--- Title: "How to Do a LLM Market Analysis: A Step-by-Step Guide" Date: "2026-02-05T21:15:47+00:00" --- For two decades, market analysis meant optimizing for discovery—fighting for a slot on page one. As we settle into 2026, we have transitioned from search-and-retrieve to *ask-and-answer*. Modern AI engines don’t just list options; they read, reason, and synthesize bespoke responses. If your brand isn’t cited in that synthesis, you risk being overlooked by the modern buyer. The goal is no longer just ranking. It is *Inclusion*. This guide details the methodology to measure and master your Share of Model. ## **Key Takeaways: How to Do a LLM Market Analysis: A Step-by-Step Guide** - **Shift to “Answer Engines”:** We have moved from a retrieval model to a synthesis model. Your goal is not just to be indexed, but to be cited in the AI’s generated answer. - **Measure “Share of Model” (SOM):** This is the new Share of Voice. It measures how often, prominently, and favorably your brand appears in AI-generated responses. - **Prioritize Brand Web Mentions:** Recent data shows that “Brand Web Mentions” correlate[ **3x stronger**](https://ahrefs.com/blog/ai-overview-brand-correlation/) with AI visibility than traditional backlinks. - **Focus on “Inclusion Rate”:** Your primary audit metric is the percentage of relevant prompts where your brand is explicitly mentioned by name. - **Optimize for “Resolution”:** AI models prefer “high-entropy” content—specific data points, statistics, and verified reviews—over vague marketing claims. - **Leverage UGC for Grounding:** User-Generated Content (reviews and photos) provides the “freshness” signals that LLMs rely on to verify your brand’s current status. - **Bring Strategy to Execution:** Auditing your Share of Model across black box ecosystems only provides the first half of a modern market analysis. Platforms like **[Yotpo Discover](https://www.yotpo.com/discover/)** translate conversational visibility metrics into automated agent action across on-site technical readiness, AI-ready content, and authentic off-site shopper signals. Meet the Tool That Gets Your Products Recommended by AI [ Check it out ](https://www.yotpo.com/discover/) ## **The Paradigm Shift: From Search Engines to Generative Engines** To analyze your market position effectively, you must first understand the machinery that determines it. A market analysis of an LLM is effectively an audit of a “black box” system that combines retrieval, reasoning, and generation. Understanding these components is critical for interpreting why a brand is visible or invisible. ### **From Retrieval to Reasoning (The RAG Workflow)** The core mechanism powering modern search experiences—whether in Google’s AI Overviews, Perplexity, or ChatGPT’s web-browsing mode—is **Retrieval-Augmented Generation (RAG)**. A deeper understanding of RAG is essential, as market analysis is essentially a test of how well your brand survives each stage of this process. The workflow consists of three distinct phases: 1. **Retrieval:** The system searches its index or the live web to find “context chunks” relevant to the user’s prompt. *Challenge: Is your content technically structured (Schema) so the retriever identifies it?* 2. **Reasoning:** The LLM evaluates the retrieved chunks for relevance, factual accuracy, and sentiment. It filters out low-quality or contradictory information. *Challenge: Is your content “citation-worthy”?* 3. **Generation:** The model synthesizes the surviving information into a coherent natural language response. *Challenge: Do you appear in the consensus of the generated narrative?* Research highlights that this process creates a “winner-take-all” dynamic. In a traditional search engine, the difference between result #1 and result #3 is click-through rate. In a generative engine, the difference is often **inclusion vs. exclusion**. If your brand’s value proposition is unclear, the model simply omits you to maintain the coherence of its answer. ### **The “Zero-Click” Reality** For the e-commerce sector, the implications of this shift are acute. Buying journeys are complex and research-heavy. Historically, this behavior drove significant traffic to blogs and whitepapers. However, current data indicates a change in the flow of information. Buyers now turn to generative AI tools for the initial “shortlisting” phase. According to the **Semrush 2025 AI Overviews Study**, the presence of AI summaries in search results stabilized at approximately[ **15.69% of all queries**](https://www.semrush.com/blog/semrush-ai-overviews-study/) by late 2025. More critically, the study revealed a **1,295% explosion in navigational queries** triggering these overviews. This means that even when a user specifically searches for your brand name, the AI is likely intercepting the intent, summarizing your reviews and pricing before the user ever clicks your link. If a user asks, *“What is the best loyalty app for a Shopify Plus store?”