Executive Summary: The Shift in How Modern Travelers Discover Brands
The path to booking for travelers, diners, and hospitality guests has fundamentally transformed. Travelers no longer exclusively rely on traditional keyword searches, online travel agencies (OTAs), or static social feeds to plan trips. Instead, they interact directly with conversational AI assistants—asking Google Gemini, ChatGPT, Perplexity, and Microsoft Copilot nuanced, multi-variable questions:
- “What boutique hotels in Florence have an intimate rooftop cocktail bar, historic charm, and rooms under $350 a night?”
- “Which traditional seafood restaurants in Lisbon near Chiado are best for authentic petiscos and local wine pairings?”
- “Where should we book dinner in Kyoto for an authentic kaiseki experience that accommodates English-speaking guests?”
In response, generative engines do not present a standard list of ten blue links. They synthesize dynamic answers using Retrieval-Augmented Generation (RAG)—pulling, summarizing, and citing third-party sources they deem authoritative.
To unpack how AI engines evaluate, select, and cite web content in practice, The FS Agency conducted a 90-day technical analysis of Food & Drink Destinations (FDD), our digital culinary and travel publication.
Using first-party reporting from Google Search Console’s Generative AI Features report and Bing Webmaster Tools’ AI Performance Overview, this case study explores how Generative Engine Optimization (GEO) works for hospitality and travel brands—and why establishing AI Authority requires both on-site optimization and an off-site algorithmic PR strategy.
The Glossary: Demystifying AI Search Jargon for Non-Specialists
Before analyzing the data, hospitality operators, hotel marketing directors, and agency leaders need a clear understanding of the core metrics and terminology defining AI search for hospitality.
- Generative Engine Optimization (GEO): The practice of structuring, writing, and optimizing web content and brand entity signals so that Large Language Models (LLMs) understand, reference, and recommend your business in AI-generated answers.
- LLM Visibility: The frequency and prominence with which your hotel brand, property, or content appears when users query conversational AI models (such as ChatGPT, Google Gemini, Claude, and Perplexity).
- Generative AI Performance on GSC: A dedicated first-party reporting dashboard inside Google Search Console that tracks AI Impressions—measuring how often your canonical URLs are displayed as citation cards or linked sources within Google AI Overviews.
- AI Performance on Bing: A reporting suite within Bing Webmaster Tools that measures AI Citations (the number of times Copilot footnotes your domain as a source) and Cited Pages (the daily breadth of unique URLs Copilot references).
- AI Citations: The specific hyperlinked reference cards, footnotes, or source attributions rendered inside an AI synthesis that credit a website as the factual origin of an answer.
- AI Authority (Algorithmic Authority): An LLM’s statistical trust in a domain as a verified primary source. AI models determine authority by evaluating a publication’s topical depth, structured information gain, factual consistency across the web, and how frequently its editorial insights are cited across the wider digital ecosystem.
Before the Booking: Closing the Hotel AI Discovery Gap to Drive Total Revenue
The new book from Amber S. Hoffman of The FS Agency. Travelers now plan entire trips — where to stay, eat, and spend — in conversations with AI, before they ever reach a booking site. Before the Booking shows hotel owners and operators how to make sure AI can see, understand, and recommend their property.
The book is available on Amazon via Kindle download or paperback. Secure your copy here.
Why a Travel Publication’s AI Performance Matters for Hotels & Restaurants
Large Language Models do not generate travel recommendations in a vacuum. When a prospective guest asks ChatGPT or Google Gemini to plan an itinerary or recommend a boutique stay, the AI relies on Retrieval-Augmented Generation (RAG) to scan the open web for consensus, validation, and context.
An individual hotel or restaurant website cannot be its own unbiased validator. To recommend your property with high confidence, an LLM must find your brand mentioned, contextualized, and praised across authoritative third-party sources—specifically trusted culinary and destination guides.
By analyzing the first-party AI visibility data of Food & Drink Destinations, we can see the exact mechanics of this ecosystem in real time:
- The Training & Citation Pipeline: How search engine LLMs crawl, index, and select third-party editorial content to synthesize travel answers.
- The Blueprint for On-Site Content: What structural formats (clean entity definitions, semantic tables, front-loaded facts) an LLM requires before it will extract and cite information.
- The Ecosystem of Influence: Why earning inclusion on high-citation digital publications is the modern equivalent of high-tier PR—directly feeding the algorithms that recommend hotels and restaurants to travelers.
