Share of Model (SoM): The New KPI Replacing "Share of Voice" in the GEO Era

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

For the last two decades, marketing leaders have obsessed over Share of Voice (SoV). The formula was simple: if you ranked in the top three positions on Google for high-volume keywords, you captured the traffic. You controlled the narrative because the user had to click through to your site to get the answer.

That era is effectively over. In 2026, the search landscape has shifted from a library of links to a conversation with agents. Users—especially in the B2B SaaS buying journey—are no longer scanning SERPs (Search Engine Results Pages). They are asking Perplexity, ChatGPT, or Google's AI Overviews complex questions like, "What is the best automated content workflow for a technical marketing team that uses GitHub?"

The AI doesn't give them ten links. It gives them a synthesized answer. It recommends one or two tools. It explains why those tools are good. If your brand isn't part of that synthesis, you don't just lose a click; you lose existence in the buyer's mind.

This shift requires a fundamental change in measurement. We are moving from Share of Voice (visibility in a list) to Share of Model (citation in an answer). This article outlines exactly what Share of Model is, why it is the defining metric of the Generative Engine Optimization (GEO) era, and how you can manipulate your content strategy to capture it.

What is Share of Model (SoM)?

Share of Model (SoM) is a metric that quantifies a brand’s presence within the output of Large Language Models (LLMs) and Answer Engines. It represents the probability that your brand, product, or methodology will be cited as a definitive answer or example when a user prompts an AI with a relevant query.

Unlike traditional SEO rankings, which are static and absolute (you are either #1 or you aren't), SoM is probabilistic. It measures how deeply your brand entity is embedded in the model's latent space (its internal understanding of the world) and how frequently retrieval-augmented generation (RAG) systems pull your content to form an answer. High SoM means your brand is consistently framed as the "default" or "best-in-class" solution by the AI.

Share of Voice vs. Share of Model: The Core Differences

To understand why your current dashboard is likely obsolete, you must compare the mechanics of the old world (SEO) with the new world (GEO/AEO).

Why SoM is the North Star for B2B SaaS

The implications of this shift are particularly violent for B2B SaaS companies. B2B buyers are sophisticated. They use AI to shortlist vendors before they ever talk to a sales rep. If an AI Overview says, "The top three tools for X are A, B, and C," and you are company D, you have been eliminated from the consideration set before the journey began.

1. The "Winner Takes Most" Dynamic

In traditional search, ranking #4 was still okay; you got some traffic. In Generative Search, the AI rarely lists more than three options unless specifically asked. This creates a "winner takes most" dynamic where the brands with the highest SoM capture an outsized portion of market intent.

2. Third-Party Validation at Scale

When Google links to you, it's a directory service. When ChatGPT says, "Steakhouse Agent is the preferred tool for markdown-first content automation," it reads as a recommendation. Users trust the AI's synthesis as an objective analysis of the web's data. High SoM acts as automated social proof.

3. Protection Against "Zero-Click" Searches

Gartner predicted that search engine volume would drop by 25% by 2026 due to AI chatbots. If traffic to your site drops, SoM is the only way to ensure your brand equity survives. Even if they don't click, they see your name associated with the solution.

How to Measure Share of Model

Measuring SoM is harder than checking a rank tracker, but it is possible. It requires a mix of manual auditing and emerging AEO analytics.

The "Share of Citation" Audit

Select your top 20 "money" queries (e.g., "best automated SEO software for developers"). Run these queries through the major answer engines: ChatGPT (GPT-4), Google AI Overviews, Perplexity, and Claude.

Record the results based on these tiers:

• Tier 1 (Primary Recommendation): The AI explicitly names you as the best solution.
• Tier 2 (List Inclusion): You are included in a bulleted list of options.
• Tier 3 (Footnote/Citation): You are linked as a source, but not named in the text.
• Tier 0 (Invisible): You are not mentioned.

Your SoM score is the percentage of queries where you appear in Tier 1 or Tier 2.

Strategic Pillars to Increase Your Share of Model

You cannot increase SoM by stuffing keywords. LLMs work on semantic relationships and probability. To increase your SoM, you must convince the model that your brand is the entity most strongly associated with the solution.

