The "Share-of-Model" Framework: Measuring Brand Visibility in a Zero-Click AI Ecosystem

The Death of the Click and the Rise of the Answer

The fundamental contract of the internet—"I create content, you give me traffic"—is being rewritten. For two decades, B2B SaaS marketing relied on a deterministic funnel: a user searches for a keyword, clicks a blue link, lands on a blog, and eventually converts. That linear path is fracturing.

In the generative era, the search bar is becoming a chat interface. Users aren't looking for a list of links to research; they are looking for a synthesized answer. Recent data suggests that zero-click searches (where the user's query is satisfied on the results page or within the chat interface) are rapidly approaching, and in some verticals exceeding, 60% of all query volume. When a potential buyer asks ChatGPT, "What is the best automated SEO content generation tool for developers?" and the model answers with a summarized list, no click occurs. If your brand isn't in that summary, you don't exist.

This shift demands a new measurement framework. We can no longer rely solely on Rank Tracking or Click-Through Rate (CTR). We need to measure Share-of-Model (SoM): the probability of your brand appearing in the probabilistic output of an AI.

In this guide, we will unpack the Share-of-Model framework, how to measure it, and how to optimize your content strategy to dominate this new battlefield using Generative Engine Optimization (GEO).

What is Share-of-Model?

Share-of-Model (SoM) is a marketing metric that quantifies a brand's visibility within the generated responses of Large Language Models (LLMs) and Answer Engines. It measures the frequency with which a brand is cited, recommended, or used as an example in response to non-branded, categorical queries. Unlike Share-of-Voice, which measures advertising weight or organic search volume, Share-of-Model measures entity salience within the neural network of the AI, reflecting how strongly the model associates a specific problem with your brand as the solution.

The Three Pillars of Share-of-Model

To effectively measure and improve your standing in AI ecosystems, you must break down SoM into three distinct components. An aggregate score is useful, but diagnostic precision requires granularity.

1. Citation Frequency (The "Mention")

This is the most direct equivalent to "ranking." When a user prompts an engine with a commercial intent query (e.g., "Best GEO software for B2B SaaS"), does your brand appear in the list?

• High Frequency: You appear in 8 out of 10 regenerations of the answer.
• Low Frequency: You appear in 1 out of 10, or only when the prompt is extremely specific.

Citation frequency is driven by co-occurrence. If your brand name frequently appears alongside relevant topic keywords (e.g., "Steakhouse" appearing near "automated markdown publishing" and "entity SEO") across authoritative sources in the model's training data or retrieval index (RAG), the model forms a strong probabilistic association.

2. Sentiment Alignment (The "Vibe")

Appearing in the answer isn't enough if the context is negative or irrelevant. Sentiment alignment measures how the model describes you. Is it hallucinating features you don't have? Is it describing you as a "legacy tool" when you are an "AI-native platform"?

AI models are prediction engines. If the consensus of the web (its training data) describes your tool as "complex and expensive," the model will predict those words when describing you. Correcting this requires flooding the ecosystem with updated, clear positioning data—something tools like Steakhouse Agent facilitate by standardizing your brand narrative across the web.

3. Feature Association (The "Depth")

Does the model understand why you are the solution? Feature association measures the depth of the citation.

• Weak Association: "Steakhouse is a content tool."
• Strong Association: "Steakhouse is a markdown-first automated content platform that integrates with GitHub for developer-focused marketing teams."

High feature association protects you from commoditization. It ensures that when users ask follow-up questions (e.g., "Which one integrates with Git?"), your brand remains the sole survivor in the conversation context.

Measuring Your Share-of-Model: A Methodology

Since Google Search Console doesn't report on ChatGPT impressions, we have to be creative. Here is a step-by-step methodology for establishing a baseline SoM score.

Step 1: Define Your "Gold Set" Queries

Identify the top 20-50 questions your bottom-of-funnel prospects are asking. These should not include your brand name.

• Too Broad: "What is SEO?"
• Just Right: "How to automate topic clusters for B2B SaaS?" or "Best tools for Answer Engine Optimization."

Step 2: The "Run-and-Regenerate" Protocol

LLMs are non-deterministic; they may give different answers to the same prompt. To get statistically significant data, you cannot test once. You must test iteratively.

1. Select Engines: Choose your battlegrounds (e.g., ChatGPT-4o, Google Gemini, Perplexity, Claude 3.5).
2. Run the Prompt: Input a query from your Gold Set.
3. Record the Output: Note if your brand was mentioned (Yes/No), the rank (1st, 2nd, etc.), and the sentiment.
4. Regenerate: clear the context window and run it again 5–10 times.
5. Calculate: (Total Mentions / Total Runs) * 100 = Your Citation Probability %.

