How Travel Brands Win (or Lose) in AI Recommendations

The New First Touchpoint for Travel

"Where should I stay in Lisbon on a budget?" Three years ago this query was typed into Google, generating a results page full of travel blogs, OTA listings, and paid hotel ads. Today, it is increasingly asked in a ChatGPT window — and the answer names two or three specific hotels or booking platforms and explains why each is recommended.

For travel brands, this shift is both an opportunity and an existential threat. The opportunity: a first-cited position in an LLM travel recommendation drives bookings with extraordinary conversion rates. PromptAds data shows that booking conversions from LLM-cited travel brands convert at 3.2 times the rate of bookings initiated by Google search. The threat: brands not in the LLM response are not in the consideration set at all.

Booking.com vs Expedia: A PromptScore™ Case Study

Both Booking.com and Expedia are global OTA leaders with comparable market positions in traditional search. Their LLM visibility profiles tell a different story.

Booking.com scores consistently in the 75–82 range on PromptScore™ for hotel recommendation queries. Expedia scores in the 31–39 range for the same queries. The gap is not primarily driven by size — it is driven by content strategy.

Booking.com has invested heavily in destination editorial content: city guides, neighborhood breakdowns, seasonal travel recommendations, local tips. This editorial layer means that when a user asks "what's the best neighborhood to stay in Barcelona?", Booking.com's content is directly cited. Expedia's strategy has been more product-forward — listing inventory rather than creating editorial context — and LLMs absorb editorial context more readily than inventory listings.

The lesson: in travel, content authority drives LLM citations more than market share.

Why Independent Hotels Are Disappearing From AI Responses

The most concerning pattern in travel LLM visibility is the near-total absence of independent hotels from AI recommendations. When users ask ChatGPT "where should I stay in [city]?", the responses cite OTAs (Booking.com, Hotels.com, Expedia) or major hotel chains (Marriott, Hilton, Accor). Independent hotels — which represent over 40% of global hotel inventory — are almost entirely absent.

The reason is structural. Independent hotels rarely publish editorial content about their destinations, rarely invest in third-party review programs, and are rarely mentioned in travel media. Their LLM citation profile is close to zero, even when they represent the best lodging option in their market.

This is a distribution crisis more severe than the rise of OTAs in the 2000s. At least with OTAs, hotels appeared in listings. In the AI era, they simply don't appear.

4 GEO Tactics Specific to Travel

1. Destination FAQ Content

Create comprehensive FAQ pages for each destination you serve: "What's the best time to visit Lisbon?" "Which neighborhood is best for families in Barcelona?" "Is it safe to travel to Marrakech solo?" These questions are asked constantly to LLMs, and brands that have published direct, well-structured answers to them are cited consistently. Each FAQ page should be 800–1,200 words of genuine local expertise, structured as questions and answers.

2. Review Schema Markup

Implement schema.org Hotel, LodgingBusiness, and Review markup across all property listings. This makes your review data machine-readable in a format that both Google and LLMs can process efficiently. Combined with a systematic review-generation program (email sequences post-stay, response protocols for negative reviews), this dramatically improves your review corpus quality.

3. Travel Media Partnerships

Earn mentions in travel media — Condé Nast Traveler, Lonely Planet, Time Out, TripAdvisor editorial, travel podcasts with transcripts. These sources are heavily indexed by LLMs and carry disproportionate citation weight. A hotel featured in a Condé Nast "Best Hotels in Lisbon" list will appear in LLM responses to Lisbon hotel queries for years.

4. Conversational Comparison Content

Publish honest comparisons: "Booking.com vs Hotels.com: Which is better for last-minute deals?" "5-star hotel vs luxury vacation rental in Tuscany: which offers better value?" Comparison content is extremely popular with LLMs because users ask comparison questions constantly. Even OTAs and hotel chains can use this tactic to dominate specific query types.

The Business Impact

The conversion data is compelling, but the strategic significance goes beyond immediate conversion rates. A travel brand that consistently appears in LLM recommendations builds a credibility moat that traditional advertising cannot replicate. Users who receive an AI recommendation experience it as unbiased expert advice — not advertising. The implicit endorsement of "ChatGPT recommends" is more powerful than any banner ad or paid search click.

For independent hotels, the opportunity is time-sensitive. The OTAs are already investing in LLM visibility. Independent properties that start their GEO programs now can, for the first time in twenty years, compete with OTAs for first-position recommendations — because LLMs value content quality over distribution scale.