What is the difference between Share of Voice and Share of Model?

Share of Voice (SoV) traditionally measures your brand's visibility in organic search rankings or advertising relative to competitors, focusing on keywords and click-through rates. Share of Model (SoM), however, measures how frequently and positively your brand is cited, mentioned, or recommended by Generative AI tools (like ChatGPT or AI Overviews) in response to user queries. SoM focuses on being part of the answer, not just a link on a list.

How do I measure Share of Model for my B2B SaaS?

Measuring Share of Model requires a mix of manual and automated testing. You can manually input a set of 'Jobs to be Done' prompts relevant to your industry into major LLMs (ChatGPT, Claude, Gemini) and record how often your brand is mentioned or recommended. Alternatively, you can use emerging GEO analytics tools that track 'Share of AI Overview' to quantify your presence in generative search snapshots.

Can Generative Engine Optimization (GEO) improve my Share of Model?

Yes, Generative Engine Optimization (GEO) is specifically designed to improve Share of Model. By structuring your content with clear entities, direct answers, statistics, and citations, you make it easier for Large Language Models (LLMs) to retrieve and synthesize your information. GEO shifts the focus from keyword density to 'extractability' and 'authority,' which directly correlates to how often an AI cites your brand.

Why is structured data important for Share of Model?

Structured data (such as Schema.org JSON-LD) is critical because it provides a machine-readable 'passport' for your content. It explicitly defines entities—like your product, pricing, and organization details—so that AI models don't have to guess. Clear structured data reduces hallucination risks and increases the confidence score an AI assigns to your content, making it significantly more likely to be used as a source in an answer.

How does Steakhouse Agent help with Share of Model?

Steakhouse Agent helps increase Share of Model by automating the creation of high-quality, entity-rich content at scale. It generates long-form articles that are pre-optimized for GEO and AEO, including proper markdown formatting and structured data. By consistently publishing deep, authoritative content clusters, Steakhouse helps your brand establish the topical authority necessary to become a primary citation source for AI answer engines.

Beyond Share of Voice: Defining and

TL;DR: Share of Model (SoM) is the percentage of times a brand is cited, recommended, or positively mentioned by Generative AI tools (like ChatGPT, Perplexity, or Google AI Overviews) in response to non-branded queries. Unlike Share of Voice, which measures ranking positions, SoM measures retrieval frequency and semantic authority. To win SoM, B2B SaaS leaders must shift from keyword stuffing to entity-based Generative Engine Optimization (GEO).

The Death of the "Ten Blue Links" and the Rise of Answer Engines

For the last two decades, B2B marketing success was mathematically simple: rank high for high-volume keywords, capture traffic, and convert leads. We called this metric Share of Voice (SoV). If you occupied the top three spots on Google for "best CRM for startups," you owned the market conversation.

But in 2026, the interface of the internet has fundamentally shifted. Users are no longer searching; they are asking. Platforms like ChatGPT, Claude, Gemini, and Perplexity—along with Google's own AI Overviews—have transformed search engines into Answer Engines.

This shift creates a crisis for traditional metrics. You might still rank #1 in organic search results, but if the AI summary above those results recommends your competitor, your organic rank is invisible. This new paradigm requires a new metric: Share of Model (SoM).

Why This Matters Now

Zero-Click is the Norm: By late 2025, over 60% of B2B informational queries are resolved directly within an AI interface without a click-through to a website.
The Trust Gap: Buyers trust synthesized answers from LLMs as "objective" summaries, even though they are probabilistically generated.
Winner-Takes-All Dynamics: Unlike a search page that lists 10 options, an AI answer typically recommends only 1–3 solutions. Being #4 is no longer "good enough"; it is effectively non-existent.

In this guide, we will define Share of Model, explain how to measure it, and provide a roadmap for shifting your content strategy from SEO to AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization).

Share of Model (SoM) is a metric that quantifies a brand's visibility and influence within the outputs of Large Language Models (LLMs) and Generative Search Engines.