*, the AI generates a recommendation, synthesizing reviews and feature sets into a concise paragraph. If your brand is not mentioned in this “Zero-Click” interaction, you may miss the prospect before they ever visited a website. ### **The “Black Box” Challenge** Unlike SEO, where we have tools like Search Console, LLM providers offer limited transparency into why a specific source was cited. This opacity means that market analysis must rely on **inference** and **correlation**. We cannot see the “PageRank” of a ChatGPT response. Instead, we must correlate external signals—like web mentions and sentiment—with the output. We are analyzing the brand’s footprint in the *vector space* of the model, not just its footprint on the server. ## **Defining the New Metrics: Share of Model (SOM)** To effectively analyze the LLM market, we must discard obsolete KPIs. “Rankings” are irrelevant when there are no positions. “Traffic” is a lagging indicator. The industry has coalesced around a new primary metric: **Share of Model (SOM)**. ### **What is Share of Model?** The concept of Share of Model (SOM) was formally introduced by researchers at **INSEAD** in mid-2025. It is defined as a measure of “how often, prominently, and favorably brands appear in AI-generated responses.” SOM is critical because LLMs act as the new gatekeepers of discovery. The research highlights extreme fragmentation in SOM across different models. A brand is not “visible in AI” generally; it is visible in specific models based on their training data. For example, the INSEAD study found that the detergent brand **Ariel** held a nearly[ **24% SOM**](https://knowledge.insead.edu/marketing/meet-model-how-market-llms-and-sell-humans) on Meta’s Llama model but less than 1% on Google’s Gemini. This dictates that a comprehensive market analysis must be multi-model. ### **The Gravity Global Measurement Framework** To operationalize SOM, we need a granular measurement stack. **Gravity Global’s** report on “Measuring AI and Zero-Click Impact” proposes a robust framework that breaks SOM down into four measurable layers: - **Layer 1: Inclusion Rate (“Are we showing up?”)** This measures the percentage of relevant prompts where the brand is explicitly mentioned by name. A market leader should aim for an Inclusion Rate of 60-80% in their core category. - **Layer 2: Citation Rate (“Are we being sourced?”)** This measures the percentage of responses that link to the brand’s owned assets (blogs, documentation) as the source of truth. A high Inclusion Rate with a low Citation Rate means the AI knows *who* you are (likely from third-party reviews) but doesn’t trust *what you say*. - **Layer 3: Accuracy Score (“Is the info correct?”)** This is a qualitative audit. Is the pricing current? Are the integrations listed actually supported? An inaccurate mention is often worse than no mention at all. - **Layer 4: Share of Synthesized Voice (“Do we dominate the narrative?”)** This measures the brand’s prominence relative to competitors within the answer. Being listed #1 with a “Highly Recommended” adjective is significantly more valuable than being listed #5 as an “Alternative.” Gravity Global emphasizes that we must[ **“measure influence, not just interaction”**](https://www.gravityglobal.com/blog/measuring-ai-and-zero-click-impact-on-brands) in this clickless environment. ### **The Human-AI Gap** The INSEAD research also introduces the **Human vs. AI Awareness Matrix**. This helps researchers classify their brand’s current standing: - **Cyborgs (High Human Awareness, High AI Visibility):** The ideal state. These brands have successfully translated their offline reputation into digital signals the AI can read. - **High-Street Heroes (High Human, Low AI):** Legacy brands relying on offline reputation. These brands face a critical risk of **losing