-analytics", "description": "SEO analytics measure clicks, impressions, and rankings on result pages. AI search analytics measure citation frequency, brand mentions inside generated answers, and referral traffic from AI systems — most of which never appears in Google Search Console." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The Five Measurement Methods That Cover AI Search Visibility", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#five-measurement-methods", "description": "Five complementary methods together cover AI search visibility: manual prompt audits, LLMO tracking tools, GA4 referrer analysis, Google Search Console monitoring, and server log analysis of AI crawlers." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Building a Prompt Set of 50-100 Representative Prompts", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#building-prompt-set", "description": "A representative prompt set has three layers: brand prompts (your name and products), category prompts (the problem space), and competitive prompts (alternatives and comparisons). 50-100 prompts is enough to detect trends without becoming unmaintainable." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Choosing LLMO Tracking Tools", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#choosing-llmo-tools", "description": "Six verified LLMO tracking tools dominate the market in 2026: Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, and LLMrefs. Each automates prompt audits and citation tracking across multiple LLMs, with different strengths in coverage, pricing, and reporting depth." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The Alice Labs LLMO Citation Benchmark Methodology", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#alice-labs-methodology", "description": "The Alice Labs LLMO Citation Benchmark tracks 100 SaaS brands quarterly across a fixed prompt set. The methodology — prompt-set baseline, quarterly audit, trend analysis — is the same we use across 100+ Nordic enterprise implementations." } ], "speakable": { "@type": "SpeakableSpecification", "cssSelector": [ "[data-speakable='true']", "[data-snippet='true']", "[data-section-answer='true']", ".quick-answer", "h1" ] } }, { "@type": "BreadcrumbList", "@id": "https://alicelabs.ai/en/insights/ai-search-analytics#breadcrumb", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "ai-search", "item": "https://alicelabs.ai/en/insights/ai-search" }, { "@type": "ListItem", "position": 4, "name": "AI Search Analytics: How to Measure LLM Visibility (2026 Guide)", "item": "https://alicelabs.ai/en/insights/ai-search-analytics" } ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#eric", "name": "Eric Lundberg", "jobTitle": "Co-Founder", "worksFor": { "@id": "https://alicelabs.ai/#organization" }, "knowsAbout": [ { "@type": "DefinedTerm", "name": "AI automation", "url": "https://www.wikidata.org/wiki/Q1322483" }, { "@type": "DefinedTerm", "name": "Workflow automation", "url": "https://www.wikidata.org/wiki/Q120427660" }, { "@type": "DefinedTerm", "name": "Retrieval-Augmented Generation", "url": "https://www.wikidata.org/wiki/Q117761563" }, { "@type": "DefinedTerm", "name": "Enterprise AI implementation" } ], "sameAs": [ "https://www.linkedin.com/in/eric-lundberg-3530451bb/", "https://www.wikidata.org/wiki/Q140369978" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#linus", "name": "Linus Ingemarsson", "jobTitle": "CEO & Co-Founder", "worksFor": { "@id": "https://alicelabs.ai/#organization" }, "knowsAbout": [ { "@type": "DefinedTerm", "name": "AI agent orchestration", "url": "https://www.wikidata.org/wiki/Q98678395" }, { "@type": "DefinedTerm", "name": "AI strategy" }, { "@type": "DefinedTerm", "name": "AI search optimization (LLMO)" }, { "@type": "DefinedTerm", "name": "Enterprise AI strategy" }, { "@type": "DefinedTerm", "name": "AI consulting leadership" } ], "sameAs": [ "https://www.linkedin.com/in/linus-ingemarsson/", "https://www.wikidata.org/wiki/Q140369914" ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "What is AI search analytics?", "acceptedAnswer": { "@type": "Answer", "text": "AI search analytics is the practice of measuring brand visibility, citation frequency, and referral traffic across generative AI search systems including ChatGPT, Perplexity, Claude, and Google AI Overviews. It combines manual prompt audits, LLMO tracking tools, GA4 referrer analysis, Google Search Console monitoring, and server log analysis." } }, { "@type": "Question", "name": "Does Google Search Console show AI Overviews data?", "acceptedAnswer": { "@type": "Answer", "text": "Google