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

Share-of-Voice (SoV) is a traditional marketing metric that measures a brand's visibility in advertising or organic search results relative to competitors, typically based on keyword rankings and ad spend. Share-of-Model (SoM), however, specifically measures a brand's presence within the output of Generative AI and Large Language Models. It quantifies how often an AI cites, recommends, or utilizes a brand as a source when answering relevant user prompts, focusing on probabilistic inclusion rather than deterministic rankings.

How can I measure my brand's Share-of-Model accurately?

Measuring Share-of-Model is currently more qualitative than quantitative due to the lack of unified analytics for LLMs. However, you can estimate it by conducting systematic "prompt auditing." This involves running a standard set of buyer-intent prompts (e.g., "Best B2B software for X") across major models like ChatGPT, Gemini, and Perplexity, and recording the frequency and sentiment of your brand's mentions. Additionally, tracking referral traffic specifically from AI-powered search engines serves as a strong proxy for visibility.

Does Schema markup really impact LLM visibility?

Yes, Schema markup (structured data) is critical for LLM visibility. While LLMs are trained on unstructured text, retrieval systems (RAG) that power current AI search engines rely heavily on structured data to understand the relationships between entities. By using JSON-LD to explicitly define your product, pricing, and organization, you reduce ambiguity, making it significantly easier for the AI to parse your content and confidently cite it as a factual source within its generated answer.

Why is markdown preferred for Generative Engine Optimization?

Markdown is preferred for Generative Engine Optimization (GEO) because it is a lightweight, text-based formatting syntax that strips away the heavy code overhead found in complex HTML/CSS structures. LLMs and web scrapers can process markdown much faster and with greater accuracy because the hierarchy (headers, lists, bold text) is semantically clear. This "clean" code approach ensures that the core information is easily extractable, increasing the likelihood that an AI model will ingest and utilize the content correctly.

Can small startups compete with enterprise brands for Share-of-Model?

Absolutely. In fact, startups often have an advantage in Share-of-Model because they can pivot their content strategy faster than enterprises. Because LLMs prioritize "Information Gain" (unique data and perspectives), a startup that publishes highly specific, novel insights or proprietary data can outperform a large incumbent that publishes generic, mass-market content. By focusing on niche authority and high-quality structured content automation, a small brand can dominate the "context window" for specific, high-value problem sets.

The "Share-of-Model" Thesis: Moving

TL;DR: Share-of-Model (SoM) is the new metric for the generative age, quantifying how frequently a brand or product is cited, recommended, or utilized within the output of Large Language Models (LLMs) and answer engines like ChatGPT, Perplexity, and Google AI Overviews. Unlike traditional Share-of-Voice, which measures visibility across search engine results pages (SERPs) based on keyword volume, Share-of-Model measures probabilistic visibility within the AI's context window, requiring a shift from keyword targeting to entity-based authority and structural optimization.

The Collapse of the "Ten Blue Links" Economy

For the past two decades, B2B marketing leaders have operated under a relatively stable set of physics: rank high for high-volume keywords, capture the click, and convert the traffic. The metric of success was Share-of-Voice (SoV)—a calculation of how much organic real estate a brand occupied compared to its competitors. However, the introduction of Generative Engine Optimization (GEO) and the rapid adoption of answer engines have fundamentally altered this landscape.

In 2025, user behavior shifted. Decision-makers are no longer "searching" in the traditional sense; they are prompting. They are asking complex, multi-layered questions to AI agents and expecting synthesized answers, not a list of links to investigate. In this environment, ranking #1 on a SERP is irrelevant if the AI summarizing that SERP chooses to cite your competitor as the primary source.

This transition demands a new thesis for measurement and optimization: Share-of-Model.

Share-of-Model is not about impressions; it is about inclusion. It asks a fundamental question: When an LLM constructs a response about your industry, is your brand part of the probabilistic calculation that forms the answer? If you are not in the model's weights or retrieved context, you are effectively invisible, regardless of your domain authority.

Share-of-Model is the percentage of generative AI responses for relevant queries in which a specific brand is cited as a source, recommended as a solution, or used as a defining entity. It represents a brand's "mental availability" within the neural network of an LLM or the retrieval index of an answer engine.

