$ ~/ym8 --define share-of-model
Share of Model
definition
Share of Model is the foundational metric of AI Engine Optimisation. Just as Share of Voice measured a brand's proportion of total advertising exposure in traditional media, Share of Model measures a brand's proportion of AI-generated mentions across relevant query categories.
The metric is calculated by running a bank of queries relevant to your brand category through each AI engine, then measuring how often your brand appears in the responses compared to competitors. For example, if 100 queries about "CRM software" are run through ChatGPT and your brand appears in 23 responses, your Share of Model for that category on ChatGPT is 23%.
Share of Model varies significantly across engines. A brand might have 30% SoM on ChatGPT but only 5% on Perplexity, reflecting differences in training data, search methodology, and content weighting. This cross-engine variance is why monitoring all major AI engines simultaneously is essential for a complete AEO strategy.
Unlike traditional SEO metrics that are relatively stable, Share of Model can shift rapidly as AI models are retrained, fine-tuned, or updated. A brand that dominates SoM today could lose ground after a model update if competitors have published more recent, authoritative content.
why_it_matters
Share of Model is the single most important metric for understanding your brand's visibility in the AI-driven discovery landscape. As users increasingly rely on AI engines for product research and recommendations, a brand's SoM directly correlates with its ability to be discovered, considered, and chosen. Brands with low SoM are effectively invisible to the growing segment of consumers who use AI as their primary research tool.
examples
- A SaaS company tracking SoM across 50 queries on 8 AI engines, finding 35% on ChatGPT but only 8% on Perplexity
- An ecommerce brand comparing their SoM against 5 competitors month-over-month to detect ranking shifts
- A fintech startup monitoring SoM for "payment processing" queries before and after publishing new comparison content
faq
How is Share of Model calculated?
Share of Model is calculated by running a defined set of queries (a query bank) through each AI engine and measuring the percentage of responses that mention your brand. The calculation is: (Number of responses mentioning your brand / Total queries run) × 100. This is typically tracked per engine and per query category.
How often should I measure Share of Model?
Share of Model should be measured at least monthly, with weekly monitoring recommended for competitive categories. AI models are updated frequently, and SoM can shift significantly after model retraining events. Continuous monitoring platforms like the AEO Platform provide real-time tracking.
What is a good Share of Model percentage?
A "good" Share of Model depends on your category and competitive landscape. In niche categories, 40-60% SoM may be achievable. In competitive categories like CRM or project management, 15-25% SoM would represent strong visibility. The key is to benchmark against competitors rather than aiming for an absolute number.
Related Terms
Citation Rate
Citation Rate measures the frequency at which an AI engine references a specific source domain when generating responses. Unlike Share of Model, which tracks brand mentions, Citation Rate specifically tracks when your website URL or domain is cited as a source.
Competitor Visibility
Competitor Visibility in AEO measures how often and how favourably your competitors appear in AI engine responses compared to your brand. It provides the competitive context necessary to understand whether your AI visibility position is strong, weak, or at risk.
Query Bank
A Query Bank is a curated collection of search queries used to systematically measure AI engine visibility. It represents the questions your target audience asks AI engines about your product category, used as the basis for calculating Share of Model and other AEO metrics.
Brand Mention Tracking
Brand Mention Tracking in AEO is the process of systematically monitoring when and how AI engines mention your brand in their responses. It goes beyond simple name detection to analyse context, sentiment, accuracy, and competitive positioning of each mention.
Related Engines
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