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

Share of Search measures the volume of search queries containing a brand's name, indicating brand awareness and interest. Share of Model (SoM), however, measures the frequency with which a Generative AI or Large Language Model (LLM) cites a brand as an answer or recommendation within its output. While Share of Search tracks user curiosity, Share of Model tracks the AI's endorsement of your brand authority.

How can I improve my brand's visibility in AI Overviews?

To improve visibility in AI Overviews, focus on Generative Engine Optimization (GEO). This involves creating high-authority content rich in unique data (Information Gain), using clear and direct formatting (like lists and tables), and implementing extensive structured data (Schema.org). By clearly defining your brand entity and its relationship to specific problems, you make it easier for AI models to retrieve and cite your content as the definitive answer.

Why are traditional traffic metrics declining for B2B SaaS?

Traditional traffic metrics are declining due to the rise of "Zero-Click" searches and AI-generated answers. Users are increasingly getting their questions answered directly on the search results page (via AI Overviews) or through chatbots like ChatGPT, eliminating the need to click through to a website. This shift means that value is being consumed on the platform, not on your site, making traffic a less reliable indicator of brand influence or market share.

What tools can I use to measure Share of Model?

Currently, there is no single standardized tool for measuring Share of Model, but you can approximate it using a combination of methods. You can perform manual audits by running your top keywords through ChatGPT, Perplexity, and Google's AI Overviews to see if you are cited. Additionally, emerging AEO analytics platforms and proxy-based tracking tools are beginning to offer "share of voice" metrics specifically for generative search environments.

How does structured data help with Answer Engine Optimization (AEO)?

Structured data (JSON-LD) acts as a translator between your content and the AI. It explicitly tells the search engine or LLM what your content is about—identifying entities like "Product," "FAQ," "Author," or "Service." By providing this machine-readable context, you remove ambiguity, allowing the AI to confidently understand your brand's offerings and cite them accurately in response to relevant user queries. Tools like Steakhouse Agent automate this process to ensure maximum entity recognition.

Measuring "Share of Model": The New KPIs

TL;DR: "Share of Model" (SoM) is the new metric for the generative era, measuring how frequently and favorably an Artificial Intelligence model (like GPT-4, Gemini, or Claude) cites your brand as the solution to a user's problem. Unlike traditional "Share of Search," which tracks query volume, SoM tracks answer inclusion. To win in a zero-click world, B2B leaders must pivot from optimizing for clicks to optimizing for citations, ensuring their brand entities are embedded in the training data and retrieval layers of the world's dominant answer engines.

The Traffic Cliff: Why Traditional KPIs Are Failing

For the last decade, the B2B marketing playbook was predictable: target high-volume keywords, rank in the top three blue links, and watch the traffic convert into leads. That era is ending. In 2025, we are witnessing the "Traffic Cliff"—a sharp decline in organic click-through rates (CTR) caused by the ubiquity of AI Overviews (AIO) and direct-answer chatbots.

Data suggests that over 60% of informational B2B queries now end without a click to a website. The user asks a question, the AI synthesizes an answer, and the user leaves satisfied. If your marketing strategy relies solely on traffic volume and session duration, you are optimizing for a behavior that is rapidly disappearing.

However, the demand for your solution hasn't vanished; it has simply moved layers. The search bar has evolved into an answer engine. In this environment, "ranking" is irrelevant if the AI summarizes your competitor's product as the best solution. The new battleground isn't the SERP (Search Engine Results Page); it's the LLM (Large Language Model) context window. This shift requires a fundamental restructuring of how we measure success, moving from "Share of Search" to "Share of Model."

Share of Model (SoM) is a metric that quantifies a brand’s visibility and authority within the outputs of Generative AI and Answer Engines. It measures the percentage of relevant AI-generated responses in which a brand is cited, recommended, or used as the primary source of information. Unlike SEO, which fights for a position on a list, SoM fights for inclusion in a narrative.

When a user asks, "What is the best automated content platform for B2B SaaS?", the AI doesn't give ten links; it gives one synthesized opinion. If you have a high Share of Model, you are that opinion. If you don't, you are invisible, regardless of your Domain Authority.

Measuring SoM is more complex than tracking keyword rankings because AI outputs are non-deterministic—they can change slightly with every regeneration. However, forward-thinking marketing leaders are triangulating SoM using three distinct pillars.

1. Citation Frequency (The "Mention" Metric)

This measures the raw volume of times your brand is explicitly named in response to category-level queries.

The Query: "List the top tools for Generative Engine Optimization."
The Measurement: Does your brand appear in the list? Is it in the top 3?
Why it matters: This is the direct equivalent of "ranking," but for the generative age. High citation frequency indicates that the LLM recognizes your brand as a relevant entity within the topic cluster.

2. Recommendation Sentiment (The "Context" Metric)

Being mentioned is not enough; the context determines the conversion. LLMs are opinionated by design—they are trained to provide the "best" answer.

The Query: "Compare Steakhouse Agent vs. Jasper for enterprise SEO."
The Measurement: Does the AI describe your product accurately? Does it highlight your unique value proposition (e.g., "Steakhouse is better for structured, developer-friendly workflows")? Or does it hallucinate features you don't have?
Why it matters: This metric tracks the quality of the AI's understanding of your brand positioning. It reveals if your content strategy has successfully educated the model.

This is the holy grail of SoM. It measures how often your brand is offered as the solution to a problem, even when the user doesn't mention a product category.

