Share of model is the percentage of AI assistant answers or recommendations in a product category that mention or recommend a specific brand — the AI-search counterpart to share of voice in traditional media.
When a buyer asks ChatGPT “what is the best CRM for a mid-size retailer” or asks Perplexity to compare vendors, the model names a handful of brands. Share of model measures how often yours is one of them. It is an early-stage metric — the term and its measurement conventions are still settling — but the behavior it tracks is already real: AI assistants have become a recommendation surface that sits before the website visit, before the analyst report, sometimes before the buyer knows the category’s vendor names at all.
Why Share of Model Is Becoming a Board Metric
Share of voice earned its place in board decks because ad presence predicted market share. Share of model is inheriting that role for a channel no one can buy. An AI assistant’s recommendation is not an ad slot; it emerges from what the model absorbed in training and what it retrieves at answer time. If models in your category consistently recommend three competitors and never you, you are absent from a growing slice of buying journeys — and no media budget fixes it directly.
The metric also behaves differently from paid visibility. It moves slowly (model training cycles are months apart, and retrieval authority accrues gradually), which makes it closer to brand equity than to campaign performance. That persistence is exactly why executives track it: a weak share of model today predicts weak pipeline quarters later.
How Share of Model Is Measured
No standard methodology exists yet — vendor dashboards differ, and numbers are not comparable across tools. The common measurement pattern has four parts:
- Category prompt set. Define the questions a real buyer asks — “best [category] for [segment],” “compare X and Y,” “what should I look for in a [category].” The prompt set is the denominator.
- Sampling across models. Run the prompt set against each major assistant (ChatGPT, Gemini, Claude, Perplexity, AI Overviews). A large language model is stochastic — the same prompt yields different answers on different runs — so each prompt is sampled repeatedly and results are averaged.
- Mention classification. Count whether the brand is mentioned, recommended, or cited as a source — three different strengths of presence. A recommendation (“consider Brand X”) is worth more than a passing mention.
- Trend tracking. Because single snapshots are noisy, the useful signal is share over time and share relative to named competitors.
Share of Model vs. Share of Voice
Share of voice measures presence in paid and earned media — a function of spend and PR, adjustable within a quarter, and covered under brand awareness measurement. Share of model differs on every axis: it cannot be bought, it is generated fresh in each answer rather than placed, and it changes on the timescale of model updates and retrieval authority rather than media flights. The two are complements — share of voice tracks the attention you rent; share of model tracks the recommendation you have earned in the systems buyers now consult first.
How to Move Share of Model
The lever is generative engine optimization: extractable definitional content, structured data, authoritative sourcing, and topical depth that make a brand’s content the material models cite. Entity consistency compounds it — models consolidate a brand described identically across its site and third-party sources, and fragment one that is not. For how this optimization work diverges from traditional search tactics, see GEO vs SEO.
The inward-facing half is data. A customer data platform shows which questions customers actually ask in their own words — the raw material for the category prompt set — and unified first-party data grounds the claims (real outcomes, real numbers) that make content citation-worthy rather than promotional.
FAQ
How do you measure share of model?
Sample a category prompt set across the major AI assistants and count brand mentions. Define the questions buyers ask in your category, run them repeatedly against ChatGPT, Gemini, Claude, and Perplexity to average out run-to-run variation, and classify each result as a mention, recommendation, or citation. Track the percentage over time and against competitors; single snapshots are too noisy to act on.
What is the difference between share of model and share of voice?
Share of voice is rented; share of model is earned. Share of voice measures presence in paid and earned media and responds to spend within a quarter. Share of model measures presence in AI-generated answers, cannot be bought, and moves on the slower timescale of model training cycles and retrieval authority. Treat them as complementary reads on visibility, not substitutes.
What is a good share of model?
There is no established benchmark — the useful reading is relative and directional. The metric is too new and methodologies too varied for absolute targets. In practice, teams compare their share against the top competitors in their own prompt set, check consistency across models (strong in one assistant, absent in another signals fragile authority), and treat sustained upward trend as the success criterion.
Related Terms
- Answer Engine Optimization — Winning the direct-answer surfaces where share of model is contested
- LLM SEO — The technical mechanics that determine whether models can find and cite your content
- AI Search Optimization — The umbrella visibility program that share of model scorecards
- Marketing Analytics — The measurement discipline this metric extends into AI channels