Case study · Restaurants
Restaurant SEO case study: menus Google can read, right hours
What we did for a Mexican restaurant group (San Antonio, TX, 3 locations), and what changed.
The outcome
Crawlable menus and correct hours pushed up direction requests
Before
The situation
A Mexican restaurant group in San Antonio came to us with three locations. Its menus were PDFs that Google couldn't read, and two of the locations had the wrong hours listed.
For a restaurant, those details matter more than almost anything else online. People deciding where to eat look at two things first: what is on the menu and whether the place is open. If either is hard to find or wrong, they move on to the next restaurant on the map.
Before
The problem
A PDF menu is a common choice because it is easy to upload. But search engines read a PDF poorly, if at all, so the dishes on it do not help the restaurant show up when someone searches for a particular dish. A PDF is also awkward to read on a phone, which is where most people check a menu.
Wrong hours are worse. A diner who drives to a restaurant that the listing says is open and finds it closed is unlikely to give it a second chance. A diner who sees it listed as closed when it is open simply goes somewhere else. Either way, the restaurant loses the visit.
More people now ask AI assistants where to eat, often for a specific dish. Those assistants rely on information they can read about a restaurant's menu, and a menu locked in a PDF gives them very little.
The work
What we did
Step 01
Turned the PDF menus into crawlable menus.
The menus now live on the website as regular pages that Google can read. Each dish becomes something search engines can match to a search, and diners can read the menu easily on a phone. A text menu is also simpler to keep up to date when dishes or prices change.
Step 02
Cleaned up the restaurants' profiles.
The Google Business Profiles for the locations were cleaned up so each one carries its own correct details. Accurate profiles mean diners can trust what they see before they set off, and Google is more willing to show a business whose details are consistent across the places it checks.
Step 03
Corrected the wrong hours at two locations.
Hours are one of the first things a diner checks, and one of the most damaging to get wrong. With three locations, each profile has to show its own hours, so a diner searching near one location sees that location's opening times, not another's.
Step 04
Added menu schema with dish details.
Schema is structured data that describes the menu and its dishes in a format machines read directly. It spells out what each location serves, so search engines and AI assistants do not have to guess from the page. When someone asks for a dish rather than a restaurant, that detail is what lets a tool match the question to the right place.
After
The results
The crawlable menus and cleaned-up profiles pushed up direction requests, the moment a diner taps for the way to the restaurant. With menus Google can read and hours diners can trust, more people who find the group online now head to one of its locations.
The menu schema also reached AI search. AI assistants started recommending the restaurants when people asked about the best enchiladas in San Antonio, which reaches diners at the moment they are choosing where to eat. Out of respect for the group's privacy, we do not publish its name or anything that would identify it.
The outcome
Crawlable menus and correct hours pushed up direction requests
Next step
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