Unlike traditional SEO, which is deterministic (you optimize for a keyword, you rank for that keyword), Share-of-Model is probabilistic. It relies on the frequency of co-occurrence between your brand entity and specific solution-based concepts in the training data or the live web index. Achieving a high Share-of-Model requires a strategy focused on Answer Engine Optimization (AEO)—structuring content so that it is machine-readable, fact-dense, and highly extractable.

The Mechanics of LLM Visibility: How Citations Are Earned

To optimize for Share-of-Model, marketing leaders must understand the mechanism behind AI citations. LLMs and Retrieval-Augmented Generation (RAG) systems do not "read" content like humans; they parse tokens and semantic relationships.

1. Entity Salience and Knowledge Graph Alignment

In the generative era, keywords are secondary to entities. An entity is a distinct, well-defined concept—a brand, a person, a product, or a framework—that the AI recognizes as a noun with specific attributes.

For a brand to achieve high Share-of-Model, it must establish itself as a dominant entity associated with specific problems. For example, when a user asks, "What is the best way to automate B2B content?", the AI looks for entities that have the highest semantic proximity to "content automation." If your brand is consistently mentioned alongside these terms in high-authority contexts, the model assigns a higher probability to your brand being the answer.

Strategic Implication: You need to saturate your content with structured data (Schema.org/JSON-LD) that explicitly tells the search engine who you are and what you do. This is where platforms like Steakhouse Agent become critical, as they automate the injection of structured data into every piece of content, ensuring that search bots recognize the brand as a distinct, authoritative entity.

2. Information Gain and Unique Data

LLMs are trained to reduce redundancy. If your article merely repeats the same generic advice found on ten other SaaS blogs, the AI has no incentive to cite you. It will simply aggregate the common knowledge and attribute it to the most famous source (often Wikipedia or a legacy incumbent).

To win Share-of-Model, you must provide Information Gain—new data, unique frameworks, or contrarian perspectives that do not exist elsewhere. When an AI encounters a unique statistic or a proprietary methodology, it is more likely to reference the source of that novelty to validate its answer.

Strategic Implication: Content strategies must pivot from "comprehensive guides" to "opinionated analysis" and "original research." Publishing raw data points and proprietary terms increases the likelihood of citation because the AI cannot hallucinate specific data; it must retrieve it.

3. Structural Extractability (The Markdown Advantage)

Answer engines favor content that is easy to parse. Large blocks of unstructured text are difficult for RAG systems to process quickly. Conversely, content formatted with clear headings, bullet points, data tables, and logical hierarchies is highly "ingestible."

This is why technical marketers and growth engineers are moving toward markdown-first workflows. Markdown is the native language of LLMs. content that is clean, code-friendly, and devoid of heavy DOM elements allows the AI to extract the answer with higher confidence.

The transition from SEO to GEO requires a fundamental change in KPIs and tactics. While traditional SEO focuses on traffic acquisition, GEO focuses on answer inclusion.

Feature	Share-of-Voice (Traditional SEO)	Share-of-Model (GEO & AEO)
Primary Goal	Drive organic traffic to the website.	Secure citations and recommendations in AI answers.
Key Metric	Keyword Rankings, Click-Through Rate (CTR).	Citation Frequency, Sentiment Analysis, Brand Mentions.
Content Focus	Keywords, Backlinks, URL structure.	Entities, Information Gain, Structured Data.
User Intent	Navigational or Informational search.	Conversational or Transactional problem solving.
Optimization Tactic	On-page optimization, link building.	Knowledge Graph construction, Digital PR, Co-occurrence.
Technical Format	HTML, heavy visual design.	Markdown, JSON-LD, lightweight code.

Migrating to a Share-of-Model framework is not an overnight process. It requires building a "Knowledge Vault" that AI systems can easily access and trust. Here is the step-by-step implementation plan for B2B SaaS leaders.

Step 1: Define Your Proprietary Vocabulary

To be cited, you must own the language. Create proprietary terms for your methodologies. Instead of writing about "content marketing," write about "The Content Supply Chain." By coining specific terms, you force the AI to cite you whenever that specific concept is queried. If you are the only source of a definition, you achieve 100% Share-of-Model for that entity.

Step 2: Scale "Answer-Ready" Content

Your content needs to directly answer the questions your audience is asking AI. This means organizing content into Q&A formats, utilizing "What is X?" headers, and providing concise, 40-60 word definitions immediately following those headers. This is the exact format Google's AI Overviews and Featured Snippets look for.