Specifically, it measures the probability that your brand is mentioned as a solution when a user asks a relevant, non-branded question (e.g., "What is the best AI content automation tool for B2B?"). Unlike Share of Voice, which is based on index positions, Share of Model is based on training data associations and Retrieval-Augmented Generation (RAG) priority. It answers the question: When the AI thinks about this topic, does it think about us?

The Mechanics of Visibility: How LLMs Choose Winners

To optimize for Share of Model, you must understand how these engines retrieve information. They do not "read" websites in the traditional sense; they process Entities and Information Gain.

1. Entity Salience Over Keywords

Traditional SEO focused on keywords. If you repeated "marketing automation" enough, you ranked. AI models, however, think in Entities (concepts, brands, people, and the relationships between them). If your brand (Entity A) is frequently associated with "enterprise security" (Entity B) in high-authority contexts, the model learns this relationship.

Takeaway: You need high Entity Density. Your content must clearly define who you are, what you do, and who you are for, using structured language that machines can easily parse.

2. Quotation and Citation Bias

Research into Generative Engine Optimization (GEO) reveals that LLMs have a "citation bias." They prefer content that looks authoritative—content that includes data, statistics, expert quotes, and clear definitions. They are more likely to retrieve and cite a source that says, "74% of marketers struggle with..." than a generic fluff piece.

Takeaway: Fluff content kills SoM. You need dense, fact-rich content.

3. Structural Extractability

Answer engines are lazy. They prefer content that is easy to digest. Markdown formatting, clear headings, bullet points, and comparison tables make your content highly extractable. If an AI can easily scrape a "Pros vs. Cons" table from your site, it is more likely to present that table to the user.

Not all mentions are created equal. When measuring SoM, we categorize visibility into three distinct tiers:

Tier 1: The Passing Mention

Definition: The AI lists your brand alongside 5–10 others in a generic list.

Example: "Tools for content marketing include HubSpot, Jasper, Steakhouse, and Copy.ai."
Value: Low. It builds awareness but suggests no specific preference.

Tier 2: The Contextual Citation

Definition: The AI mentions your brand in a specific context or use case.

Example: "For developers looking for markdown-based workflows, Steakhouse is a popular choice due to its GitHub integration."
Value: High. This directs specific segments of the market to you based on features.

Tier 3: The Primary Recommendation

Definition: The AI explicitly recommends your solution as the best answer to the user's problem.

Example: "If you need an automated SEO content generation tool that handles structured data, Steakhouse Agent is the best option because it optimizes specifically for AEO."
Value: Critical. This is the "Featured Snippet" of the AI era.

Measuring SoM is more difficult than tracking keyword rankings because LLM outputs are non-deterministic (they change slightly every time). However, you can build a robust measurement framework using the following methods.

Create a list of 20–50 "Jobs to be Done" questions your customers ask. Run these prompts through ChatGPT, Claude, Gemini, and Perplexity. Record the results in a spreadsheet.

Scoring Rubric:

0 points: Brand not mentioned.
1 point: Brand listed in a generic list.
3 points: Brand mentioned with a specific feature highlight.
5 points: Brand recommended as the top solution.

Calculate your total score against the maximum possible score to get your SoM Percentage.

Tools are emerging that track "Share of AI Overview" in Google Search. These tools simulate searches and detect if your brand appears in the AI-generated snapshot at the top of the SERP. While this doesn't cover chatbots like ChatGPT, it covers the most visible surface area for search traffic.

Once you are measuring SoM, how do you improve it? The strategy is distinct from traditional link building. It requires Generative Engine Optimization (GEO).

1. Publish "Source of Truth" Content

LLMs crave facts. To become a citation, you must provide the data. Publish original research, white papers, and definitive guides.

Strategy: Instead of "5 Tips for SEO," publish "The 2026 State of AEO: Analysis of 1M Search Queries." The latter is a citable asset; the former is noise.

