Answer engines use these fields to respond to "how much is," "is it in stock," and "is it any good" questions, often with attribution. Clean offers and aggregateRating data lets an assistant state your price and rating precisely.
Apply Product schema to individual product or offer pages, not to category listings or blog posts. Keep the markup in sync with what shoppers actually see on the page.
Example
{ "@context": "https://schema.org", "@type": "Product", "name": "Trail Running Shoe", "offers": { "@type": "Offer", "price": "89.00", "priceCurrency": "EUR", "availability": "https://schema.org/InStock" } }Place the JSON-LD on the product page and ensure price, currency, and availability match the live listing, since mismatches erode trust and eligibility. Only mark up review data you genuinely display. See the AEO guide and the FAQ schema for AEO page, and brush up on schema markup fundamentals. Validate your markup with Echo by Pyratz's free audit of up to 50 pages.
FAQ
Can I add aggregateRating without visible reviews?
No. Rating data must reflect reviews that are genuinely present on the page. Marking up ratings you do not show risks manual action and undermines the trust AI engines place in your structured data. Echo by Pyratz flags this mismatch during an audit.
Which Product fields matter most for answer engines?
Prioritize name, offers (price, priceCurrency, availability), and, if authentic, aggregateRating. These cover the questions users most often ask. Add brand and sku for disambiguation. Echo by Pyratz helps you diagnose AND fix missing or invalid fields, not just monitor them.
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