The Data: 90-Day Empirical Analysis Across Google and Bing
During the 90-day evaluation period, Food & Drink Destinations recorded substantial, continuous visibility across both search ecosystems:
Performance Comparison: Google vs. Bing (90-Day Aggregate)
| Metric / Dimension | Traditional Google Search | Google Generative AI (AI Overviews) | Traditional Bing Search | Bing AI (Copilot Citations) |
| Total Reach / Visibility | 1,613,513 Impressions | 518,028 Impressions | 340,201 Impressions | 41,282 Citations |
| Direct Clicks / Actions | 9,188 Clicks (0.57% CTR) | Unreported by GSC | 1,016 Clicks (0.30% CTR) | Unreported by Bing |
| Daily Average | ~17,538 Imp / day | ~5,756 Imp / day | ~3,780 Imp / day | ~459 Citations / day |
| Active Catalog Breadth | 575 URLs | 467 URLs (81.2% overlap) | N/A | ~50 Cited Pages / day |
| Device Distribution | 56.0% Mobile / 42.9% Desktop | 70.7% Mobile / 27.5% Desktop | N/A | N/A |
How Does Your Hotel Appear in AI Tools? Take our quick 6‑minute self‑assessment today
Your property can be excellent and still be invisible. The gap isn’t quality — it’s legibility: how clearly AI can read what you offer, across rooms, dining, spa, events, and the practical details guests actually search for. This walks you through the audit, then scores the gap.
Before you begin
Open ChatGPT, Gemini, and Perplexity in three tabs. Use them side by side.
For each section, paste the prompt — but swap in your city, neighborhood, and the details a real guest would mention. Talk to it like a person, not a search box.
Score honestly — you’re checking whether AI can describe you specifically enough to recommend. And if something doesn’t apply to your property — no bar, no spa — tap N/A; it won’t count against your score.
Want a second pair of eyes on your result?
Book a free 30-minute call with The FS Agency. Bring your score and we’ll walk through where your gap is, which guest searches you’re losing, and what’s worth fixing first. No pitch, no charge — just a clear read on where you stand.
Book a free 30-minute call →Three Critical Findings from the Data
1. AI Search Represents Nearly One-Third of Total Organic Visibility
On Google, generative AI impressions accounted for 32.1% of total traditional search volume. Generative AI is not a fringe experiment—it is an established discovery layer. Furthermore, with 70.7% of AI impressions occurring on mobile devices, AI Overviews dominate the immediate screen space of travelers actively searching on the go.
2. Copilot Relies on Broad Domain Catalog Breadth
Bing Webmaster Tools demonstrated that Microsoft Copilot cited an average of 49.8 unique URLs every day, peaking at 81 distinct URLs in a single 24-hour window. This reveals that LLMs do not build authority on single, isolated viral articles; they evaluate topical depth across entire domain topic clusters.
3. The Authority Divide: Why LLMs Prioritize Exhaustive Topical Depth Over Single-Topic Pages
The data revealed a stark divergence in how search engine LLMs determine what to cite:
- Shallow / Single-Topic Pages Get Bypassed by AI: Pages on the domain designed for narrow, transactional search queries (such as single-dish recipes or basic overview snippets) captured high traditional search volume through legacy rich snippets, but generated minimal AI citations. LLMs do not need to cite a third-party source for simple, commoditized facts that they already know.
- Deep, Multi-Entity Editorial Guides Earn High AI Parity: Pages that demonstrated genuine subject-matter expertise—providing structured cultural context, historical background, regional taxonomy, and curated recommendations—achieved near 1:1 parity between traditional search and AI Overviews:
/what-is-scotland-famous-for/: 93.3% AI-to-traditional ratio (5,620 AI vs. 6,021 traditional)/portuguese-breakfast-dishes/: 92.8% AI-to-traditional ratio (8,592 AI vs. 9,260 traditional)/authentic-portuguese-snacks.../: 12,931 AI citations- Top regional taxonomy and curated guides averaged 6,773 AI impressions per page.
The Lesson for Hospitality: LLMs award citations to editorial depth and structured information gain. When a publication publishes an exhaustive, well-structured guide to a destination’s culinary or hospitality landscape, AI engines treat that content as a primary source of truth.
What This Means for Hospitality & Travel Brands
1. Re-Engineering Your On-Site Content Architecture
If a boutique hotel or restaurant brand wants to be surfaced when an LLM plans an itinerary for a potential guest, relying on flowery narrative prose is no longer sufficient. LLMs look for extractable facts:
- Information Density & Entity Salience: Clearly define property amenities, dietary specializations, operating hours, neighborhood landmarks, and signature dishes directly under clean
<h2>and<h3>tags. - Front-Loaded Answers: State the core facts in the first 2 sentences beneath each subheading so an LLM crawler can parse and extract the answer without processing unnecessary filler.
- Semantic Schema: Implement comprehensive
Hotel,Restaurant,Menu, andFAQPageschema markup to give LLMs structured machine-readable context.
2. Next-Gen Algorithmic PR: The Death of the Vanity Media Kit
For years, travel and hospitality PR teams evaluated influencers and travel bloggers using surface-level metrics: Instagram follower counts, short-term video views, and third-party Domain Authority (DA).
