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

Ecommerce Structured Data Audit: A Store-Wide Workflow

A structured data audit should produce a prioritized worklist, not just a list of validation warnings. The most useful approach is to sample products, aggregate repeated gaps and keep product-level evidence.

Why this matters

Start with valid product URLs, collect product objects and offers, score the important fields, then summarize the percentage of sampled products missing each signal.

What to check

  • Sample includes diverse templates and product types
  • Structured values are compared with visible facts
  • Repeated gaps are aggregated by frequency
  • Individual product results remain available for diagnosis

How to improve it

  1. Fix template-level issues before isolated product records
  2. Re-run the same sample after deployment
  3. Use history to distinguish regressions from old gaps
  4. Add monitoring for stores where product templates change often
Common mistake
A validator that only says “valid” or “invalid” may not tell you which missing fields are widespread enough to deserve priority.

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.Structured Reviews for Ecommerce: What to Check Before Marking Up RatingsAudit review and rating structured data while avoiding fabricated, stale or mismatched customer-review signals.
Scan your store

Run a store-wide readiness scan and see the repeated product-data gaps first.

Open AI SHOP CHECK