How to Track Your Brand’s Share of Model (SOM)

In the rapidly evolving landscape of digital search and information retrieval, traditional metrics like “Share of Voice” are being challenged by a more nuanced and forward-looking indicator: the Share of Model (SOM) metric. As artificial intelligence, large language models (LLMs), and generative AI become central to how users discover information, understanding and influencing what these models ‘know’ about your brand is paramount. For brands aiming to maintain relevance and visibility in the age of AI, tracking your brand’s Share of Model is no longer optional – it’s critical.

What Exactly is the Share of Model (SOM) Metric?

The Share of Model (SOM) metric represents the extent to which your brand, products, or services are represented and accurately understood within the vast datasets and knowledge graphs that power generative AI and conversational search interfaces. Unlike Share of Voice, which often quantifies brand mentions or visibility in traditional media channels and SERPs, SOM delves deeper. It measures your brand’s presence and prominence within the informational fabric that AI models draw upon to generate responses, summarize topics, and answer user queries.

Think of it this way: when a user asks an AI chatbot, “What are the best [product category] for [specific need]?” or “Tell me about [industry trend],” how likely is it that your brand will be accurately mentioned, recommended, or included in the AI’s synthesized answer? Your Share of Model metric is a direct indicator of this influence. It’s about ensuring your brand is not just *found* by search engines, but *known* and *understood* by the underlying AI.

Why the Share of Model Metric Matters More Than Ever

The shift towards generative AI fundamentally changes how users interact with information. Instead of clicking through ten blue links, users are increasingly receiving synthesized answers. This means brands need to move beyond simply ranking on Google to being a trusted, authoritative source that AI models naturally reference. Several factors highlight the increasing importance of the Share of Model metric:

• Rise of Conversational AI: Tools like ChatGPT, Bard, and Bing Chat are becoming primary interfaces for information discovery. If your brand isn’t part of their knowledge base, you’re invisible.
• Knowledge Graph Dominance: AI models rely heavily on structured data and knowledge graphs to understand entities and relationships. Brands that feed into these systems have a natural advantage.
• Generative Content Creation: AI is not just summarizing; it’s creating. Influencing the sources AI uses to generate content means your brand’s narrative can be woven into new outputs.
• Competitive Advantage: Early adopters in optimizing for SOM will establish a significant lead, becoming the default authoritative source for AI models in their niche.

Key Pillars for Tracking and Improving Your Brand’s SOM

Optimizing for the Share of Model metric requires a strategic pivot from traditional SEO, focusing on how AI models ingest and interpret information. Here are the core pillars:

1. Structuring Data for AI Consumption

AI models thrive on well-organized, unambiguous data. For your brand to achieve a high Share of Model, your content needs to be presented in a way that AI can easily parse, understand, and integrate into its knowledge base. This goes beyond simple HTML. It involves a deep understanding of semantic relationships and data hierarchies. For a comprehensive guide on this, explore our insights on Structuring Data for RAG (Retrieval-Augmented Generation).

2. Implementing Robust Schema Markup

Schema markup is essentially the language you use to communicate directly with search engines and AI models about the meaning and context of your content. By adding specific tags to your HTML, you can tell AI that a particular piece of text is a product review, a price, a person’s name, or a brand. This clarity is invaluable for increasing your Share of Model. To master this critical aspect, read our article on Schema Markup for AI: Speaking the Robot’s Language.

3. Developing Authoritative, Comprehensive Content

AI models seek out trustworthy, in-depth information. Brands that consistently produce high-quality, long-form content that thoroughly addresses topics relevant to their industry are more likely to be recognized as authorities. This content not only provides rich data for AI but also establishes your brand’s expertise, experience, authoritativeness, and trustworthiness (E-E-A-T). Discover why this content strategy is more vital than ever in our post: Why Long-Form Content is Making a Comeback in GEO.

Google has increasingly emphasized E-E-A-T as a critical factor in ranking and information retrieval. Content that demonstrates these qualities is more likely to be incorporated into AI models’ understanding of a topic, directly impacting your Share of Model metric. For more on Google’s quality guidelines, refer to their guidance on creating helpful, reliable, people-first content.

