GEO for Hotels: What Is Generative Engine Optimization and How Do You Benefit from It?

GEO: a new discipline, not a new buzzword

The term GEO — Generative Engine Optimization — emerged in 2024 from academic research at Princeton University and Georgia Tech. It describes the set of practices that help content appear in responses generated by large language models (LLMs) — ChatGPT, Perplexity, Claude, Gemini and their successors.

The distinction from SEO is fundamental:

• SEO: your site appears in a list of results. The user chooses whether to click.
• GEO: your property is mentioned (or not) in a direct answer to a question. There's no list — there's a recommendation.

This difference changes everything. In SEO, ranking 3rd on a query still delivers 10-15% of traffic. In GEO, if you're not in the answer, you don't exist. The logic is binary: recommended or invisible.

How AI decides who to recommend

To appear in AI responses, your property must pass three successive filters:

1. Access: can AI crawl your site?

The first filter is technical. If GPTBot or ClaudeBot are blocked in your robots.txt, LLMs can't learn from your content. Entry point blocked = guaranteed invisibility. Our data shows that 64% of hotel websites block at least one major AI bot — usually without knowing it.

2. Understanding: does the AI grasp what you are?

Once it can access your site, the AI needs to understand it. The machine vocabulary for describing a hotel is schema.org: Hotel, LodgingBusiness, with its properties (starRating, amenityFeature, priceRange, checkInTime). Without this vocabulary, the AI knows you exist but little else — and can't recommend you for a specific search.

3. Relevance: do you match the query?

The third filter is relevance. For an AI to recommend you to a traveller searching for "a boutique hotel with pool and spa in Bordeaux, under €250/night", it must find this information in your content and structured data. If your description is generic or incomplete, you won't be selected — even if you perfectly match the request.

The 5 pillars of hotel GEO

Pillar 1 — Allow AI bots (absolute priority)

This is the prerequisite. In your robots.txt, verify that GPTBot (OpenAI), ClaudeBot (Anthropic), Google-Extended (Google) and PerplexityBot (Perplexity) are not blocked. Without specific rules, these bots are allowed by default. The problem usually comes from a Disallow: / rule applied to User-agent: * that blocks everyone, or a WordPress security plugin that added these exclusions without your knowledge.

Pillar 2 — Complete Hotel schema.org

JSON-LD schema markup is the vocabulary AI systems use to identify and classify your property. For a hotel, the essential properties are:

• @type: "Hotel" or "LodgingBusiness"
• name: the exact property name
• starRating: official classification
• priceRange: price range indication (€, ££, etc.)
• amenityFeature: list of amenities (pool, spa, parking, restaurant...)
• checkInTime and checkOutTime
• address with full PostalAddress
• telephone and email
• numberOfRooms

These properties allow an AI to precisely respond to "4-star hotel with spa, around €200/night, central Lyon" — a query impossible to satisfy with incomplete schema.

Pillar 3 — The llms.txt file

llms.txt is a text file placed at your site root (myhotel.com/llms.txt). It presents your property directly to LLMs, in natural and structured language. Unlike schema.org, it's not mechanically parsed — it's read by the model and integrated into its understanding of your hotel.

A good hotel llms.txt includes: property description, category, precise location, distinctive amenities, policies (breakfast included, pets welcome...), competitive advantages and the types of stays for which it's particularly well-suited.

Pillar 4 — Conversational and specific content

In GEO, your site content must answer the questions travellers ask, not the keywords they once searched. Practically: a "Spa" page shouldn't say "Our 500 sqm spa offers relaxing treatments" — it should say "Open daily 9am–9pm, our 500 sqm spa features 8 treatment rooms, hammam, Finnish sauna, indoor jacuzzi and covered pool. Booking recommended 24 hours in advance. Complimentary for residents, day access available for non-residents."

This second version answers the questions travellers actually ask their AI: opening hours, availability for non-guests, specific equipment types.

Pillar 5 — Trust and authority signals

LLMs give more weight to properties that appear in sources they consider reliable. For a hotel, this means: a verified Google Business Profile, mentions in travel media or blogs, numerous and recent reviews on major platforms, and consistency of information (same address, same phone number, same name) across all sources.

Measuring your GEO score

Unlike SEO — where you can see your ranking on a query — GEO is harder to measure. There's no "position 1" in an AI response. You're either mentioned or you're not.

Measurement uses two complementary approaches:

1. Technical signal auditing: this is what AIscore does — verifying that the prerequisites (robots.txt, schema, llms.txt, metadata) are correctly configured. A good AIscore is a necessary, but not sufficient, condition for good AI visibility.
2. Real presence testing: asking ChatGPT, Perplexity and Gemini directly the questions that match your positioning (e.g., "design hotel with pool in Montpellier") and noting whether your property appears in the responses.

Where to start?

If you've never optimised for GEO, here's the recommended priority order:

1. Scan your site on AIscore to identify priority blockers (5 minutes)
2. Fix robots.txt if any bots are blocked (5 minutes)
3. Add or complete Hotel schema.org (1-3 hours depending on your CMS)
4. Create a llms.txt (1-2 hours of writing)
5. Rework descriptions to answer conversational questions (depending on site size)

These five actions represent a few hours of work for a potentially significant gain on a rapidly growing channel. The window of opportunity is open — and it won't remain so for long.