market relevance** if they do not digitize their brand signals. They are famous to people, but invisible to the algorithm. - **AI Pioneers (Low Human, High AI):** Brands that have optimized for data and technical schema but lack genuine “brand love” or offline market share. - **Ghosts (Low Human, Low AI):** Brands struggling to gain traction in either reality. ## **The Drivers of AI Visibility: It’s Not Just SEO** Before conducting the manual audit, it is crucial to understand *why* the AI chooses Brand A over Brand B. Traditional wisdom would suggest backlinks and Domain Authority (DA) are the drivers. However, the latest data refutes this. ### **Brand Web Mentions vs. Backlinks** The most significant finding comes from **Ahrefs’ AI Overview Brand Correlation Study**, which analyzed 75,000 brands to determine ranking factors. The results show a dramatic decoupling of traditional link metrics from AI visibility. - **Brand Web Mentions:** Correlation of **0.664** (Strongest Signal). - **Branded Anchors:** Correlation of **0.527**. - **Domain Rating (DR):** Correlation of **0.326** (Weak Signal). - **Backlinks:** Correlation of **0.218** (Very Weak Signal). **Key Insight:** The AI model is reading the internet, not just crawling the link graph. It builds a probabilistic understanding of a brand’s authority based on **Semantic Proximity**—how often the brand name appears in the same context as relevant topic keywords. We are looking for “Share of Discussion,” not just links. As Ahrefs notes, brands in the top quartile for web mentions earned, on average,[ **169 AI mentions**](https://ahrefs.com/blog/ai-overview-brand-correlation/) compared to just 14 for the next quartile down. This 10x gap suggests a “tipping point” effect: you need a critical mass of consensus data before the AI feels confident enough to cite you. ### **The “Consensus” Model** This phenomenon is best described as the **Consensus Model**. LLMs are essentially prediction engines designed to generate the most probable answer. They prioritize brands with consistent cross-web narratives. If G2, Capterra, Reddit, and major industry blogs all describe your software as “enterprise-ready,” the AI adopts that consensus as fact. If the narrative is fragmented—some sources say “SMB tool,” others say “Enterprise”—the AI’s confidence score drops, and it excludes the brand to prevent hallucination. ### **Knowledge Graphs and Data Consistency** **Yext’s Visibility Brief** adds another layer: the **Knowledge Graph**. AI agents rely on structured data to verify entities. A Knowledge Graph structures your business data (products, locations, pricing) so AI platforms can recognize and surface it. If a brand’s information is inconsistent across the web (e.g., different pricing on the pricing page vs. a directory listing), the AI cannot resolve the entity. According to Yext,[ **86% of AI citations**](https://www.yext.com/blog/2025/12/why-ai-citations-matter-more-than-clicks) come from brand-managed sources like websites, listings, and reviews. This reframes visibility as an operational challenge: you must maintain a “Single Source of Truth” that feeds the AI the correct data points. Without a clear Knowledge Graph, you are leaving the AI to guess—and an AI that guesses often hallucinates or ignores you entirely. ## **Step-by-Step Guide: Conducting Your LLM Market Analysis** With the theory established, we now move to execution. This section outlines a comprehensive, four-phase workflow for conducting a professional LLM market analysis. ### **Phase 1: Taxonomy & Prompt Engineering (The Setup)** You cannot analyze the entire internet. You must define a representative “Universe of Queries” that reflects your customer’s journey. This is often referred to in AI training as a **“Golden Set”**—a curated collection of inputs used to benchmark model performance. 1. **Define Entity Clusters:** Identify the 3-5 core “Entities” your business owns. For example: “Loyalty Programs,” “Customer Retention,” or “UGC Platforms.” 2. **Construct the ‘Golden Set’:** Develop a dataset of **20–30 prompts** per cluster. These must mirror conversational intent, not keyword-ese. - *Discovery:* “What are the best loyalty apps for scaling Shopify stores?” - *Comparison:* “Compare Yotpo vs \[Competitor\] for enterprise brands.” - *Use-Case:* “How can I increase repeat purchase rate using referral software?” - *Brand Safety:* “Is \[Brand\] worth the price? What are the complaints?” 