In the early 2010s, SEO professionals relied on keyword rankings and Google Analytics traffic reports as the north star of digital performance. By the 2020s, the focus shifted to entity-based search, semantic relevance, E-E-A-T, and rich results. Now in 2025, we’re entering a new frontier: Large Language Model Optimization (LLMO)—a discipline that challenges the foundational KPIs we’ve trusted for decades. Large Language Model Optimization (LLMO) With generative AI tools like ChatGPT, Perplexity, Gemini, and Microsoft Copilot becoming preferred discovery layers, brands must adapt. These systems summarize, cite, and recommend content based on training data and live web results—but often bypass traditional search engines and eliminate the need for clicks. That’s where LLMO comes in, and more importantly, where LLMO-specific KPIs must evolve. LLMO-specific KPIs This article introduces a modern KPI framework to measure SEO success in this AI-first world—grounded in real-world use cases, future-forward tools, and a vision that transcends traffic and rankings. The Problem: Old Metrics, New Realities Traditional SEO success is often defined by organic traffic growth, keyword rankings, CTRs, and conversion rates. But when users get direct answers from LLMs—without visiting your site—those metrics offer an incomplete picture. Consider this: your content may be summarized or quoted in an LLM answer (e.g. Perplexity or Gemini AI Overviews), yet Google Search Console and GA4 won’t reflect this exposure. It’s a visibility win—but invisibly tracked. summarized or quoted in an LLM answer It’s a visibility win—but invisibly tracked. The rise of zero-click discovery means we need new ways to answer: zero-click discovery Are we being referenced by AI models?
Are we influencing the answers people receive from LLMs?
How often are we mentioned, cited, or summarized—even without a backlink? Are we being referenced by AI models? Are we influencing the answers people receive from LLMs? How often are we mentioned, cited, or summarized—even without a backlink? From SEO to LLMO: A New Optimization Layer LLMO (Large Language Model Optimization) is the art and science of ensuring your brand, content, and expertise are discoverable and favored in AI-generated responses. While rooted in SEO, it considers new layers of optimization, including: LLMO (Large Language Model Optimization) Semantic proximity in embeddings
Entity recognition in AI indexes
Influence on AI output and prompt results
Citation frequency in AI responses Semantic proximity in embeddings Entity recognition in AI indexes Influence on AI output and prompt results Citation frequency in AI responses The goal is no longer just to rank—but to exist and persist in the AI layer of search. exist and persist in the AI layer of search A 3-Level KPI Framework for Measuring SEO Visibility in LLMs World As search continues to evolve beyond blue links and into AI-generated answers, traditional KPIs just don’t cut it anymore. To help marketers adapt, I’ve put together a practical, 3-tiered framework based on insights from Iguazio’s LLM architecture notes, Jim Wang’s KPI analysis, and real-world observations from SEO platforms like Search Engine Land. Let’s break it down: Level 1: What You Can Track Today – The Application-Level KPIs Level 1: What You Can Track Today – The Application-Level KPIs These KPIs are your starting point. They’re tangible, often tool-supported, and tie directly into how your brand is being represented (or not) across AI platforms. AI Mentions & Citations AI Mentions & Citations AI Mentions & Citations Track when your brand or content appears in LLM responses across platforms like Perplexity, You.com, or even Bing Chat. KPI to track: “Monthly AI Mentions” or “Mentions by Topic” KPI to track: “Monthly AI Mentions” or “Mentions by Topic” Quoted Snippets in AI Answers Quoted Snippets in AI Answers Quoted Snippets in AI Answers LLMs often paraphrase or directly pull structured content—especially lists or definitions. Keep an eye out for these. KPI: “AI Snippet Frequency” KPI: “AI Snippet Frequency” Voice Assistant Visibility Voice Assistant Visibility Voice Assistant Visibility Are Siri, Alexa, or Google Assistant referencing your site when people ask questions? You can manually test prompts like: “What is [topic] according to [brand]?” KPI: “Voice Assistant Inclusion Rate” KPI: “Voice Assistant Inclusion Rate” LLM Share of Voice by Topic LLM Share of Voice by Topic LLM Share of Voice by Topic Benchmark how often your brand is mentioned