SEO for restaurants and hotels: how to win bookings from Google, Maps and AI
Which sources AI assistants read when someone asks where to eat or where to stay, and five steps to get a restaurant or hotel onto the Google map and into those answers.
Published 7 November 2026
Short answer
Restaurant and hotel SEO is the work that gets a hospitality business found when someone looks for a place to eat or stay on Google Maps, in Google’s results or by asking an assistant such as ChatGPT or Gemini. It rests on five pieces: a complete Google Business Profile, the same name, address and opening hours on every booking and review site, reviews with replies, the menu or the rooms written as text on the business’s own website, and Restaurant or Hotel structured data. AI assistants lean heavily on booking and review sites, so what those sites say counts as much as the website itself.
What is restaurant and hotel SEO
DefinitionRestaurant and hotel SEO is the practice of getting a hospitality business found where bookings are decided today: the Google map, Google’s results, booking and review sites, and the answers of AI assistants. The goal is not website visits but tables and room nights booked.
It differs from SEO in other sectors because almost every search is local and urgent: someone looking for a restaurant usually wants a table tonight, close to where they are. That is why the map listing and the reviews weigh more than blog articles.
The part that works on presence in AI assistants is usually called GEO. What GEO is and how it differs from SEO has its own article; this one covers only what changes for a restaurant or a hotel.
An example: a small hotel in central Lisbon with a restaurant open to the public. It needs one Google profile for the hotel and another for the restaurant, the same details on Booking.com, Tripadvisor and TheFork, recent reviews with replies, and a website where the rooms and the menu can be read as text.
How guests choose a restaurant or hotel today
ContextGuests now choose a restaurant or hotel in three places: the Google map, booking and review sites such as Tripadvisor, Booking.com or TheFork, and increasingly an AI assistant asked “where should I eat in…” or “which hotel do you recommend in…”. The three feed each other.
On the Google map, a business competes to appear among the first results near the guest. On booking and review sites, it competes on rating, number of reviews and position in the city listing. In an assistant, it competes to be one of the few names the answer mentions.
What matters for a hospitality owner is that an assistant’s answer is built largely from what booking sites and reviews say, not only from the business’s own website. How each assistant works under the hood, with its crawlers and its sources, is in the guide on how to appear in ChatGPT step by step.
Which sources ChatGPT and Gemini cite when recommending a restaurant or hotel
DataChatGPT and Gemini mostly cite directory listings, booking sites and the business’s own website. For restaurants, directory listings are the most cited source and reviews weigh more than in any other sector studied. For hotels, assistants consult Booking.com and other booking sites heavily, but link mostly to the hotel’s own website.
For restaurants, the reference is a Yext study published in October 2025, covering 6.8 million citations from ChatGPT, Gemini and Perplexity collected in July and August 2025. In food service, 41.6% of citations came from directory listings and 13.3% from reviews and social media, the highest reviews share of the four sectors analysed.
The same Yext study found that, across sectors, Gemini relies more on brand websites (52.1% of its citations) and ChatGPT on directory listings (48.7%). For a restaurant, that means its own website and its listings do not compete with each other: each assistant leans on a different one.
For hotels, Nicolas Sitter’s AI Hotel Landscape 2026 study analysed 19,579 answers from six models by OpenAI, Google, Perplexity and xAI across 25 cities, in its January 2026 measurement. Gemini 2.5 Flash cited booking.com in 63% of answers, and GPT 5.2 cited it in 53.9%, ahead of tripadvisor.com (20.5%).
The figure that matters most to a hotel is in the same study: between 75% and 91% of the hotel links the assistants gave, depending on the model, went straight to the hotel’s own website rather than to a booking site. Assistants read booking sites to decide which hotels to recommend, but send the guest to the hotel’s website. How Google’s AI picks its sources is covered in the Gemini guide.
Both studies use sets of questions from several countries, measured with the model versions of those dates. They are a guide, not an exact split for any particular city.