4. Monitoring AI Outputs and Mentions

To track your SOM, you need to actively monitor where and how your brand is being mentioned (or not mentioned) in AI-generated responses. This involves:

• Direct Querying: Regularly query popular AI models (ChatGPT, Bard, Bing Chat) with questions related to your brand, products, industry, and competitors. Note how your brand is represented.
• Brand Monitoring Tools: Utilize advanced brand monitoring tools that can track mentions across web pages, news articles, and even identify potential sources for AI training data. Tools like Brandwatch or Mention can be adapted for this.
• Competitor Analysis: Observe your competitors’ presence in AI outputs. This can reveal strategies they are employing effectively.

Understanding how AI models interpret and present information can also be aided by exploring resources from AI thought leaders. For instance, reputable AI research entities like OpenAI’s research blog often discuss how models are trained and evaluate information, providing context for what makes a source authoritative in the AI world.

5. Cultivating Brand Entity Recognition

For AI, your brand isn’t just a string of words; it’s an entity with attributes, relationships, and a reputation. Ensuring AI models recognize your brand as a distinct, authoritative entity is crucial. This involves consistent branding across all digital touchpoints, building a strong online presence, and acquiring high-quality backlinks that signal trust and authority to both traditional search engines and AI models.

Practical Steps to Measure and Improve Your Share of Model

1. Define Key Query Clusters: Identify the specific questions, topics, and keywords users would ask AI where your brand should ideally be the answer.
2. Baseline AI Visibility: Conduct an initial audit by asking AI models these key questions. Document the results: Is your brand mentioned? How accurately? How prominently?
3. Audit Your Content for AI Readiness: Review your website content for structured data implementation, schema markup, and overall comprehensiveness and authority. Identify gaps.
4. Implement AI-Optimized Content Strategy: Create new content and optimize existing content with a strong focus on structured data, E-E-A-T, and clear, concise information that AI can easily process.
5. Actively Build Brand Entity: Work on consistent brand messaging, high-quality content, and strategic digital PR to bolster your brand’s perception as an authoritative entity.
6. Continuous Monitoring and Iteration: The AI landscape is dynamic. Regularly re-evaluate AI outputs for your key queries, analyze changes, and refine your strategy.

Conclusion

The Share of Model metric represents the next frontier in digital visibility. As AI increasingly mediates how users discover and consume information, ensuring your brand is deeply embedded within the knowledge base of these models is paramount for future success. By focusing on structured data, robust schema, authoritative content, and diligent monitoring, brands can proactively shape their presence in the AI era, securing their position as a trusted and recognized entity in the models that matter most.

Frequently Asked Questions About Share of Model

What is the main difference between Share of Model (SOM) and Share of Voice (SOV)?

Share of Voice (SOV) traditionally measures your brand’s visibility and mentions across various channels (e.g., social media, traditional media, search results) relative to competitors. The Share of Model (SOM) metric, on the other hand, specifically quantifies your brand’s presence and accurate representation within the datasets and knowledge graphs that power generative AI models. SOM is less about direct mentions and more about being a fundamental part of the AI’s understanding and generated responses.

How can a small business effectively track its Share of Model without extensive resources?

Small businesses can start by focusing on key, niche-specific queries relevant to their offerings. Regularly use public generative AI tools (like ChatGPT, Bard, or Bing Chat) to ask questions related to their products, services, and local area. Document if and how their brand is mentioned. Simultaneously, prioritize implementing basic schema markup on their website, ensuring their Google Business Profile is fully optimized, and creating comprehensive, high-quality content around their core expertise. Consistency in these areas can significantly improve their foundational SOM.

Will traditional SEO still be relevant if Share of Model becomes the dominant metric?

Yes, traditional SEO will absolutely remain relevant, but its focus will evolve. Many foundational SEO practices – like creating high-quality, authoritative content, building a strong backlink profile, ensuring technical site health, and mobile-friendliness – directly contribute to a higher Share of Model. These practices improve your brand’s overall digital authority and trust, which in turn makes your content more likely to be considered valuable by AI models for inclusion in their knowledge bases. SEO will increasingly be viewed as the strategic foundation for influencing AI.