3. **Select Target Models:** A standard analysis set includes **Google AI Overviews** (Mass market), **ChatGPT** (Business research), and **Perplexity** (Deep research). ### **Phase 2: The Audit Execution (The Data Gathering)** Execute the prompts in each selected model. Crucially, use a “clean” environment (Incognito mode or specialized tools) to prevent personalization bias. For each response, log the following data points: - **Mentioned?** (Yes/No) - **Rank/Position:** (e.g., 1st of 5). - **Sentiment:** (Positive/Neutral/Negative). - **Citations:** (List of URLs cited as sources). - **Adjectives Used:** (e.g., “Expensive,” “Robust,” “Easy-to-use”). **Calculate your Baseline:** “In the ‘Loyalty’ category, we appeared in 12/20 prompts (60% Inclusion Rate). In ‘Referrals’, we appeared in 2/20 (10% Inclusion Rate).” ### **Phase 3: The Gap Analysis (The Insight)** Once you have the data, analyze the *why*. - **The “Citation Authority” Audit:** When the AI mentioned your competitor, where did it get the info? Classify sources into **High-Trust** (G2, Major News, Wikipedia) and **Low-Trust** (Forums). If competitors are being cited via G2 reports and you are not, that is a gap in your Digital PR strategy. - **The “Resolution” Audit:** Does the AI provide a “high resolution” answer for your brand? Does it list specific features (“AI-powered segmentation”) or vague fluff (“great tool”)? If your brand description is vague, it means your owned content lacks the **information density** required for extraction. ### **Phase 4: Strategic Reporting (The Output)** Synthesize the data into a report for stakeholders. - **Executive Summary:** “We are a ‘High-Street Hero’—known by humans but invisible to AI.” - **Risk Assessment:** “Our pricing data is hallucinated in 30% of ChatGPT queries.” - **Opportunity:** “We can capture the ‘Enterprise’ narrative by targeting 3 specific industry reports referenced by Perplexity.” ## **Optimization Strategies: From Audit to Action** The ultimate goal of the analysis is to improve the metrics. Based on the 2025-2026 research, here are the three primary levers for optimization to move from “Invisible” to “Inclusion.” ### **Digital PR as Technical SEO** Since **Brand Web Mentions** are the strongest correlation to AI visibility (0.664 correlation), Digital PR is no longer just for brand awareness—it is a technical necessity. You need to “Surround the Sound.” Identify specific entities (e.g., “Subscription Management”) and aggressively pitch guest posts, expert commentary, and data studies to publications that *already rank* for those terms. You need to co-locate your brand name with the topic keyword in as many authoritative text bodies as possible to train the model on the association. ### **Optimization for “Resolution” and “Citation-Worthiness”** The **GEO (Generative Engine Optimization)** research indicates that content modifications—like adding statistics, quotations, and unique data—can improve visibility significantly. Audit your core product pages. Do they contain unique data? (e.g., “Customers see a 15% lift in LTV”). Replace marketing fluff (“We help you grow”) with “Resolution-dense” facts (“Our platform processes 5M API calls daily with 99.99% uptime”). This makes the content “sticky” for the reasoning layer of the LLM. ### How Yotpo Discover Helps Brands Win Algorithmic Consensus ![Yotpo Discover Page Screenshot](https://www.yotpo.com/wp-content/uploads/2026/01/Yotpo-Discover-Screenshot-1-scaled.png "Yotpo Discover Screenshot 1 scaled How to Do a LLM Market Analysis: A Step-by-Step Guide 1")[Yotpo Discover]() Closing the citation gap uncovered during an LLM market analysis requires more than cleaner schema or better content alone. It requires a system that works on every layer generative models evaluate, from your underlying codebase structure to the third-party signals AI engines use to verify trust. That operational intersection is exactly the gap [Yotpo](https://www.yotpo.com/discover/) [ Discover](https://www.yotpo.com/discover/) was built to close. Discover is the first AI visibility platform built specifically for the complex reality of commerce. While generic software tools analyze search keywords broadly, Discover accounts for intricate e-commerce variables, including hero versus non-hero SKUs, fluid product lifecycles, and