Step 3: Automating the Measurement

Manually checking is tedious. Technical marketers and growth engineers can automate this using Python scripts and API access to OpenAI and Anthropic. By running a script that pings the API with your Gold Set queries and parses the JSON response for your brand entity, you can build a dashboard that tracks SoM over time, alerting you when a competitor starts encroaching on your entity space.

Optimizing for Share-of-Model: The GEO Strategy

Once you have a baseline, how do you improve it? This is where Generative Engine Optimization (GEO) comes into play. Unlike SEO, which focuses on keywords and backlinks, GEO focuses on Information Gain, Entity Relationships, and Structured Data.

1. Optimize for "Information Gain"

LLMs prioritize content that adds new information to the knowledge graph. If your article is a generic rewrite of the top 10 search results, the AI has no reason to cite it—it already "knows" that information.

To increase citation frequency, your content must provide:

• Unique Data: Proprietary statistics or benchmarks.
• Unique Frameworks: Naming a methodology (like the "Share-of-Model Framework" itself).
• Expert Quotes: direct insights that don't exist elsewhere.

Steakhouse Agent is built on this principle. It doesn't just write words; it structures arguments and integrates unique brand data to maximize the "perplexity" (uniqueness) of the content, making it more attractive for retrieval.

2. The "Entity Home" Strategy

You must make it easy for the AI to understand who you are. This requires a dedicated "About" or "Entity" page on your site that serves as the single source of truth.

This page should be heavily marked up with JSON-LD Schema (Organization, SoftwareApplication, Brand). It should explicitly state:

• Who you are.
• What you do (using specific industry ontology).
• Who you are for (Audience).
• How you differ from competitors (Disambiguation).

When an AI crawls your site (or a search engine indexes it for RAG), this structured data acts as a direct injection of facts into the system, reducing the likelihood of hallucination.

3. Citation Velocity via Digital PR

LLMs trust patterns. If your brand is mentioned as the "best AEO tool" on your own blog, that's a claim. If it's mentioned on G2, Capterra, Reddit, TechCrunch, and industry newsletters within the same month, that's a pattern.

Focus your off-page efforts on sources that are known to be high-weight in training datasets. Wikipedia, reputable news outlets, and high-authority niche blogs carry disproportionate weight in establishing entity relationships.

Comparison: Traditional SEO vs. Share-of-Model (GEO)

The shift to SoM requires a fundamental change in mindset. Here is how the two frameworks compare.

Advanced Strategy: The "Inception" Technique

For advanced practitioners, specifically those targeting developer-marketers and technical founders, there is the "Inception" technique. This involves creating content that specifically answers the "long-tail" questions that LLMs struggle with.

LLMs often struggle with very recent or very specific technical integrations. By creating highly detailed, technical documentation or "cookbook" style articles (e.g., "How to configure Steakhouse Agent with Next.js 14 App Router"), you become the only viable source for the answer.

When a user asks a question about that specific integration, the AI (via RAG) is forced to retrieve your content because no other semantic match exists. This 100% Share-of-Model on niche queries builds a foundation of trust that bleeds over into broader queries over time.

Common Mistakes in Measuring SoM

• Mistake 1: Ignoring Personalization. AI responses are often personalized based on the user's chat history. Measuring SoM requires using "fresh" instances or API calls that strip away user history bias.
• Mistake 2: Obsessing over "Rank." In a chat interface, being mentioned second is often as good as being mentioned first, provided the sentiment is positive. The user is reading a paragraph, not scanning a list of 10 links.
• Mistake 3: Neglecting the "Why." If you are cited but the AI can't explain your value proposition, you have failed. You need to optimize for the justification of the citation, not just the name drop.

Conclusion: The First-Mover Advantage

The transition to zero-click search is not a possibility; it is a mathematical certainty driven by the efficiency of LLMs. Brands that cling to traditional CTR metrics will find themselves optimizing for a shrinking pie of traffic.

Adopting the Share-of-Model framework today gives you a first-mover advantage. By structuring your content for machines, defining your entities clearly, and focusing on high-information-gain publishing, you ensure that when the world asks AI for a solution, your name is the answer.

Ready to automate your Share-of-Model strategy? Steakhouse Agent helps you build the entity-rich, structured content layer that LLMs crave, turning your brand positioning into a dominant digital footprint without the manual grind.