Google map, booking sites, website and AI: what weighs in each
ComparisonA restaurant or hotel appears in four different shop windows, and each one is worked on and measured in its own way. The Google map depends on the profile and reviews; booking and review sites, on rating and reviews; the website, on what can be read on it; AI assistants, on all of the above at once.
| Shop window | Where the business appears | What weighs most | How it is measured |
|---|---|---|---|
| Google map | Google Maps and Google’s local results | Complete profile, distance and reviews | Google Business Profile performance |
| Booking and review sites | Tripadvisor, Booking.com, TheFork and similar | Rating, number of reviews and up-to-date details | Each site’s own partner reports |
| Own website | Google and Bing results | Menu or rooms as text, and structured data | Google Search Console and Bing Webmaster Tools |
| AI assistants | Answers from ChatGPT, Gemini, Claude or Perplexity | Booking sites, reviews and website that agree with each other | AI crawler logging and a fixed set of questions for each assistant |
How to rank a restaurant or hotel on Google and in AI search, step by step
StepsRanking a restaurant or hotel on Google and in AI assistants takes five steps, ordered from what pays off first to what takes longest. The first three move the Google map and the booking sites; the last two shape what assistants can read and cite from the website.
- 01 / PROFILE
Step 1: a complete Google Business Profile
The Google Business Profile is what appears on the Google map. Google’s help page puts it this way: “Local results are mainly based on relevance, distance, and popularity.” The same page says businesses with complete and accurate information are more likely to show up. Google’s guidelines add two rules specific to the sector: a restaurant lists the hours when diners can sit down and eat, and a hotel does not list hours at all. If the restaurant operates inside the hotel, each has its own profile, and the hotel’s profile does not add “Restaurant” as a category.
- 02 / CONSISTENCY
Step 2: the same name, address and hours everywhere
Name, address, phone number and opening hours have to match exactly on the Google profile, the website and Tripadvisor, Booking.com, TheFork or any other site the business is listed on. Google’s guidelines ask businesses to represent themselves as they are consistently represented and recognised in the real world, on signage, stationery and branding. An assistant that finds two different sets of hours may give the old one; what to do when ChatGPT gets your company wrong has its own guide.
- 03 / REVIEWS
Step 3: recent reviews and a reply to each one
Reviews weigh on the map and in what assistants read. According to Google’s help page, more reviews and positive ratings can help a business’s local ranking, and replying to reviews shows that the business values guests’ feedback. In food service, Yext’s October 2025 study found reviews and social media made up 13.3% of assistant citations, more than in any other sector analysed. Why third-party opinions count so much in AI is covered in brand mentions and reviews in AI search.
- 04 / WEBSITE
Step 4: the menu or the rooms, as text on the website
A restaurant’s menu and a hotel’s room descriptions have to be written on the page, not only inside a photo or a scanned PDF: what sits inside an image cannot be quoted as text. Each dish with its price, each room type with its capacity and amenities, and the address and opening hours as text. First of all, check whether AI crawlers can read the website, because a site that blocks those crawlers stays out of their answers.
- 05 / MARKUP
Step 5: Restaurant or Hotel structured data
Structured data is an invisible block of code that describes the business to machines. Google recommends using the most specific LocalBusiness sub-type possible and gives Restaurant as an example. Schema.org gives a restaurant properties such as servesCuisine (the type of cuisine), hasMenu (the menu) and acceptsReservations (whether it takes bookings, and where). For a hotel, the Hotel type supports checkinTime and checkoutTime, amenityFeature (amenities) and petsAllowed.
How to know whether Google and AI assistants recommend your restaurant or hotel
MeasurementFor Google, the answer lies in Google Business Profile performance and Search Console. AI assistants offer no provider report, but there is a technical way to know which of the restaurant’s or hotel’s pages they read: logging their crawlers’ visits in the website’s firewall or CDN.
Crawler logging is a firewall or CDN rule that records, without blocking, every request from the assistants’ crawlers, identified by their user agent (the name they announce themselves with) and verified by IP address when the provider publishes its ranges. Those logs show which pages each crawler visits and when.
Each crawler tells you something different. According to OpenAI’s documentation, OAI-SearchBot is used to surface websites in ChatGPT’s search results, ChatGPT-User may visit a page when a person asks ChatGPT a question, and GPTBot crawls content that may be used to train its models. A ChatGPT-User visit to the menu page is the closest thing to knowing that an assistant has read it to answer someone. This is what DoGeo sets up for each client.
What the logs do not show is what the assistant writes in its answer, or what it takes from Booking.com or Tripadvisor without visiting the website. For that you need a fixed set of questions, such as “best seafood restaurant in Porto” or “hotel with parking in central Seville”, asked every month in each assistant.
Keep reading
Do Google and AI recommend you when someone looks for where to eat or stay in your city?
DoGeo reviews your restaurant’s or hotel’s Google profile, whether your name, address and hours match on the booking and review sites you are listed on, and whether AI assistants can read your website, then tells you what to fix first. We need four details: your name, your company, your website and a work email.