Share of Model: a key visibility metric in AI search
Share of Model is how often AI mentions or recommends your brand for the buying questions that matter. Track it by model, compare it with competitors, then act on the signals holding you back.
What Share of Model measures
The percentage of AI-generated answers that mention or recommend your brand when the AI is asked a buying-intent prompt in your category. Tested per model, then averaged.
SoM = ( responses_with_brand_mention
÷ total_buying_intent_prompts )
× 100Composite SoM averages the per-model score across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. Outrigger also tracks a recommended sub-score (positive sentiment + actionable phrasing) and a linked sub-score (your URL appears as a citation).
Why Share of Model replaces keyword rank
One answer, not ten
Google gave 10 blue links per query. AI gives one paragraph. There's no scroll. You're in the answer or you're invisible.
Multiple models, no overlap
The five AI experiences don't agree — a brand ChatGPT cites can be invisible on Gemini. Per-experience SoM gives you the honest read.
Buyer behavior already shifted
69.5% of businesses aren't mentioned by AI. If your category buyers ask AI, your visibility metric is Share of Model. If they ask Google, it's still keyword rank. Most categories are now both.
What a good Share of Model score looks like
Tier bands are Outrigger's product definitions.
Invisible
0–5%
69.5% of businesses aren't mentioned by AI
Emerging
5–25%
Early, inconsistent presence
Recognized
25–60%
Mentioned but rarely the top pick
Category leader
60–95%
Recommended in most answers
Five levers that move Share of Model
The signals that correlate with AI recommendations.
01
Entity & directory sync
Directory listings, Wikipedia, LinkedIn, and consistent entity data
02
Cited-source placement
Reddit, Quora, and industry pages AI already cites
03
Review velocity
G2, Trustpilot, Google, Capterra
04
Press coverage
Outlets AI cites — not PR-wire churn
05
Technical GEO
Schema, structured data, robots.txt
Share of Model FAQ
What is Share of Model?
Share of Model (SoM) is the percentage of AI-generated answers that mention or recommend your brand when an AI experience is given a buying-intent question in your category. Outrigger measures ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews separately, then rolls all five into a composite view.
How do you calculate Share of Model?
Share of Model = (number of AI responses where your brand is mentioned) ÷ (number of buying-intent prompts tested) × 100. Outrigger's AI Monitor tests buying-intent prompts across ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews. The composite SoM is the average across all five experiences. We also break out 'mentioned only' vs 'recommended' vs 'recommended with a link cited' for finer-grained tracking. Prompt volume and monitoring cadence depend on your plan.
Why is Share of Model better than keyword rank?
Keyword rank measures whether you're in a list of 10 blue links on Google. Share of Model measures whether you're in the actual answer the buyer reads. With AI, there's often only one recommendation — the buyer doesn't scroll to the next result. Either you're in the answer or you're not. Share of Model captures that binary in a way keyword rank can't.
What's a good Share of Model score?
A useful score is relative to your own market, prompt set, and starting baseline. Across 2,729 businesses audited, 69.5% aren't mentioned by AI at all. Compare your score with the competitors buyers actually consider, then track whether the same prompt set improves over time.
How do you increase Share of Model?
Five levers recur in our research: directory and entity presence, inclusion in cited sources, review momentum, relevant press coverage, and technical GEO such as schema and structured data. Outrigger identifies the gaps, ranks the next moves, and prepares the work for review.
How often should you measure Share of Model?
Weekly monitoring gives most teams a useful balance between signal and noise. Scan volume depends on the plan and prompt set, and every run uses credits. The important part is using a consistent prompt set so the change over time is comparable.
Establish your AI visibility baseline
Run the free audit to see your baseline and top three improvement levers. Then move the number with monitoring and prepared workflows.