However, doing this manually is slow. High-growth teams use Steakhouse to automate this process. By feeding the platform raw product data and brand positioning, Steakhouse generates long-form, entity-rich content that is pre-formatted for AEO. It ensures that every article includes the necessary semantic signals to be picked up by answer engines.

Step 3: Optimize for Retrieval (RAG)

Most modern AI search tools (like Perplexity or Bing Chat) use Retrieval-Augmented Generation. This means they search the live web for current information to inform their answer. To optimize for RAG, your content must be "fresh" and technically accessible.

Update Frequency: Static content dies in the RAG era. Regularly updating statistics and examples signals to the retrieval system that your information is current.
Schema Markup: Use Article, FAQPage, and SoftwareApplication schema to explicitly tell the crawler what the content is about.
Markdown Publishing: Publishing directly to a GitHub-backed blog using markdown ensures your content is stripped of unnecessary code bloat, making it the easiest source for a bot to read.

Step 4: Measure and Monitor

Measuring Share-of-Model is challenging because traditional analytics tools (Google Analytics) cannot track "views" inside a ChatGPT conversation. However, you can use proxy metrics:

Referral Traffic from AI Engines: Track referrers from Bing, Perplexity, and other AI-first browsers.
Brand Mention Velocity: Use social listening tools to see if your brand is being discussed more frequently in technical forums (which feed LLM training data).
Manual Testing: Regularly prompt major LLMs with your target queries (e.g., "Best AI content tools for SaaS") and record the frequency of your brand's appearance.

The Role of Automation in GEO

The paradox of Generative Engine Optimization is that while quality matters more than ever, volume is still required to establish topical authority. You cannot own a topic by writing one great article; you need a cluster of interlinked content that covers every nuance of the entity.

This is where manual writing fails. It is too slow to build the necessary density of information.

Steakhouse Agent solves this by acting as an always-on content colleague. It doesn't just "write text"; it architects a campaign. It takes a core topic, identifies the necessary sub-entities, generates the content in markdown, applies the correct JSON-LD schema, and pushes it to your repository. This allows technical marketing teams to build a massive footprint of high-quality, structured data without expanding their headcount. By automating the "grunt work" of GEO, leaders can focus on strategy while the software ensures the brand remains visible in the AI layer.

For brands ready to move beyond the basics, advanced GEO involves manipulating the "context window" through strategic co-occurrence.

The "Vs." Strategy: Aggressively target comparison queries (e.g., "Steakhouse vs. Jasper"). AI users are often in the evaluation phase. By creating detailed, objective comparison pages, you ensure your brand is part of the consideration set when an LLM summarizes the landscape.
Digital PR for Training Data: Get your brand mentioned in high-authority publications that are known to be heavily weighted in LLM training sets (e.g., TechCrunch, documentation sites, GitHub repositories). A mention in a high-weight domain is worth 100x a mention on a low-quality blog.
Data Journalism: Release annual reports or white papers in PDF and HTML formats. AI models frequently cite statistical reports as the "ground truth" for an industry.

Common Mistakes to Avoid

In the rush to adopt AI strategies, many brands make critical errors that hurt their Share-of-Model.

Mistake 1: Blocking AI Crawlers: Some brands block GPTBot or CCBot via robots.txt to protect their IP. This is a strategic error. If the bot cannot read your site, it cannot learn about your brand, and you will be excluded from the answer.
Mistake 2: Fluff Content: Writing 3,000 words of generic introduction before getting to the point ensures the AI will ignore your page. Put the answer first (BLUF - Bottom Line Up Front).
Mistake 3: Ignoring Brand Positioning: If your content is inconsistent—sometimes calling yourself a "platform," other times a "tool"—the entity resolution fails. Be consistent with your Noun-Name association.

Conclusion

The era of Share-of-Voice is fading; the era of Share-of-Model is here. B2B leaders who cling to keyword volume and traditional SERP rankings risk becoming invisible to the next generation of buyers who rely on AI for decision support. By shifting focus to entity density, structural extractability, and information gain, brands can secure their place in the AI's context window.

This transition requires a new operational cadence—one that prioritizes structured data and high-velocity, high-quality publishing. Tools like Steakhouse provide the infrastructure to make this shift, turning raw brand knowledge into a dominant digital footprint that answer engines cannot ignore.