LocalBusiness schema markup and AI Overviews: does it actually help?

What LocalBusiness JSON-LD actually is

Every normal web page is written mostly for people to read. Schema markup is a small, separate block of code on the same page, written in a format called JSON-LD, aimed at machines instead. It states facts about a business plainly and in a standard structure, rather than leaving a search engine or an assistant to work them out from sentences and page layout.

For a local trade business, a LocalBusiness schema block typically carries:

• Name — the business's legal or trading name, stated once, exactly.
• Address — street, city, postal code and country, in structured fields rather than free text.
• Phone number — in a single, consistent format.
• Opening hours — including any exceptions, in a structured day-and-time format.
• Service area — the city, region or radius the business actually serves.
• Price range — a general indicator such as "€€", where relevant.

None of this is new or exotic. It is a long-standing, published standard (schema.org), used mostly so search engines can build things like map listings and knowledge panels more reliably.

What we actually know about schema and AI recommendations

Here is the plain version: no public source from OpenAI, Google or Google's Gemini team confirms that adding LocalBusiness schema, by itself, causes a business to be named in a ChatGPT answer or a Google AI Overview. We have not seen that claim published anywhere, and we are not going to repeat it as if it were.

What is reasonable to say is narrower. Structured data makes a business's core facts — name, address, phone, hours — easier for any system to read correctly and match consistently across sources. That is plausibly useful groundwork, in the same loose way that consistent listings across directories seem to help (see the five fix categories below). It is an observed correlation at best, not a documented ranking rule. Treat any claim that schema markup "gets you into AI answers" with the same scepticism you'd apply to a guaranteed-rankings pitch.

Where this fits with the five fix categories

Schema markup, if you have a developer who can add it cleanly, mostly supports one of the five fix categories we check and write up in every report: structured business details — whether categories, service area, hours and website link are filled in and match the website. The other four — review volume and recency, listing consistency across directories, review response coverage, and service and area wording — are not things schema can fix on its own; they depend on your actual reviews and listings, not a code block.

What the snapshot does and does not do

To be clear before you order: the AI Visibility Snapshot checks and reports. It does not write or install schema markup, and it does not touch your website's code. If a report flags structured business details as a gap, it names what's missing and where it likely needs to go — the writing and installing is yours or your developer's to do, or a separately quoted one-off correction we can scope afterward.

Check it with the snapshot

Rick runs a fixed set of prompts for your trade and town in ChatGPT, Gemini and Google AI Overviews, across at least three separate sessions at different times, and compares what comes back against your actual listings. You get a short PDF: which businesses were named instead of you, and the three to five changes most likely to help. See the full method or what's inside the report.

49 EUR, one-off, intro price. PDF delivered within 48 hours.