Compare mentions across AI models

Highlights

What you get out of this flow

Outcomes you can expect when you run this playbook inside EShopSet on Shopify.

Side-by-side model mention differences
Prioritize content formats each surface favors
Avoid one-model bias in executive reporting

Why model comparison is pillar content

Shoppers don’t use one assistant. If leadership only hears “AI mentions are up,” you may be winning on one model while losing discovery on another. Comparing models turns a vague AI KPI into a channel mix—similar to organic vs paid.

How it works in EShopSet

AI Presence breaks mentions and citations out by ChatGPT, Gemini, Perplexity, and Google AI Overviews for each tracked query. Use the spread to assign content experiments (e.g., FAQ schema and crisp facts often help Overviews; narrative guides may lift chat models).

Example: electronics accessory brand

Mentions: ChatGPT 18/50 keywords, Gemini 14/50, Perplexity 9/50, AI Overviews 6/50. The team noticed Overviews favored spec tables and retailer roundups. They added structured spec blocks to top 15 PDPs and a comparison chart; Overviews mentions moved 6 → 15 keywords over a month while ChatGPT stayed roughly flat—proof the gap was format, not brand awareness alone.

Playbook

Export or note mention counts by model.
Flag models ≥30% below your best model.
Hypothesize format gaps (tables, citations, freshness).
Ship one format experiment per lagging model.
Re-measure after 10–14 daily syncs.

What to measure

Mention count by model
Model gap (best − worst) week over week
Queries where only 1 of 4 models mentions you