The digital marketing landscape has undergone a huge transformation. Consumers no longer rely solely on traditional search engines to discover products and services. This shift from keyword-based search to AI-powered discovery through large language models (LLMs) like ChatGPT, Claude, and Gemini represents more than a technological evolution—it's a fundamental restructuring of how brands connect with consumers.
For marketers, this transformation demands a new framework for measuring brand performance. "Share of Model" has emerged as the critical transparency metric for the AI era, offering unprecedented insights into how brands exist within the digital consciousness of artificial intelligence. This guide explores what Share of Model means, why it matters, how to measure it, and most importantly, how to leverage it for competitive advantage.
What Is Share of Model?
Share of model is defined as the number of mentions of a brand by one or multiple LLMs, as a proportion of total mentions of brands in the same category. In simpler terms, it measures how often AI platforms like ChatGPT, Claude, Gemini, or Perplexity recommend or reference your brand when responding to user queries compared to your competitors.
To understand Share of Model, it helps to see how it relates to established marketing metrics:
- Share of Voice (SOV) traditionally measured the percentage of advertising presence your brand commanded within a market—how much of the conversation you owned through paid media. It answered: "How loud is our brand in the marketplace?"
- Share of Search emerged with the digital age, tracking the proportion of search queries related to your brand versus competitors. It revealed consumer intent and interest patterns, answering: "How often are people actively looking for us?"
- Share of Model represents the next evolution, measuring brand visibility within AI-generated content ecosystems. It answers: "When AI assists with decision-making, how prominently does our brand appear?"
Think of it this way: if a potential customer asks ChatGPT, "What are the best CRM platforms for small businesses?" Share of Model tells you whether your brand appears in that response, how favorably it's positioned, and how you stack up against competitors. LLMs are the gatekeepers to consumers, and Share of Model quantifies your access through that gate.
Why It Matters in AI-Driven Campaigns
Large language models have fundamentally altered the consumer journey. They're not just search tools—they're recommendation engines, research assistants, and decision-making partners. When consumers use AI to explore options, they often receive complete answers without ever clicking through to websites. This creates a critical challenge: brands can become invisible even if their products are superior and their websites are optimized.
The implications are profound:
- Brand visibility now occurs before website traffic. Traditional metrics like click-through rates and page views only capture consumers who make it to your site. But if an LLM doesn't mention your brand in its response, those consumers never enter your funnel. Brands that don't master this transition risk becoming invisible in an AI-driven marketplace.
- AI models shape perception at scale. LLMs are now answering billions of search queries daily, creating and reinforcing brand perceptions with every response. A single well-positioned mention can influence thousands of purchase decisions. Conversely, absence from AI responses signals irrelevance to a growing segment of consumers.
- Competitive dynamics are being rewritten. Analysis reveals brands can belong to one of four distinct categories: Cyborgs like Tesla and BMW maintain strong awareness among both humans and AI models, AI Pioneers such as electric vehicle start-up Rivian score high with AI models despite limited mainstream awareness, and High-Street Heroes including Lincoln and Jaguar enjoy strong human brand recognition but struggle with AI visibility. Traditional market leaders can find themselves outflanked by digitally savvy upstarts who've optimized for AI discovery.
- Content efficacy is being redefined. The content that ranks well in traditional search may not resonate with LLMs. These models prioritize different signals—clarity, specificity, authoritative sources, and structured information. Share of Model serves as a proxy for how successfully your brand has integrated into these AI-powered information ecosystems.
Understanding your Share of Model isn't just about measurement—it's about recognizing where your next customers are forming their opinions about your brand and whether you're part of that conversation.
How Share of Model Is Calculated
Unlike traditional metrics with standardized methodologies, Share of Model measurement is still evolving. However, several approaches have emerged as best practices for tracking brand visibility across LLMs.
The Core Methodology
The leading method uses a polling-based model inspired by election forecasting. A representative sample of 250–500 high-intent queries is defined for your brand or category, functioning as your population proxy. These queries are systematically submitted to multiple LLMs, and brand mentions are tracked and compared.
The basic calculation follows this pattern:
- Prompt Selection: Identify 20-50 relevant queries that potential customers might ask (e.g., "best project management software for remote teams," "top cybersecurity solutions for healthcare," "recommended email marketing platforms")
- Multi-Model Querying: Submit each prompt to major LLMs (ChatGPT, Claude, Gemini, Perplexity, etc.)
- Mention Tracking: Record when your brand appears in responses, noting:
- Frequency of mentions
- Position in recommendations (first, middle, last)
- Context of mentions (positive, neutral, negative)
- Citation vs. text mention
- Competitive Analysis: Track competitor mentions using the same prompts
- Share Calculation: Your Share of Model = (Your brand mentions ÷ Total category mentions) × 100
Data Collection Methods
Manual Auditing: For smaller brands or initial assessments, manually querying LLMs and documenting responses works. This is time-intensive but provides qualitative insights into how models describe your brand.
Automated Tools: Early tools providing this capability include Profound, Conductor, OpenForge, and Semrush. These platforms systematically query multiple LLMs, aggregate results, and track changes over time, providing dashboards that show share of voice, sentiment analysis, and competitive benchmarking.
Frequency Analysis: Beyond simple mention counts, sophisticated tracking examines:
- Citation analysis: When your brand appears as a linked source vs. an unattributed mention
- Sentiment scoring: Whether mentions are positive, neutral, or negative
- Topic association: Which product categories or use cases trigger your brand mentions
Limitations and Challenges
Share of Model measurement faces several inherent challenges:
- Model Variability: Brands' SOM varies significantly across the models. For instance, Ariel commands nearly 24 percent of mentions on Meta's Llama but less than 1 percent on Google's Gemini. Chanteclair enjoys a 19 percent SOM on Perplexity but disappears completely from Llama. This inconsistency means you need multi-platform tracking to understand true visibility.