compared to competitors in prompts within your niche. This gives you a sense of AI visibility dominance. KPI: “LLMO Share of Voice (Topic Cluster)” KPI: “LLMO Share of Voice (Topic Cluster)” AI Referral Traffic AI Referral Traffic AI Referral Traffic Yes, some AI tools actually drive traffic—especially Perplexity and Bing Chat. Track these under GA4 Source/Medium filters. KPI: “AI-Originated Sessions” KPI: “AI-Originated Sessions” Level 2: Going Deeper – The Model-Level Influence KPIs Level 2: Going Deeper – The Model-Level Influence KPIs These metrics look under the hood to assess how well your content fitsthe knowledge architecture of AI models—even if it’s not visibly cited yet. fits Embedding Proximity to Key Topics Embedding Proximity to Key Topics Embedding Proximity to Key Topics In NLP terms, how close is your content to dominant subject clusters? It’s a semantic signal that can affect inclusion. KPI: “Semantic Topic Proximity Score” KPI: “Semantic Topic Proximity Score” Prompt Responsiveness Prompt Responsiveness Prompt Responsiveness Run real prompts relevant to your vertical and see if you show up. Think: “Best FM platforms UK” or “Predictive maintenance in real estate.” KPI: “Prompt Appearance Rate” KPI: “Prompt Appearance Rate” Entity Confidence Score Entity Confidence Score Entity Confidence Score If your brand is structured as a recognized entity in tools like Kalicube or Google’s NLP API, you’re more likely to be trusted by LLMs. Kalicube Kalicube KPI: “Entity Trust Index” KPI: “Entity Trust Index” Level 3: The Long Game – Data-Level KPIs for Strategic Visibility Level 3: The Long Game – Data-Level KPIs for Strategic Visibility This layer deals with the invisible influence: how your presence is baked into the training data or surfaces across ecosystems in non-obvious ways. invisible influence Unlinked Brand Mentions Over Time Unlinked Brand Mentions Over Time Unlinked Brand Mentions Over Time Even without a link, consistent brand references in reputable sources build implicit authority. KPI: “Unlinked Mentions Velocity” KPI: “Unlinked Mentions Velocity” Citation Recurrence Score Citation Recurrence Score Citation Recurrence Score How often is the same piece of your content re-used or paraphrased in AI-generated summaries or answers? KPI: “Citation Reuse Rate” KPI: “Citation Reuse Rate” Structured Data Health Structured Data Health Structured Data Health LLMs need context. Schema markup makes your content more machine-readable and boosts ingestion potential. KPI: “Schema Coverage Score” KPI: “Schema Coverage Score” This framework isn’t just about measuring what’s happening—it’s about understanding your AI-era visibility footprint. The more you quantify it, the better you can position your brand to not just survive, but lead in this new search environment. understanding your AI-era visibility footprint not just survive lead Want help setting up tracking or building dashboards for these KPIs? Let me know—I’ve built custom solutions for brands facing the same shift. Case Study: UK-Based FacilityTech Brand's Visibility in LLMs Let me ground this with a real-world example from my own experience. At DIREK, a UK-based smart building tech company, we noticed a puzzling 15% drop in organic clicks over a three-month period—even though we had just rolled out a series of high-quality, long-form articles targeting key industry topics like asset tracking and compliance reporting. DIREK DIREK 15% drop in organic clicks over a three-month period 15% drop in organic clicks over a three-month period At first, it didn’t add up. But when we manually tested prompts in ChatGPT and other LLM-powered tools, we found something interesting: our content was actually being quoted—word for word—in AI-generated answers. In multiple prompts around “predictive maintenance tools” and “compliance reporting for facilities,” DIREK’s messaging and insights were showing up, just without a backlink or citation. This became a turning point. While our traditional SEO metrics showed a decline, our LLM visibility was increasing—we were influencing the conversation, just not seeing it reflected in GA4 or GSC. That’s when we realized we needed to rethink how we measure success in the AI-first search ecosystem. While our traditional SEO metrics showed a decline, our LLM visibility was increasing So, we adjusted our approach. Instead of relying solely on clicks and keyword rankings, we expanded our KPIs to reflect how our brand was surfacing in AI-driven environments. We began tracking: Monthly AI