AggregateRating Schema on Product Pages: Audit Checklist
AggregateRating compresses many review signals into a rating value and count. Because it is compact, errors are easy to miss and can propagate across large catalogs.
Why this matters
The key checks are product association, value consistency and count accuracy. The structured value should represent the same review set shoppers can inspect on the page.
What to check
- ratingValue is within the defined scale
- reviewCount or ratingCount is accurate
- The rating belongs to the current product
- Visible and structured ratings stay synchronized
How to improve it
- Generate rating markup directly from the review source
- Disable markup on products without valid review data
- Test products with zero, few and many reviews
- Audit after switching review providers
Copying a store-wide rating onto every product creates misleading product data. Keep rating scope explicit.
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
Run a store-wide readiness scan and see the repeated product-data gaps first.
Open AI SHOP CHECK