Search Console has historically not broken out AI Overviews appearance separately as of 2024-2025. GSC remains useful for query-level performance and for spotting CTR declines that may indicate AI Overviews is now answering queries upstream, but it does not provide direct AI Overviews visibility data." } }, { "@type": "Question", "name": "How do I track ChatGPT, Perplexity, and Claude in GA4?", "acceptedAnswer": { "@type": "Answer", "text": "Google Analytics 4 already captures ChatGPT, Perplexity, and Claude as referrers when users click through inline citations. Create a custom segment or comparison filtering by source for chatgpt.com, perplexity.ai, claude.ai, and gemini.google.com. Most teams have this data already and have simply not surfaced it." } }, { "@type": "Question", "name": "Which LLMO tracking tools are real?", "acceptedAnswer": { "@type": "Answer", "text": "Verified LLMO tracking tools in 2026 include Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, and LLMrefs. Each automates prompt audits and citation tracking across multiple LLMs. Coverage of specific LLMs, refresh frequency, and pricing vary — trial on your own prompt set before committing." } }, { "@type": "Question", "name": "How many prompts should I track?", "acceptedAnswer": { "@type": "Answer", "text": "50-100 prompts is the right range for most brands. It is large enough to detect trends across brand, category, and competitive prompts, and small enough to manage and audit consistently every quarter. Larger sets create maintenance burden; smaller sets do not produce enough signal." } }, { "@type": "Question", "name": "How often should I re-run my AI visibility audit?", "acceptedAnswer": { "@type": "Answer", "text": "Quarterly (every 90 days) is the right cadence for most brands. Weekly creates noise that obscures real trends, and annual is too slow to act on. The 90-day cycle aligns with typical content refresh cadences and gives enough signal to separate trend from noise." } }, { "@type": "Question", "name": "Can I do AI search analytics without a paid tool?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. A manual prompt audit, GA4 referrer filters, Google Search Console review, and server log analysis can all be done without paid tooling. Paid LLMO tools add automation, scale, and competitive benchmarking but are not strictly required for a working measurement system." } }, { "@type": "Question", "name": "What is the difference between a brand mention and a citation?", "acceptedAnswer": { "@type": "Answer", "text": "A brand mention is any reference to your brand in an LLM response — even just by name. A citation is an explicit attributed source link, typically rendered as an inline reference in tools like ChatGPT search and Perplexity. Citations are higher-value because users can click through; mentions still build awareness and category association." } } ] }, { "@context": "https://schema.org", "@type": "HowTo", "name": "AI Search Analytics: How to Measure LLM Visibility", "description": "Learn how to measure AI search visibility with 8 actionable steps: prompt sets, baseline citation audits, GA4, GSC, server logs, and LLMO tracking tools. Research-backed guide.", "totalTime": "12 min read", "step": [ { "@type": "HowToStep", "position": 1, "name": "Define your prompt set (50-100 representative prompts)", "text": "Build a structured set of 50-100 prompts your audience actually types into ChatGPT, Perplexity, Claude, and Google AI Overviews. Cover three layers: brand prompts (your name, your products), category prompts (the problem space you solve), and competitive prompts (alternatives, comparisons). Store them in a spreadsheet with columns for prompt text, intent, target page, and priority." }, { "@type": "HowToStep", "position": 2, "name": "Run a baseline citation audit", "text": "Manually query each prompt across the LLMs you care about and log the results. Record which sources are cited, your citation position if any, which competitors appear, and which pages on your site (if any) the LLM references. The baseline is the snapshot you compare every future quarter against. Without it, trend analysis is impossible." }, { "@type": "HowToStep", "position": 3, "name": "Set up GA4 referrer filters for AI sources", "text": "In Google Analytics 4, create a custom segment or comparison that filters traffic by source. Capture chatgpt.com, perplexity.ai, claude.ai, and gemini.google.com as referrers. ChatGPT, Perplexity, and Claude already pass referrer headers when users click through inline citations. Most teams already have this data — they