cross-channel regional intents. Instead of merely leaving your marketing team with a static readiness score, it tracks how your products surface across ChatGPT, Gemini, and Google AI Overviews, uncovers exactly why a model recommended a competitor over your business, and deploys three specialized autonomous execution agents to close your coverage gaps. The platform operates across three specific commerce vectors to turn visibility tracking into automated action: - **Site Readiness:** The Onsite Agent continuously scans your storefront codebase to automatically identify and repair technical indexing errors, poor internal link structures, and broken product detail page schemas, ensuring AI search crawlers can parse your catalog attributes with confidence. - **Off-Site Authority:** The Content Agent rapidly generates SEO and AEO-ready blog posts and strategic outreach outlines grounded in real customer reviews and order histories to answer the granular sub-queries generated by query fan-out. - **Verified Shopper Mobilization:** The Activation Agent maps out the specific Reddit threads, retail marketplaces, and community forums that AI engines actively cite for validation, prompting your actual customer base to share authentic experiences directly on those exact platforms. The engine powering this platform is a built-in data moat formed by over a decade of SKU-level signals from Yotpo Reviews and Yotpo Loyalty. Rather than generating generic AI content fluff, Discover leverages this foundation of authentic shopper voices to build a reliable depository of facts that models inherently trust. Brands like David Protein and Beekman 1802 utilize this agentic infrastructure to align their operations with model behavior. To move past passive data monitoring and start optimizing, you can evaluate your brand’s baseline visibility score at [commerce-gpt.](https://commerce-gpt.yotpo.com/) ### **The “Knowledge Graph” Defense** To secure your Accuracy Score and prevent hallucinations, you must manage your entity data. As recommended by **Yext**, implement robust Schema markup (Organization, Product, FAQ) on your site. Ensure that your “About Us” and “Pricing” pages are essentially “machine-readable” fact sheets. This reduces the computational “effort” required for the AI to verify your details, increasing the likelihood of inclusion. ## **Risks and Reputation: The “Promise and Peril”** No market analysis is complete without a risk assessment. As noted in the **Harvard Business School** case study “What Google, Lego, and Other Brands Know About the Promise and Peril of AI” (July 2025), visibility is a double-edged sword. ### **Brand Alignment Risk** The study cites **Lego** as an example where AI-generated content clashed with the brand’s core identity of “human craftsmanship.” For a SaaS company, the equivalent risk is an AI summary that frames your “Enterprise Platform” as a “SMB Tool.” Check the adjectives used in your Phase 2 Sentiment Analysis. If the AI calls you “cheap” or “basic” when you are positioning as “premium,” you have a critical brand alignment issue. This often stems from training data drawn from forums rather than your official documentation. ### **The Hallucination Hazard** The **Intuit/TurboTax** example—where an AI assistant gave wrong tax advice—demonstrates the liability of incorrect synthesis. Your market analysis must flag any “High-Risk” queries where the AI provides *confident but wrong* instructions on how to use your software. These require immediate content intervention, such as publishing a “Correction” or “Official Guide” page optimized to override the bad data. ## **Conclusion** The transition from SEO to **Generative Engine Optimization (GEO)** is not merely a change in acronyms; it is a fundamental shift in the physics of information. We have moved from a scarcity model (10 spots on Page 1) to a consensus model (what the AI believes to be true). To conduct a successful LLM Market Analysis, you must adopt **Share of Model** as your North Star metric, accept that it is fragmented, and focus on **Inclusion** and **Citation**. By following the step-by-step methodology outlined in this guide, you can illuminate the black box, turning the opacity of AI into a measurable, optimizable competitive advantage. The goal is no longer just to be found; it is to be *synthesized*. Meet the Tool That Gets Your Products Recommended by AI [ Check it out ](https://www.yotpo.com/discover/) ## **FAQs: How to Do a LLM Market Analysis: A Step-by-Step Guide** ### **What is the difference between Share of Voice and Share of Model?