Structured Data (Schema Markup)

The dominant format is JSON-LD, a small script block in the page's HTML that describes entities using schema.org types like Product, Offer, Review, AggregateRating, FAQPage, BreadcrumbList and Organization. Instead of forcing an engine to infer that "$49" is a price, the markup states it explicitly, in a format every major engine understands.

For traditional search, structured data powers rich results, star ratings, prices, FAQ dropdowns, and helps Google build its Knowledge Graph. For answer engines, the same clarity helps in a subtler way: unambiguous, consistent facts are easier for a model to trust and lift into an answer, and they reduce the risk of the engine getting a detail like price or availability wrong.

Structured data isn't a magic ranking lever, and marking something up doesn't guarantee a rich result or a citation. Its value is reliability: it removes ambiguity about what your page says, keeps facts consistent across your site, and makes your content safer for an engine to quote, which is exactly what GEO and AEO reward.

For a store, structured data is largely product truth in machine form. Accurate Product, Offer and Review markup, price, availability, GTIN, ratings, helps engines represent your products correctly in both rich results and AI answers, and keeps them from citing stale or wrong details.

FAQPage and well-structured Q&A markup are a direct GEO asset: they package buyer questions and answers in exactly the shape answer engines like to extract. On Shopify, much of this comes from the theme and app ecosystem, so auditing what schema your pages actually emit is a high-leverage check.

Illustrative scenario: a store adds Product and Offer schema exposing correct price and "in stock" status, plus FAQPage markup for its top sizing question. Engines can now state the price accurately and lift the sizing answer verbatim, instead of guessing from unlabeled page text.