The Query: "How can I automate my blog using GitHub and Markdown?"
The Measurement: Does the AI suggest, "You should use a tool like Steakhouse Agent to automate this workflow"?
Why it matters: This indicates that the AI maps your brand entity directly to the user's "Jobs to Be Done" (JTBD). You aren't just a software tool; you are the recognized method for solving the problem.

SEO vs. GEO: A Comparison of KPIs

To operationalize Share of Model, teams must swap their dashboard metrics. Here is how the old world compares to the new.

Strategic Focus	Traditional SEO (Search Engine Optimization)	GEO (Generative Engine Optimization)
Primary Goal	Drive clicks to a landing page.	Drive citations in an AI answer.
Key Metric	Organic Traffic / CTR.	Share of Model / Citation Frequency.
Content Structure	Keywords, Backlinks, Word Count.	Entities, Structured Data, Information Gain.
User Experience	User scrolls and clicks a blue link.	User reads a synthesized answer (Zero-Click).
Conversion Logic	"Read more on our site."	"This brand is the trusted authority."

Currently, there is no single "Google Analytics for LLMs," but marketing teams can build a measurement framework using a mix of manual auditing and emerging tools. Here is a step-by-step process for 2025.

Step 1: Define Your "Golden Queries"

Identify the top 50 questions your bottom-of-funnel prospects ask. These should not just be keywords (e.g., "AEO tools") but full conversational queries (e.g., "What is the best software for automating SEO content on a technical blog?").

Step 2: The "incognito" AI Audit

Run these queries through the major answer engines: Google AI Overviews (SGE), ChatGPT (GPT-4), Perplexity, and Claude.

Record Presence: Did we appear? (Yes/No)
Record Rank: Were we the first recommendation or the last?
Record Accuracy: Was the description correct?

If you test 50 queries and your brand appears in the answer for 20 of them, your Share of Model is 40%. Track this monthly. If you implement a GEO strategy and that number rises to 60%, you have successfully influenced the model.

Improving your SoM requires a fundamental shift in how content is produced. You cannot "trick" an LLM with keyword stuffing; you must teach it with structured facts.

1. Adopt an Entity-First Content Strategy

LLMs view the world as a web of entities (things, people, concepts) and the relationships between them. To increase SoM, your content must clearly define your brand entity and link it to relevant topic entities.

Tactical Shift: Instead of writing a generic blog post about "content marketing," write a definitive guide on "How [Brand Name] automates [Specific Process]." This creates a strong semantic connection between your brand and the solution.

2. Implement Heavy Structured Data (JSON-LD)

Robots don't read like humans; they parse code. Schema markup (JSON-LD) is the language of entities. By wrapping your content in rich schema (Organization, SoftwareApplication, FAQPage, Article), you provide the AI with a structured syllabus of your business.

Platforms like Steakhouse Agent are built specifically for this. Unlike generic AI writers that just output text, Steakhouse automates the generation of deep structured data and entity mapping. This ensures that when Google's crawlers or an LLM digests your content, they understand exactly who you are and what you solve, increasing the probability of citation.

3. Optimize for "Information Gain"

LLMs are trained to reduce redundancy. If your article repeats the same generic advice as the top 10 search results, the AI has no reason to cite you. It will simply aggregate the consensus.

To win Share of Model, you must provide Information Gain—unique data, contrarian viewpoints, or proprietary frameworks that do not exist elsewhere. When you introduce a new term or a unique statistic, the LLM cites you as the "origin node" of that information.

4. Create "Answer-Ready" Content Blocks

Format your content so it is easy for an AI to extract. Use direct definitions, clear comparison tables, and bulleted lists. The easier it is for the algorithm to parse your answer, the more likely it is to serve that answer to a user.

The Pattern: Ask the question in an H2. Answer it immediately in a concise, 50-word paragraph (the "snippet"). Then expand on the details.

As teams rush to adapt to GEO and AEO, they often fall into traps that damage their visibility.

Mistake 1: Gating all high-value content. If your best insights are locked behind a PDF lead magnet, the LLM cannot read them. If the LLM cannot read them, it cannot learn from them. In the age of AI, un-gated content builds the training data that drives brand preference.
Mistake 2: Ignoring the "About" page. Your "About" and "Home" pages are critical for defining your brand entity. Vague marketing fluff ("We make the world better") confuses the AI. Be literal: "We are a B2B SaaS platform for Generative Engine Optimization."
Mistake 3: Inconsistent Brand Positioning. If your blog says you are an "SEO tool" but your landing page says you are a "Growth Platform," the AI lowers its confidence score in your entity. Consistency across the web reinforces the model's understanding.

Conclusion: The First-Mover Advantage in GEO

We are currently in the "land grab" phase of Generative Engine Optimization. The brands that establish their entities within the LLMs today will be the default answers for the next decade of search.

Measuring Share of Model is not just a vanity metric; it is a survival metric. By shifting your focus from accumulating clicks to accumulating citations, you position your brand to thrive in a zero-click world. Whether you are using manual audits or leveraging automated platforms like Steakhouse to scale your GEO-optimized content, the goal remains the same: Be the answer, not just a link.

The Traffic Cliff: Why Traditional KPIs Are Failing

What is "Share of Model"?

The Three Pillars of Share of Model Metrics

1. Citation Frequency (The "Mention" Metric)

2. Recommendation Sentiment (The "Context" Metric)

3. Share of Intent (The "Solution" Metric)

SEO vs. GEO: A Comparison of KPIs

How to Measure Share of Model in Practice