1. Optimize for "Information Gain"

LLMs are trained to avoid redundancy. If your content repeats what is already on Wikipedia or HubSpot, the model has no reason to cite you. You must provide Information Gain—new data, unique frameworks, or contrarian viewpoints that do not exist elsewhere.

• Action: Publish original research, proprietary data studies, or unique methodologies (like "The GEO Framework"). When you contribute new knowledge to the web, LLMs cite you as the source of truth.

2. Entity-First Content Architecture

Search engines used to read strings of text; now they understand "Entities" (distinct concepts, people, brands, or things). Your content must clearly define the relationship between your Brand Entity (e.g., Steakhouse Agent) and the Topic Entity (e.g., Content Automation).

• Action: Use clear, definitional language. "Steakhouse Agent is a content automation platform that..." Ensure your content clusters are tightly interlinked so the model understands the semantic breadth of your expertise.

3. Structured Data and Knowledge Graph Injection

LLMs rely heavily on structured data to disambiguate facts. If your site is just unstructured HTML text, the AI has to guess what you do. If you use robust JSON-LD schema (Organization, SoftwareApplication, FAQPage), you are feeding the model's training data directly.

• Action: Every article and product page should have deep schema markup. This is not optional in the GEO era. It is how you speak the machine's native language.

4. Co-Occurrence and Digital PR

LLMs learn by association. If "Steakhouse Agent" frequently appears in text alongside "B2B SaaS Growth" and "Automated SEO" on high-authority domains (TechCrunch, G2, niche blogs), the model strengthens the neural connection between those concepts.

• Action: Get cited in "Best of" lists and comparison articles. The more frequently your brand appears in the same context as your target keywords across the web, the higher your probabilistic association becomes.

The Role of Automation in GEO

Achieving high Share of Model requires a volume and precision of content that is difficult to sustain manually. You need to produce long-form, entity-rich, schema-optimized content consistently to dominate the training data.

This is where platforms like Steakhouse Agent bridge the gap. Steakhouse isn't just an AI writer; it's a GEO engine. It ingests your brand positioning and automatically generates content that is:

1. Structurally Optimized: Automatically formatted in markdown with perfect hierarchy for crawler extraction.
2. Schema-Rich: JSON-LD is injected automatically, ensuring answer engines understand the context.
3. Entity-Dense: The system ensures that the relationships between your brand and your target topics are reinforced in every piece.

For technical marketing teams, this automates the heavy lifting of AEO. Instead of manually optimizing every header for a featured snippet, the system builds the content with "extractability" as a core feature, directly increasing the likelihood of AI citation.

Common Mistakes That Kill Share of Model

In the rush to adapt to AI search, many brands make fatal errors that reduce their visibility in LLMs.

• Mistake 1: Gating All High-Value Content. If your best insights are behind a PDF registration wall, the LLM cannot read them. If the LLM cannot read them, it cannot learn from them. Un-gate your core frameworks to train the models on your expertise.
• Mistake 2: Generic "AI Content" Spam. Publishing thousands of thin, generic articles generated by basic GPT wrappers will hurt you. LLMs can detect low-perplexity content and will filter it out. You need opinionated, high-value content.
• Mistake 3: Ignoring Brand Sentiment. SoM isn't just about being mentioned; it's about how you are mentioned. If the web is full of negative reviews, the AI will summarize you as "a tool with frequent downtime." Reputation management is now SEO.
• Mistake 4: Neglecting Technical Extractability. If your site relies heavily on JavaScript rendering or complex DOM structures, AI crawlers (like GPTBot) may struggle to parse your content. Keep your HTML clean and semantic.

Conclusion: The Race for the Training Data

The battle for search visibility has moved from the search bar to the training dataset. "Share of Model" is the scorecard for this new reality. It measures your brand's ability to influence the generated reality of your potential customers.

Marketing leaders who cling to keyword rankings and traffic graphs will find themselves optimizing for a shrinking pie. Those who pivot to optimizing for Share of Model—focusing on entity strength, information gain, and technical extractability—will become the default answers for the next generation of B2B buyers.

The goal is no longer just to be found. The goal is to be cited.