How to Do a LLM Market Analysis: A Step-by-Step Guide

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 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.

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 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 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” 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 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 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.

Leveraging UGC for “Grounding”

One of the most powerful signals for LLMs is recency and verification. Models are constantly seeking “ground truth” to verify their outputs. This is where User-Generated Content (UGC) becomes a strategic asset. By systematically collecting high-quality reviews and Q&A, you generate a continuous stream of fresh, structured text that explicitly connects your products to consumer problems.

Consider utilizing Yotpo Reviews to automate the collection of this high-entropy data. Beyond the traditional conversion lift—where shoppers convert 161% higher after engaging with UGC—reviews serve as a continuous feed of “ground truth” for AI models. By syndicating this content to Google via Seller Ratings, you effectively train the model on your customers’ actual experiences, reducing the likelihood of hallucinations while increasing click-through rates by up to 17%. This dual benefit of traditional search visibility and generative AI grounding is critical for establishing trust in the answer engine.

Yotpo Reviews

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.

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. This decline is concentrated in informational queries where AI Overviews satisfy user intent without a click. Commercial intent (transactional) traffic remains stable.

Which tools are best for tracking Share of Model?

While the industry is evolving, tools like Semrush’s AI Overview tracking and Gravity Global’s frameworks are leading the way. However, for a true audit, manual testing using a “Golden Set” of prompts in a clean browser environment remains the most accurate method for specific queries.

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% 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.