Guides / Structured data
Updated September 17, 2026 · DDANZG / AI SHOP CHECK

Structured Reviews for Ecommerce: What to Check Before Marking Up Ratings

Review markup can provide useful product context, but only when it represents genuine reviews associated with the product being shown. A scanner should distinguish missing review data from unsafe pressure to fabricate it.

Why this matters

Check whether review or aggregate rating data is structured when real customer reviews exist, and verify that the values match visible review information. Products without reviews should not receive synthetic ratings.

What to check

  • Review data refers to the correct product
  • Rating values and counts match visible information
  • No default five-star values are injected into unrated products
  • Markup updates when reviews change

How to improve it

  1. Integrate structured review data with the actual review provider
  2. Remove stale rating snapshots
  3. Keep product-level and store-level reviews separate
  4. Re-scan after changing review apps or widgets
Common mistake
Structured review data is not a place for marketing claims. Fabricated ratings can mislead shoppers and undermine data quality.

How AI SHOP CHECK approaches the audit

AI SHOP CHECK samples representative product pages, evaluates a fixed set of machine-readable commerce signals and aggregates repeated gaps into a store-wide report. The scan is designed to make remediation easier: you can see the overall score, the percentage of sampled products missing a signal, individual product scores and a short prioritized action plan.

The score is an independent readiness metric. It is not issued by OpenAI, Google, Shopify or another shopping platform, and it does not guarantee inclusion, ranking, traffic or recommendation. Use official platform documentation for channel-specific eligibility and feed requirements.

Use the result as a baseline

Save a scan before changing your theme, structured data, catalog import or policy configuration. Re-scan after the change and compare the result. For stores that change frequently, recurring monitoring can help surface score drops that deserve investigation.

Related guides

Product JSON-LD for Ecommerce: A Practical Audit GuideHow to inspect Product JSON-LD on ecommerce pages and spot missing names, offers, images, identifiers and policy signals.Product Schema Markup: What a Store-Wide Check Should Look ForA store-wide approach to reviewing Product schema markup across ecommerce templates and product types.AggregateRating Schema on Product Pages: Audit ChecklistA focused checklist for AggregateRating consistency, review counts and product-level structured data.
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