Glossary

AI visibility score: definition and examples

eCommerce Insights's 0–100 per-SKU metric combining citation count, citation position, engine coverage, and PDP readiness into a single read.

Last updated Q1 2026

In detail

The AI visibility score is eCommerce Insights's per-SKU summary metric, scaled 0 to 100, designed to be read per-SKU per-engine and rolled up to catalog level. It combines four signals: citation count (how often the SKU is surfaced across the prompt set), citation position (where in the answer the SKU appears), engine coverage (the breadth of engines citing it), and PDP readiness (how well the product page is structured for AI extraction, including Product schema completeness).

The score is intentionally composite. A SKU might cite well on Perplexity but fail schema checks; the score flags both, and the per-component breakdown is always visible in the dashboard. A single number alone would obscure the source of the gap.

Why it matters

A catalog of 2,000 SKUs cannot be inspected one PDP at a time. The score is the triage primitive: sort descending, pick a cutoff, and the 150 SKUs below the line become the week's work. Without a per-SKU score, teams default to brand-level reporting and never surface which specific products need attention.

The score is also the vocabulary eCommerce Insights uses across features. Recommendations quote the expected score delta. Push-to-Shopify diffs show the before/after. Agency dashboards roll scores by client. That shared number is how teams stay aligned across merchandising, SEO, and brand work.

Example

For example: a Shopify brand selling wireless earbuds has 42 active SKUs. The AI visibility score ranges from 28 (a discontinued color variant nobody cites) to 91 (the flagship black over-ear model). Sorting by score reveals a cluster of eight SKUs scoring 40–55, all of which share the same structural weakness: short PDP descriptions that do not answer battery life or codec support. The recommended fix is to append a typed specifications block and rewrite the FAQ section. eCommerce Insights predicts a score lift to 62–70 after the fix; after one week of rescanning, six of the eight land in that range. The outlier two need additional work on review aggregation.

Related terms

Product AI visibility — the outcome the score measures.
AI visibility — the broader category.
SKU-level AEO — the discipline the score guides.
GEO (Generative Engine Optimization) — the umbrella acronym.
AEO (Answer Engine Optimization) — the answer-surface discipline.

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Frequently asked questions

What goes into the score?

Four components, weighted and normalized to a 0 to 100 scale. Citation count measures how often the SKU appears. Citation position measures where in the answer it appears. Engine coverage measures how many engines cite it. PDP readiness measures structured data, schema, and answer-readiness of the product page. The exact weights are tuned and published in eCommerce Insights's methodology doc.

Is the score comparable across brands?

Within a category, yes. Across categories, be careful — a beauty brand and a furniture brand sit in different query-volume environments, and raw scores are not directly comparable. eCommerce Insights exposes category-relative percentile rankings alongside the raw 0 to 100 score to make cross-category reading safer, as of Q1 2026.

How often does the score update?

Weekly on standard plans. Daily is available on larger plans. Engine-side volatility is significant week to week — answers drift as the underlying models update — so single-week reads are noisier than four-week averages. The eCommerce Insights dashboard shows both the latest value and the rolling average.

Related guides

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