What is Share of Model (SoM)? The New SEO Metric for the AI Era

In an increasingly AI-driven world, traditional SEO metrics are no longer enough to gauge a brand's true online influence. The rise of large language models (LLMs) like ChatGPT, Google's Gemini, and Microsoft's Copilot has fundamentally shifted how consumers discover and interact with brands. This new landscape demands a new metric: Share of Model (SoM). SoM quantifies how AI models perceive, understand, and recommend your brand against competitors, making it a critical indicator for future success. Understanding and optimizing your SoM is not just an advantage it's a necessity, especially when platforms like GenRankEngine provide the tools to precisely measure this AI perception. This post will delve into what SoM is, why it's the new cornerstone of SEO, and how marketers can effectively measure and improve it.

Why Share of Model (SoM) is the New SEO Metric for the AI Era

The rapid integration of AI into daily life has created a seismic shift in consumer behavior and brand discovery. As AI chatbots and virtual assistants become primary conduits for information, research, and purchasing decisions, their influence on brand perception is escalating dramatically. Billions of search queries are now processed daily by LLMs, making a brand's representation within these AI outputs paramount [marketing.org.nz].

This shift moves "beyond traditional SEO" which historically focused on optimizing content for search engine rankings based on keywords and links [hallam.agency]. SoM, however, is about optimizing for visibility and positive representation within AI-generated answers. It ensures that a brand’s content is not only discoverable by search engines but also comprehensively understood and favorably interpreted by AI models, which is crucial for succeeding in the "agentic web" where AI acts as an agent for the user [ayzenberg.com].

A key difference is that LLMs often generate "direct answers" rather than a list of links. This means a brand either appears directly in the AI’s response, or it effectively doesn’t exist for that particular query [substack.com]. This zero-click reality makes SoM indispensable, as it directly impacts "brand perception and trust." A high SoM signals that a brand is well-represented and positively perceived by these powerful models, significantly increasing the likelihood of recommendations. Marketers gain insights into the sentiment, attributes, and associations that shape AI-driven brand perception, enabling them to align their positioning with what resonates most effectively [shareofmodel.ai].

Measuring and Improving Your Share of Model

Measuring Share of Model involves tracking how frequently and favorably a brand is mentioned or referenced by LLMs relative to its competitors within a given category. This includes analyzing the positive and negative associations generated by LLMs, assessing overall visibility to AI models, and understanding how each model perceives and suggests products or services [nevillehobson.com]. Tools like GenRankEngine are designed precisely for this, simulating agentic visits to your site, crawling content, and reporting which entities (features, pricing, claims) are successfully extracted by AI models and which are lost in the noise.

To improve SoM, marketers should implement several strategic initiatives:

• Optimize for LLMs: Websites and digital assets must be structured and optimized specifically for AI models. This means focusing on clarity, factual accuracy, and readily digestible information that LLMs can easily process [contentmarketinginstitute.com].
• Structured Content: Ensure content is well-structured, using clear headings, bullet points, and schema markup to enable AI models to accurately represent the brand.
• Consistent Engagement and Adaptation: Brands need to consistently engage with LLMs and adapt their content strategies to maintain favorable AI recognition and sentiment. This involves continuously monitoring AI outputs for brand mentions and adjusting content accordingly.
• Competitor Monitoring: Regularly track competitor comparisons within AI responses to identify gaps and opportunities for differentiation.
• Thought Leadership and Media Presence: Develop a strong media presence and establish thought leadership in key topics. AI models learn from vast datasets, including news, academic papers, and industry sources, so a prominent and authoritative presence across these channels is vital.

The proactive measurement and optimization of SoM are becoming essential components of forward-thinking marketing strategies. It allows brands to drive awareness, consideration, and sales in an increasingly AI-powered consumer landscape by ensuring they own the answer in AI-driven environments.

The Audit Process:

• Fetch: Use a tool to download your page as raw text (stripping HTML).
• Analyze: Feed that text into Gemini Pro or GPT-4.
• Prompt: Ask the model, "Extract the pricing tier for Enterprise users." If it hallucinates or fails, your content is broken for agents.

This is where GenRankEngine becomes essential. Our platform simulates an agentic visit, crawling your site and reporting exactly which "entities" (features, pricing, claims) are successfully extracted by the model and which ones are lost in the noise.

Conclusion

The future buyer will not be a human scrolling Google; it will be an AI agent compiling a dossier. Optimizing for Agentic AI isn't just a technical upgrade it's a survival strategy for the autonomous web. Start by treating your content less like a brochure and more like a database.

Ready to see if agents can read your site? Run a free Agentic Visibility Scan with GenRankEngine today.