● Buyer Question · Measurement · Discovery Stage
What is share of model in AI search?
● The Short Answer
Share of model is the percentage of AI-engine responses to a given query category in which a specific brand is mentioned. It is the AI-era equivalent of share of voice: if your brand appears in 45 out of 100 AI answers to "best project management tool," your share of model for that category is 45%. GEOscanAI, Profound, and AthenaHQ each measure share of model across ChatGPT, Claude, Gemini, and Perplexity.
● Who's Asking This
A marketing analyst, CMO, or agency strategist who is hearing the term "share of model" in AI marketing conversations and needs a precise working definition before building measurement frameworks or presenting AI visibility metrics to leadership.
● The Breakdown
The formal definition and how it is calculated
Share of model is calculated by running a structured set of category queries against one or more AI engines, parsing responses for brand mentions, and expressing each brand's appearance rate as a percentage of total queries run. If you run 200 queries asking "what is the best [category] tool" across ChatGPT and Claude, and your brand appears in 90 responses, your share of model for that engine-query combination is 45%. Because AI responses are non-deterministic, the metric is inherently statistical — it requires a sufficient query sample size to be meaningful, typically 50 to 200 queries per category per engine.
Share of model vs share of voice: what the AI metric adds
Share of voice, the traditional equivalent, measures how often a brand is mentioned in paid or organic media relative to total category mentions. Share of model is more demanding: it measures how often an AI engine independently selects your brand as a recommendation when no query specifically names your brand. This is closer to earned recommendation than earned media — the AI is functioning as an advisor, and share of model tells you how often it advises for you versus competitors. It correlates more directly with buyer consideration than share of voice because AI recommendations actively shape shortlists rather than just measuring passive exposure.
Engine-level vs aggregate share of model
Share of model varies significantly by engine. A brand might have 60% share of model on Perplexity (which uses live web retrieval and tends to cite brands with recent press coverage) but only 35% on ChatGPT (which relies more on pre-training data and tends to reinforce established brands). Aggregate share of model scores — a single number across all engines — obscure this variance and can produce misleading trend signals. Enterprise measurement should track engine-by-engine share of model separately, with a weighted average as the summary metric for reporting, not as the sole data point for decision-making.
How to use share of model as an actionable KPI
Share of model becomes actionable when it is tracked weekly against a baseline, broken out by query category, and correlated with content and authority-building actions. A brand that publishes three comparison pages and earns coverage in two industry publications should see measurable share of model movement within 6 to 12 weeks on Perplexity and 12 to 20 weeks on ChatGPT. Without a baseline metric tracked before the content programme begins, it is impossible to attribute visibility changes to specific actions. Platforms like GEOscanAI provide the continuous measurement layer needed to close this attribution loop.
● The Verdict
Treat share of model as your primary KPI for AI visibility: a rising share of model for your top buyer-intent keywords is the clearest leading indicator that your GEO and AEO strategy is working.
● Representative share-of-model snapshot
Share of model across ChatGPT, Claude, Gemini, Perplexity.
Illustrative pattern based on category monitoring, not a live reading.
Inclusion is not endorsement.
● People Also Ask
What is a good share of model score for a B2B SaaS brand?
For most B2B SaaS categories, a share of model above 50% on your primary category queries is competitive. Early-stage brands typically start at 0 to 15% before any GEO programme. Mid-market brands with active content and PR programmes often reach 30 to 50% within 6 to 12 months. Category leaders in well-established software verticals routinely exceed 60 to 70% share of model across engines.
How is share of model different from keyword ranking?
Keyword ranking measures position in a link list on a search engine results page. Share of model measures inclusion in a generated text response from an AI engine. There is no position 1 to 10 — you either appear or you do not, and when you appear, your placement within the generated text (first mentioned vs. fifth mentioned) is its own secondary signal. The two metrics can diverge significantly: a brand can rank #1 on Google for a category term and still have low share of model if its authority signals in AI training data are weak.
Which AI engines should I track share of model on?
At minimum, track ChatGPT (highest enterprise buyer usage), Perplexity (fastest-updating, most useful for content experiment feedback), and Claude (growing adoption among technical and professional buyers). Gemini is important for consumer and SMB categories. Tracking all four simultaneously with a tool like GEOscanAI gives a complete picture; tracking only one engine risks optimising for a channel that misrepresents your overall AI visibility.
How many queries do I need to run to get a statistically meaningful share of model reading?
At least 50 queries per category per engine per measurement period — weekly or monthly — to surface meaningful signal above the noise of AI response variability. With 50 queries, you can detect a 15-percentage-point shift in share of model with reasonable confidence. For high-stakes decisions (budget reallocation, agency reporting), use 100 to 200 queries per category to tighten confidence intervals.
Can share of model go down without any action on my part?
Yes — and this is a critical risk signal. Share of model can fall when a competitor aggressively builds third-party authority, when a new entrant earns significant press coverage, when AI engines update their training data with content that underweights your brand, or when your own content programme goes inactive. Declining share of model with no apparent change in your own activity is one of the strongest early warning signals that a competitor is executing an effective AEO programme.
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