Mentions – how often DIREK was referenced in AI-generated responses Prompt Response Rate – how frequently our brand appeared in answer sets for relevant industry queries Structured Data Utilization Score – ensuring our schema markup made our content machine-readable and AI-friendly The impact was eye-opening. Within weeks, we noticed a shift: 23% of new lead inquiries specifically mentioned finding us through AI tools like ChatGPT and Perplexity, not traditional search engines. They weren’t clicking on a Google result—they were seeing DIREK cited as a source inside AI summaries. we noticed a shift: 23% of new lead inquiries specifically mentioned finding us through AI tools like ChatGPT and Perplexity This was a clear signal: in LLMs World, SEO isn’t just about driving traffic—it’s about earning presence in AI-generated conversations. Traditional metrics alone no longer tell the full story. As large language models shape how people access information, being visible to the model becomes just as important as being visible to the user. For forward-thinking SEO professionals, the message is clear: it’s time to expand the scoreboard. For forward-thinking SEO professionals, the message is clear: it’s time to expand the scoreboard. Tools You Can Start Using Today You don’t need to wait for the perfect LLM analytics platform to get started—there are already powerful tools at your fingertips that can give you early visibility into how your brand is performing in the AI-first search landscape. Tools like Google Search Console still provide invaluable insight into long-tail queries and featured snippets, which often influence LLM training data. Pair that with GA4’s exploration reports to track unusual traffic patterns that may hint at LLM exposure. For more proactive monitoring, platforms like Brand24, Mention, or Diffbot can alert you when your brand is cited online, even if it's not linked. And if you're feeling more technical, tools like SEMRush’s Brand Monitoring, Wikidata editors, or even a custom GPT scraper can help you start building a foundation of structured, recognizable, and AI-friendly content. The key is to stop waiting for perfect attribution—and start experimenting with the tools you already have. You don’t need a data science team to begin tracking LLMO performance. Here’s a quick stack: Tip: Run 5–10 core prompts weekly in different LLM tools and record mentions manually. Over time, you’ll see trends in which platforms “favor” your brand. While some KPIs in modern SEO management can be tracked with advanced tools, some of the most revealing insights still come from manually tracked data. Don’t dismiss the value of manual tracking—especially for metrics that aren’t yet captured by software. Embracing this process can uncover hidden patterns and strategic opportunities that automated dashboards might overlook. While some KPIs in modern SEO management can be tracked with advanced tools, some of the most revealing insights still come from manually tracked data. Don’t dismiss the value of manual tracking—especially for metrics that aren’t yet captured by software. Embracing this process can uncover hidden patterns and strategic opportunities that automated dashboards might overlook. Why This Matters for Global SEO & Digital Leadership SEO leaders are not just optimizing for search engines anymore—they’re optimizing for generative knowledge systems. generative knowledge systems Whether you're leading marketing at a SaaS firm, publishing digital magazines, or guiding an eCommerce giant, measuring success in 2025 means knowing: How your content influences AI output
When you’re trusted as a source—even invisibly
What actions users take after an AI-powered discovery How your content influences AI output When you’re trusted as a source—even invisibly What actions users take after an AI-powered discovery These new KPIs are crucial for: These new KPIs are crucial for: Investor and stakeholder reporting
Measuring brand authority in a zero-click world