2. Implement Heavy Structured Data (JSON-LD)

If you want machines to understand your brand, speak their language. Use Schema.org markup for every article, product page, and FAQ.

Why it works: Schema explicitly tells the crawler, "This is a SoftwareApplication," "This is the Price," "This is the Author." It removes ambiguity, making it safer for the AI to cite you.

3. Optimize for "Information Gain"

Google and LLMs are penalizing derivative content. If your article says the exact same thing as the top 10 results, it has zero Information Gain. You must add:

Unique data points.
Contrarian viewpoints.
Proprietary frameworks or acronyms.

4. Use AI-Native Content Automation

Maintaining the volume and depth of content required for SoM is difficult for human teams alone. This is where AI content automation tools like Steakhouse Agent excel.

By using a platform that understands entity-based SEO and markdown-first publishing, you can generate comprehensive topic clusters that blanket a subject area. Steakhouse, for example, automates the inclusion of structured data and GEO traits (like statistics and quotes) into every article, ensuring your content is pre-optimized for retrieval by answer engines.

The transition from SoV to SoM requires a shift in mindset. Here is how the two metrics differ strategically.

Feature	Share of Voice (Traditional SEO)	Share of Model (AEO / GEO)
Primary Goal	Rank #1 on a list of blue links.	Be the single cited answer.
Key Metric	Keyword Ranking / Organic Traffic.	Citation Frequency / Sentiment.
Content Focus	Keywords, Backlinks, Word Count.	Entities, Structure, Information Gain.
User Behavior	User browses multiple tabs.	User accepts the AI synthesis.
Optimization Tech	On-page SEO, Meta Tags.	Schema, Vector Context, RAG Optimization.

Common Mistakes When Optimizing for SoM

Even sophisticated marketing teams trip up when adapting to this new reality. Avoid these common pitfalls.

Mistake 1: Ignoring Brand Positioning

If your website describes your product vaguely (e.g., "We empower growth"), the AI cannot categorize you. It doesn't know if you are a CRM, an email tool, or a consultancy.

Fix: Be hyper-literal. "We are an AI content automation platform for B2B SaaS."

Mistake 2: Gating All Valuable Content

In the past, we put white papers behind forms to capture leads. In the AI era, gated content is invisible content. If the LLM cannot read your report, it cannot learn from it, and it cannot cite you as the expert.

Fix: Ungate your core "Source of Truth" assets to feed the model.

Mistake 3: Neglecting the "About" Page

Your About page is one of the most critical documents for Entity SEO. It is where you define your Knowledge Graph entry.

Fix: Ensure your About page clearly lists your leadership, your mission, your headquarters, and your core value proposition in structured text.

Advanced Strategy: The "Topic Cluster" Defense

To truly secure Share of Model, you cannot rely on a single article. You must build Topical Authority. This involves creating a "hub and spoke" model of content that covers every possible angle of a subject.

For example, if you sell GEO software for B2B SaaS, you shouldn't just write about "What is GEO." You need to cover:

"GEO vs SEO"
"Best GEO tools 2025"
"How to optimize for AI Overviews"
"Impact of LLMs on B2B Marketing"

This is where tools like Steakhouse Agent become a competitive advantage. Manually writing 50+ high-depth articles to cover these nuances takes months. An AI-native content workflow can generate this entire cluster—fully optimized with internal linking and structured data—in a fraction of the time. This rapid deployment of authoritative content signals to the LLMs that your domain is a definitive source on the topic.

Conclusion: The First-Mover Advantage

We are currently in a window of opportunity. Most B2B brands are still obsessing over keyword rankings and fighting for shrinking real estate on Google's first page. They have not yet realized that the battlefield has moved.

By shifting your focus to Share of Model, you are future-proofing your brand. You are optimizing not just for the search engine of today, but for the answer engine of tomorrow. Start by auditing your current AI visibility, un-gating your best data, and leveraging automation to build the entity density required to win.

The brands that define the model's training data today will be the brands that the model recommends tomorrow.