LocalBusiness Schema

LocalBusiness schema is the primary entity declaration Schema.org offers for physical local businesses — the structured-data block that tells Google and AI systems exactly what a business is, where it operates, and how to reach it, in a format machines can parse without guessing.

Subtypes relevant to Indian businesses

Schema.org defines dozens of LocalBusiness subtypes, and picking the right one matters more than most businesses assume — a generic LocalBusiness declaration works, but a specific subtype gives search engines and AI systems more to work with:

• MedicalClinic / Physician / Dentist (healthcare)
• Restaurant / FoodEstablishment (food and hospitality)
• LodgingBusiness / Hotel (accommodation)
• EducationalOrganization / School (education)
• BankOrCreditUnion (banking)
• RealEstateAgent (real estate)
• Store / ClothingStore / ElectronicsStore (retail)

Essential fields

• name: the exact business name, matching the GBP down to punctuation
• address: a PostalAddress object with streetAddress, addressLocality, addressRegion, postalCode, and addressCountry
• telephone: the primary business phone number
• geo: GeoCoordinates — latitude and longitude
• openingHoursSpecification: hours for each day of operation, including any special or seasonal hours
• sameAs: links to directory profiles and social media pages, tying the schema entity to the business's presence elsewhere on the web

Of these, sameAs is the field most businesses skip, and it's one of the more useful ones — it's how a schema block on a website connects to the same business's entity → on Google, Facebook, JustDial, or Practo, reinforcing that all of those profiles describe the same real-world entity rather than leaving each platform's data to stand alone.

Why LocalBusiness schema matters for AI

LocalBusiness schema is how AI systems extract precise entity data from a website rather than inferring it. Without schema, an AI system has to read unstructured page text and guess at business name, address, category, and hours — a slower, less reliable process that's more prone to getting a detail wrong. With schema, that same data is declared explicitly in a format the model can read directly, the same way a form field is easier to parse than a paragraph describing the same information.

Multi-location note

Each location page needs its own LocalBusiness schema with that location's specific data — name, address, hours, rating. A single schema block on a central /locations/ page, covering every branch in aggregate, doesn't give AI systems or Google what they need to correctly identify and cite a specific branch. See local schema → and multi-location SEO → for the operational detail on running this across dozens or hundreds of locations at once.

Common mistakes

The most common mistake is choosing the generic LocalBusiness type when a more specific subtype exists and applies — a dental clinic declaring itself as plain LocalBusiness instead of Dentist loses the extra structured fields (like accepted insurance or specific procedures) the more specific type supports. The second mistake is letting the schema drift out of sync with the GBP over time — hours change, a new location opens, a rating shifts — while the JSON-LD block on the website stays frozen from whenever it was first added.

Validating implementation

Google's Rich Results Test tool checks whether a LocalBusiness schema block parses correctly and which rich result features it's eligible for. Running it after any change — not just at initial launch — is the only reliable way to know the schema still works, since a small syntax error can silently break extraction while the page itself renders normally to a human visitor.

Adjacent concepts

LocalBusiness schema is one instance of structured data → more broadly, written in JSON-LD → using Schema.org → vocabulary. sameAs connects it to entity authority → and, for businesses with a Wikidata presence, to Wikidata → itself.

Related terms: Structured Data → · Schema.org → · JSON-LD → · sameAs → · Local Schema → · Multi-Location SEO →

Example: A hotel chain's Gurgaon location page carries LodgingBusiness schema declaring: name "The Grand Gurgaon," the Gurgaon address, coordinates, "hasAmenity" pool and gym, openingHoursSpecification of 24 hours, and an aggregateRating of 4.6 from 890 reviews. AI systems extract all of these attributes directly when answering a hotel-recommendation query, rather than inferring them from the page's marketing copy.