just have not surfaced it." }, { "@type": "HowToStep", "position": 4, "name": "Set up Google Search Console monitoring", "text": "Use Google Search Console to track query-level performance for terms that surface AI Overviews. GSC has historically not broken out AI Overviews appearance separately as of 2024-2025. Watch for declining CTR on queries that still rank well — this often signals AI Overviews is now answering the query upstream of your blue link." }, { "@type": "HowToStep", "position": 5, "name": "Choose an LLMO tracking tool", "text": "Evaluate dedicated tools that automate prompt audits at scale. Real options include Profound, Otterly.ai, Ziptie, AthenaHQ, Peec AI, and LLMrefs. Each tracks citation share, brand mentions, and competitor benchmarks across multiple LLMs. Run trials on your prompt set before committing — coverage of LLMs and pricing models vary significantly." }, { "@type": "HowToStep", "position": 6, "name": "Analyze server logs for AI crawler traffic", "text": "Pull server access logs and filter for known AI crawler user agents: GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended, and Bingbot. The volume and frequency of crawler hits indicate how aggressively each LLM is fetching your content. Pages crawled often but not cited are a signal to investigate entity clarity or extractability." }, { "@type": "HowToStep", "position": 7, "name": "Build a measurement dashboard", "text": "Consolidate citation share, GA4 AI referrer traffic, GSC query performance, and crawler activity into a single dashboard. Looker Studio, Metabase, or a simple Google Sheet all work for the first version. The point is one shared view — not best-in-class tooling. Stakeholders need a single number trend they can act on." }, { "@type": "HowToStep", "position": 8, "name": "Run a quarterly review cycle", "text": "Re-run the prompt audit every 90 days against the same prompt set. Track changes in citation share, new competitors appearing, prompts where you lost ground, and prompts where optimization moved the needle. The 90-day cadence matches typical SEO content refresh cycles and gives enough signal to separate noise from real movement." } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "AI Search Analytics: How to Measure LLM Visibility", "description": "Learn how to measure AI search visibility with 8 actionable steps: prompt sets, baseline citation audits, GA4, GSC, server logs, and LLMO tracking tools. Research-backed guide.", "url": "https://alicelabs.ai/en/insights/ai-search-analytics", "datePublished": "2026-05-06", "dateModified": "2026-09-19", "creator": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "license": "https://creativecommons.org/licenses/by/4.0/", "isAccessibleForFree": true, "keywords": [ "ai search analytics", "measure ai visibility", "track llm citations", "ai search reporting", "llmo measurement", "llm analytics", "ai citation tracking" ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Related articles", "itemListElement": [ { "@type": "ListItem", "position": 1, "url": "https://alicelabs.ai/en/insights/ai-search-optimization-guide", "name": "AI Search Optimization: Complete Guide for 2026" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/best-llmo-tools-2026", "name": "Best LLMO Tools 2026" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/llmo-vs-seo", "name": "LLMO vs SEO: What's the Difference?" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 5, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Why AI Search Analytics Is Different From SEO Analytics", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#ai-vs-seo-analytics" }, { "@type": "ListItem", "position": 2, "name": "The Five Measurement Methods That Cover AI Search Visibility", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#five-measurement-methods" }, { "@type": "ListItem", "position": 3, "name": "Building a Prompt Set of 50-100 Representative Prompts", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#building-prompt-set" }, { "@type": "ListItem", "position": 4, "name": "Choosing LLMO Tracking Tools", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#choosing-llmo-tools" }, { "@type": "ListItem", "position": 5, "name": "The Alice Labs LLMO Citation Benchmark Methodology", "url": "https://alicelabs.ai/en/insights/ai-search-analytics#alice-labs-methodology" } ] } ] }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "AI Search & LLMO", "item": "https://alicelabs.ai/en/insights/ai-search" }, { "@type": "ListItem", "position": 4, "name": "AI Search Analytics: How to Measure LLM Visibility" } ] } ] ```