Driving long-term discovery and digital footprint growth Investor and stakeholder reporting Measuring brand authority in a zero-click world Driving long-term discovery and digital footprint growth Challenges & Limitations Despite the exciting possibilities of SEO in the LLM era, the path forward is far from straightforward. One of the biggest hurdles is model opacity—large language models like ChatGPT or Gemini don’t publicly reveal the exact data sources they’re trained on, nor do they provide a clear methodology for how they select, prioritize, or paraphrase content. This creates a blind spot for SEOs trying to understand why or how their brand is (or isn’t) being surfaced in AI-generated answers. model opacity why how Another significant issue is the lack of tooling. Unlike traditional SEO metrics tracked in Google Search Console or GA4, there’s currently no consolidated platform that allows you to monitor brand or page mentions across LLMs. You may find some signal in tools like Diffbot, Sentione, or custom LLM scrapers, but nothing matches the maturity of SERP analytics. lack of tooling We also face entity bias: if your brand, site, or content hasn’t been semantically established as a trusted entity in structured data ecosystems (think schema markup, Wikipedia entries, Wikidata, or authoritative backlinks), there’s a real risk that the AI simply doesn’t “know” you exist. This can lead to outright omission or, worse, substitution with better-optimized competitors. entity bias Finally, there’s a growing attribution gap. Even when LLMs draw from your content, they often paraphrase rather than link—removing the traditional SEO payoff of referral traffic and visibility. Without clear citations, tracking ROI becomes more complex, and this pushes SEOs to think beyond traditional traffic models toward visibility, reputation, and entity strength as key success metrics. attribution gap It’s not all smooth sailing. Key challenges include: Opaque Models: LLMs don’t disclose all data sources or citation logic
Tool Gaps: No unified platform to track all LLM mentions
Bias Risks: If your entity isn’t recognized, you may be excluded
Attribution Gaps: AI may paraphrase without citation Opaque Models: LLMs don’t disclose all data sources or citation logic Opaque Models Tool Gaps: No unified platform to track all LLM mentions Tool Gaps Bias Risks: If your entity isn’t recognized, you may be excluded Bias Risks Attribution Gaps: AI may paraphrase without citation Attribution Gaps That said, pioneers in this space will shape the standardsthat others follow. The earlier you adapt, the greater your advantage. shape the standards Future Outlook: Building AI-First Content Strategy In the coming years, we may see: Google Search Console adding “AI Answer Visibility” as a native report


Brands bidding for LLM Answer Ads—a sponsored response in Gemini


LLMs offering “Verified Source Panels” similar to featured snippets


AI content ranking algorithms rewarding contextual accuracy over link authority
Preparing for that future starts now—with KPIs that reflect it. Google Search Console adding “AI Answer Visibility” as a native report Google Search Console adding “AI Answer Visibility” as a native report Brands bidding for LLM Answer Ads—a sponsored response in Gemini Brands bidding for LLM Answer Ads—a sponsored response in Gemini LLM Answer Ads LLMs offering “Verified Source Panels” similar to featured snippets LLMs offering “Verified Source Panels” similar to featured snippets AI content ranking algorithms rewarding contextual accuracy over link authority
Preparing for that future starts now—with KPIs that reflect it. AI content ranking algorithms rewarding contextual accuracy over link authority contextual accuracy over link authority Preparing for that future starts now—with KPIs that reflect it. If you're looking to stay ahead in the era of AI-driven search, don’t miss my article "Win SEO in the Age of LLMs: Strategies for AI Visibility." It breaks down how to adapt your SEO game for large language models and includes a step-by-step guide on how to track LLM-driven traffic using GA4. This is the kind of insight most SEOs overlook—make sure you’re not one of them. "Win SEO in the Age of LLMs: Strategies for AI Visibility. Win SEO in the Age of LLMs: Strategies for AI Visibility. Win SEO in the Age of LLMs: Strategies for AI Visibility. Final Thoughts: Stop Counting Clicks. Start Measuring Influence. In this AI-first search ecosystem, visibility is no longer confined to the SERP. Your brand can—and should—exist in AI answers, be cited in conversation, and influence decisions without a single click. Measuring LLMO success isn't about replacing your current SEO metrics. It’s about expanding them to capture the full spectrum of discoverability in 2025 and beyond. capture the full spectrum of discoverability “If your content shapes the answers people trust—you're winning. Even if they never visit your site.”

This story contains new, firsthand information uncovered by the writer.

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