- Opaque Training Data: LLMs don't disclose their training datasets or how they weight different sources. This makes it difficult to reverse-engineer why certain brands appear more frequently.
- Response Randomness: LLMs use probabilistic generation, meaning the same prompt can yield different answers. Consistent sampling at scale transforms apparent randomness into interpretable signals, but individual queries can be unreliable.
- Hallucinations: LLMs occasionally generate false information or attribute non-existent features to brands. This noise complicates measurement and can spread misinformation.
- Temporal Decay: Model knowledge becomes outdated over time. A brand's visibility might reflect past market position rather than current reality, especially for models with older training cutoffs.
Strategic Implications for Marketers
Share of Model fundamentally reshapes marketing strategy across multiple dimensions. Understanding these implications helps marketers adapt their approaches for AI-mediated consumer discovery.
Brand Visibility Without Website Traffic
Traditional marketing funnels assumed consumers would visit websites during their research phase. LLMs disrupt this assumption. A consumer might receive a comprehensive product recommendation, compare options, understand pricing, and form purchase intent entirely through AI conversations—never seeing your website until they're ready to buy (or not at all if they purchase through voice commerce).
This means brand equity must exist independently of owned channels. Your brand's reputation, associations, and positioning need to be embedded in the broader information ecosystem where LLMs train and draw knowledge. Marketing can no longer rely solely on controlling the narrative through paid media and owned content.
Campaign Planning Beyond SERPs
Traditional SEO focused on search engine results pages (SERPs)—ranking in the top 10 for target keywords. AI-driven search often bypasses SERPs entirely, delivering synthesized answers.
Campaign planning must now consider:
- AI-First Content Formats: Creating content specifically designed to be referenced by LLMs—comprehensive guides, comparison tables, technical specifications, case study databases.
- Multi-Source Distribution: Since LLMs train on diverse sources, your content can't live only on your website. Guest articles, industry publications, review sites, forums, and social platforms all contribute to model knowledge.
- Temporal Strategies: Understanding that LLM knowledge has lag times. Major product launches or positioning changes may take months to fully reflect in model outputs. Plan accordingly.
- Reputation Management at Scale: Negative information in LLM responses can spread widely. Every marketer dreads negative reviews. In the age of LLMs, negative perceptions can be amplified and repeated in response to search queries. Proactive reputation monitoring becomes critical.
The Future of Transparent Marketing Metrics
Share of Model is evolving rapidly. Several trends will shape its development:
Standardization and Methodology
It's too early to say if share of model will prove to have the universal utility that has been the key to the success of its 'share of' predecessors, and there's a lot of work to do if it's to become anywhere near as widely known and used. Firstly, as with SOV, ESOV and share of search, establishing if there's a relationship with share of market feels important.
Expect industry efforts to:
- Establish standard query sets for different categories
- Create benchmarks for what constitutes "good" Share of Model
- Validate correlations between Share of Model and business outcomes
- Develop certification or auditing processes for measurement accuracy
Platform Evolution
Current tools provide basic visibility tracking, but next-generation solutions will offer:
- Real-time monitoring across dozens of LLMs simultaneously
- Predictive analytics showing how content changes might affect visibility
- Automated content optimization recommendations
- Integration with existing marketing analytics platforms
- Competitive intelligence showing rival optimization strategies
Regulatory Implications
As Share of Model becomes more influential, expect regulatory scrutiny around:
- LLM training data transparency requirements
- Brand representation fairness
- Consumer protection from AI-driven manipulation
- Disclosure requirements for AI-optimized content
The Multi-Model Reality
The LLM landscape is fragmenting. ChatGPT, Claude, Gemini, DeepSeek, and numerous other models each have different training data, update cycles, and algorithmic approaches. Future Share of Model strategies must account for this diversity, optimizing across multiple platforms rather than focusing on a single dominant player.
Taking Action Today
Share of Model represents a fundamental shift in how brands must think about visibility and perception. As LLMs increasingly mediate consumer discovery, understanding and optimizing your presence in these systems becomes essential for competitive survival.
The brands that will thrive in this new landscape are those that begin measuring and optimizing for AI now, while Share of Model remains a competitive edge rather than table stakes. This means:
- Start measuring: Even basic manual audits provide valuable baseline data. Understand where you stand today.
- Invest in content quality: High-signal, authoritative content that serves genuine audience needs will naturally strengthen AI visibility.
- Think holistically: Share of Model should inform strategy alongside traditional metrics, not replace them.
- Stay ethical: Pursue visibility through value creation, not manipulation. Build brands that deserve to be recommended.
- Remain adaptive: The LLM landscape is evolving rapidly. Strategies that work today may need adjustment tomorrow.
Share of Model is not just another vanity metric—it's a lens into how your brand exists in the AI-mediated future of consumer research and decision-making. Marketers who master this metric, while balancing it with timeless principles of brand building and customer service, position their organizations for sustained success in the AI era.
The shift from traditional search to AI-powered discovery is not temporary disruption—it's the new normal. Brand visibility in LLMs isn't optional; it's fundamental to being discovered, considered, and chosen. The question is no longer whether to track Share of Model, but how quickly you can integrate it into your strategic framework and begin optimizing accordingly.
The brands that win won't be those with the most mentions, but those that are mentioned in the right contexts, for the right reasons, at the moments that matter most to consumers. That requires not just measurement, but deep strategic thinking about how to build brand equity in an age when artificial intelligence increasingly shapes human perception.
Begin your Share of Model journey today. Your future customers are already asking AI for recommendations. The only question is whether your brand will be in the answer.
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