By Tangenix · Published 2026-10-23 · Free template

Structured Data Content Audit Template

Compare JSON-LD fields with the actual visible product page and supporting evidence before treating valid syntax as a finished schema implementation.

When to use this template

Use this after adding or changing structured data, especially when templates populate many public pages from shared records.

Choose another approach when: You want to add fabricated ratings, hide unsupported facts in JSON-LD, or assume a syntactically valid result guarantees a rich search appearance.

Gather these inputs

The exact rendered page and its structured-data blocks.
The supported schema type’s current official documentation.
Visible evidence for each material property and a test result.

Copy and adapt the template

Edit the text below and copy it into your own document. Edits stay in this browser field and are not submitted or saved by this page.

Structured Data Content Audit Template: editable text

Worked example

Illustrative example, not a Crany customer result. Replace every example detail with facts you can verify.

A fictional tutorial has Article markup with a date and headline. The rendered headline matches, but the schema describes a public launch while the visible page says the app remains in development. The audit marks that description for correction.
The tester can also confirm JSON-LD parses, but a passing syntax result does not resolve the factual mismatch. After editing, the owner checks the live page again and avoids adding a star rating merely because a competitor’s search result has one.

How to use it

Extract the schema from the rendered page rather than reviewing only a source template.
Match each meaningful field to visible accurate content and the type-specific requirements.
Correct unsupported fields, rerun validation and inspect the deployed result.

Check before using it

Hidden claims do not exceed what the reader can verify.
The schema type describes the actual page purpose.
Rich-result eligibility and actual appearance are reported separately.

What to measure

Record factual mismatches, validation errors and deployed corrections. Valid accurate markup is an implementation result; whether a search engine displays an enhancement remains a separate observation.

Research and sources

Google’s policies require representative visible content and explain that valid markup does not guarantee appearance. The row-by-row audit format is an original implementation aid.

Google: General structured data guidelines — checked 2026-10-23. Accurate structured data that represents visible content; valid markup does not guarantee search appearance.

This original template and worked example were prepared with AI assistance. Source documentation supports the principles stated above; it does not endorse Crany or guarantee this template’s performance.

About Crany

Crany is an AI marketing co-founder in development for software founders and vibe coders, designed to strategize, execute approved marketing and improve based on results. This free resource is usable independently of the product. Join the public waitlist.