AI visibility score
A 0–100 per-SKU summary of how a product performs in AI answers — built to be sorted, so a 2,000-SKU catalog becomes a prioritized work queue.
Last updated June 2026
What goes into the score
The four components answer four different questions. Citation count: how often the SKU surfaces across category-typical buyer prompts. Citation position: where in the answer it appears — the first recommendation carries more consideration than a trailing mention. Engine coverage: how many of the six engines cite it, since a SKU strong only on Perplexity is exposed to one vendor's model changes. PDP readiness: how well the product page is structured for AI extraction, including Product schema completeness.
The score is intentionally composite, and the per-component breakdown stays visible. A SKU might cite well on Perplexity but fail schema checks; a single opaque number would hide which gap to fix first. In the v5 model the readiness side is further split into the citation score and the agent-readability score — the two-score read that distinguishes "engines don't recommend it" from "agents can't parse it."
Why it matters for ecommerce
A catalog of 2,000 SKUs cannot be inspected one PDP at a time. The score is the triage primitive: sort ascending, pick a cutoff, and the SKUs below the line become the week's work queue. Without a per-SKU score, teams default to brand-level reporting and never learn which specific products need attention — or which fixes moved which products after they shipped.
A shared number also keeps functions aligned. Recommendations quote an expected score delta, push-to-Shopify diffs show before and after, agency dashboards roll scores by client. Merchandising, SEO, and brand teams argue less when they are reading the same column.
Reading the score: an example
A Shopify brand selling wireless earbuds has 42 active SKUs scoring from 28 (a discontinued color variant nobody cites) to 91 (the flagship model). Sorting reveals a cluster of eight SKUs at 40–55 sharing one structural weakness: short descriptions that never answer battery life or codec support — the exact attributes buyer prompts ask about. The recommended fix is a typed specifications block and an FAQ rewrite per PDP. After shipping, the next two refreshes show the cluster moving into the 60s as ChatGPT and Perplexity begin citing the updated pages (illustrative example).
How it relates to neighboring terms
The score summarizes AI visibility; it is moved by GEO and AEO work; its diagnostic layer is citation analysis, which explains why a score is what it is. Competitor-relative versions of the same idea are share of model and share of voice.
How eCommerce Insights computes it
Per SKU, per engine, on every refresh — weekly on Starter, daily on Growth — with the component breakdown one click deep and every below-threshold SKU paired with a reviewable diff. The methodology is documented in the PDP Score docs.
Related terms
- Citation score — the "does AI recommend it" half of the two-score model.
- Agent-readability score — the "can an agent parse it" half.
- AI visibility — the underlying metric the score summarizes.
- Citation analysis — the diagnostic discipline behind score changes.
- Share of model — the competitor-relative cousin of this metric.
Ask AI about AI visibility scores
Have your preferred AI engine summarize this definition for your catalog.
Frequently asked questions
What is a good AI visibility score?
Why is my score different on different engines?
How fast does the score respond to PDP fixes?
Is the AI visibility score the same as the citation score?
Go deeper
- PDP Score documentation — inputs, weights, and refresh cadence.
- Product AI visibility — the pillar guide — the measurement philosophy in full.
- SKU-level tracking — the score in its operational context.
- AEO Grader — a free single-URL version of the readiness checks.
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