** Share of Voice (SOV) typically measures visibility in traditional advertising or organic search rankings (links). Share of Model (SOM) measures how often a brand is mentioned in the *text* of an AI-generated answer. SOM is qualitative (sentiment/accuracy) as well as quantitative, whereas SOV is primarily quantitative. ### **Is organic search dead in 2026?** No, but it is contracting. Recent large-scale data analysis of 40,000 top US websites shows that organic search traffic is down[ **2.5% year-over-year**](https://searchengineland.com/organic-search-traffic-down-yoy-data-467748). This decline is concentrated in informational queries where AI Overviews satisfy user intent without a click. Commercial intent (transactional) traffic remains stable. ### **How can an e-commerce brand accurately audit its visibility across generative search engines?** Conducting an AI audit requires prompting platforms like ChatGPT, Gemini, and Google AI Overviews with your priority commercial queries to track your baseline Share of Model or readiness score relative to competitors. However, running this manually fails to scale across complex e-commerce variables like product lifecycles and hero versus non-hero SKUs. [Yotpo](https://www.yotpo.com/discover/) [ Discover](https://www.yotpo.com/discover/) automates this process by functioning as a complete AI visibility platform built specifically for commerce. Instead of merely leaving your team with a static score, it deploys specialized Onsite, Content, and Activation agents to actively take steps to close the structural, content, and community citation gaps that the audit uncovers. You can run a free, initial baseline audit for your storefront at [commerce-gpt.](https://commerce-gpt.yotpo.com/) ### **How often should I conduct an LLM market analysis?** Given the volatility of AI models (which change temperature and training data frequently), it is recommended to conduct a “Spot Check” monthly and a full “Deep Dive Audit” quarterly. **Semrush** data shows that AI trigger rates can fluctuate by[ over 20%](https://www.semrush.com/blog/semrush-ai-overviews-study/) in a single month. ### **Can I optimize my content for specific LLMs like ChatGPT vs. Gemini?** Yes. Different models rely on different training data. **Google Gemini** leans heavily on Google’s index and YouTube data, while **Perplexity** prioritizes academic papers and high-authority news sources. Your “Gap Analysis” (Phase 3) will reveal which sources you need to target for each model. ### **Why is my brand invisible in AI Overviews despite high SEO rankings?** This is the “Inclusion Gap.” You likely have high Domain Authority but low “Brand Web Mentions” or “Semantic Proximity.” The AI sees your links but doesn’t see enough *discussion* about your brand to include it in a synthesized answer. You need more Digital PR and off-site coverage. ### **Does Schema markup help with LLM visibility?** Absolutely. Schema markup (structured data) helps the “Retrieval” phase of the RAG workflow. It makes your content machine-readable, reducing the chance of hallucination and increasing the “confidence score” the model assigns to your data. ### **How do I fix “Hallucinations” about my brand pricing?** If an AI is quoting old pricing, you must update your owned assets immediately and then use **Digital PR** to push that new data to third-party sites (like G2 or Capterra). The AI needs to see the new data corroborated across multiple high-trust sources to override its old training data. ### **Is “Zero-Click” traffic bad for my business?** Not necessarily. While it reduces session volume, “Zero-Click” visibility builds **Brand Recall**. If a user sees your brand cited as the expert answer three times, they are more likely to search for your brand directly (Branded Search) when they are ready to buy. It shifts the attribution to later in the funnel. ### **What is “Information Density” and why does it matter?** Information Density (or Entropy) refers to the amount of unique, specific facts in your content. AI models prefer to cite sources that provide concrete data (numbers, dates, proper nouns) rather than vague marketing language. Increasing density increases your Citation Rate.