Glossary

Share of model: definition and examples

The primitive metric for how often a single AI engine surfaces a brand or product across a defined prompt set.

Last updated Q1 2026

Share of model is the percentage of responses from a single AI engine that mention a brand or product, measured across a defined prompt set.

In detail

Share of model has a simple formula: mentions divided by total responses, per engine, per prompt set. A brand with 20 mentions across 100 ChatGPT responses has a 20% share of model on ChatGPT for that prompt set. Run the same prompt set on Perplexity and the number will differ, because the engines retrieve from different source pools and weight them differently.

The metric is a primitive — a single raw number per engine. Every higher-level report (competitive share of voice, category lead, week-over-week change) is built on top of it. eCommerce Insights computes share of model per SKU per engine per week and stores the history so the numbers can be rolled up or drilled into.

Why it matters

A Shopify brand with a 15% share of model in ChatGPT and 5% in Perplexity has a real problem with Perplexity that a single blended number would have buried. Per-engine reporting tells the team where to work and which levers to pull. PDP copy that improves ChatGPT share does not always move Perplexity share because the two engines read different signals.

For a VP of Ecommerce, share of model is the metric that sits next to revenue. A rising share of model in prompts that describe the brand's product category is the leading indicator for AI-driven traffic and agentic purchase referrals.

Example

For example: a hair serum brand tracks a 40-prompt set across ChatGPT and Perplexity. In week one, the flagship 30ml bottle appears in 7 of 40 ChatGPT responses (17.5% share of model) and in 14 of 40 Perplexity responses (35%). The gap tells the SEO team that Perplexity is finding the PDP easily but ChatGPT's answer model is favoring competitors with stronger review-site coverage. The work list that week focuses on getting more independent review placements indexed, not on rewriting the PDP.

Related terms

Share of voice (AI) — the competitive counterpart to share of model.
Prompt tracking — how the underlying data is collected.
AI visibility — the umbrella outcome share of model measures.
Citation analysis — digging into the sources inside the response.
Brand mentions — the raw signal share of model counts.

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Frequently asked questions

How is share of model calculated?

Share of model is mentions divided by total responses, expressed as a percentage. Run a fixed prompt set against a single engine, count the responses that name the brand or SKU, and divide by the total number of responses. Most dashboards roll that number up per week and per prompt cluster so teams can see which topics are pulling visibility up or down.

Why report per model rather than blending engines together?

Each AI engine retrieves and ranks sources differently. A SKU might score 40% on Perplexity and 5% on ChatGPT for the same prompt. Blending the two hides where the work is needed. eCommerce Insights reports share of model per engine per prompt so teams can triage ChatGPT-specific fixes separately from Perplexity-specific ones.

What is a good share of model for a D2C brand?

There is no universal benchmark as of Q1 2026. A useful internal target is the brand's share among a named competitor set. If five competitors show up consistently and the brand shows up in one-fifth of answers, share of model of 20% is parity. Below that is a gap; above that is a lead in that engine and prompt cluster.

Related guides

See share of model for every SKU in your catalog. Start a free trial or read the Wikipedia entry on share of voice for the classical counterpart.