However, social posts disappear within 48 hours, and traditional DA does not tell you if an LLM actually trusts a site’s factual accuracy.
Algorithmic PR is the practice of earning brand citations in publications that search engine LLMs actively use as training and retrieval data.
When travel and hospitality brands partner with authoritative publications like Food & Drink Destinations, the goal is no longer just a temporary referral link. The goal is to embed your hotel, tasting menu, or tour experience into the underlying index that AI engines query when recommending travel itineraries.
The General Manager’s Guide to Next-Gen PR: Why Your Influencer Strategy Needs an AI Overhaul
If your property is still evaluating content creators based on Instagram follower counts and aesthetic video reels, you are investing in a 48-hour dopamine hit rather than permanent commercial discoverability.
When an influencer posts a carousel of your infinity pool, signature tasting menu, or spa treatment, the engagement algorithm buries that content within two days. Meanwhile, the modern luxury guest is no longer scrolling social feeds to plan high-intent stays—they are asking conversational AI models like ChatGPT and Google Gemini: “What boutique hotel in [Region] offers a farm-to-table chef’s table, historic vineyard views, and private wellness villas?”
To win those bookings, general managers must understand how AI assistants make decisions. Large Language Models (LLMs) cannot physically visit your property; they rely on third-party digital consensus to validate whether your hotel, culinary program, or spa is truly worth recommending.
Earning a glowing review on an authoritative, niche travel publication feeds direct factual proof into the AI’s training and retrieval pipelines. When an AI crawler indexes an in-depth review that explicitly details your amenities, room tiers, chef credentials, and guest experience, your property gets codified as the definitive answer for relevant guest queries for years to come.
Moving forward, your PR and media hosting strategy must demand two non-negotiable criteria from content partners: audience relevance and LLM optimization. Partnering with a generic lifestyle creator with 100k followers yields zero lasting value if search engines do not recognize them as a credible topical authority.
Instead, prioritize specialized creators and digital publications whose audience matches your ideal demographic and whose editorial content is structured with clean semantic headers, entity-rich descriptions, and verified citation footprint. That is how you turn a single comped stay or media dinner into an evergreen algorithmic asset that drives direct bookings on autopilot.
How The FS Agency Supports Hospitality & Travel Brands
Navigating the shift to generative search requires a dual approach: optimizing your owned digital ecosystem and establishing off-site authority across verified citation sources.
- Generative Engine Optimization (GEO) Audits: We evaluate your website’s information architecture, entity clarity, and technical schema to ensure your property is positioned for Google AI Overviews, Gemini, and ChatGPT recommendations.
- Algorithmic Media & Sponsored Placements: Through our proprietary digital publications—including Food & Drink Destinations—and our network of third-party vetted travel media, we build data-backed sponsored editorial campaigns that secure permanent, high-authority citations within AI retrieval engines.
Frequently Asked Questions
Generative Engine Optimization (GEO) is the specialized process of optimizing a hotel, restaurant, or travel brand’s website and digital footprint so that AI engines like Google Gemini, ChatGPT, Perplexity, and Bing Copilot recommend the property in conversational search results. Unlike traditional SEO, which focuses on ranking for individual keywords, GEO focuses on establishing entity authority, structured data, and extractable information that LLMs use to construct personalized travel recommendations and itineraries.
Google AI Overviews provide synthesized, multi-source answers at the very top of search results pages, utilizing Large Language Models to answer complex conversational queries directly. Traditional search displays a ranked list of blue website links or visual rich snippets (like recipe carousels). AI Overviews pull information dynamically from multiple authoritative domains, displaying hyperlinked citation cards that credit the source of the facts used in the summary.
Large Language Models rely on comparative entity data to answer complex, multi-criteria guest queries (such as “What are the best boutique hotels in Provence with historic charm and on-site wine tasting?”). Curated roundups, regional food and stay guides, and structured amenity comparisons provide the contextual relationships and third-party consensus that Retrieval-Augmented Generation (RAG) models need to synthesize balanced recommendations. A single hotel homepage or static brochure page often provides only self-referential marketing copy, which AI engines treat with lower confidence unless validated against third-party editorial guides and structured destination roundups.
First-party AI metrics are official AI measurement tools provided directly by search engines to track AI visibility. Google Search Console tracks Generative AI Impressions, which measure how often a website’s link appears inside an AI Overview. Bing Webmaster Tools tracks AI Citations (the total number of times Copilot references a domain as a footnote source) and Cited Pages (the number of unique URLs from a website cited by Copilot daily).
Traditional influencer marketing focuses on short-term social media visibility, measuring success through follower counts, likes, and video views that typically decay within 24 to 48 hours. Algorithmic PR focuses on securing permanent, structured editorial coverage on digital publications that search engine LLMs actively cite. This ensures that when travelers use AI tools to research recommendations for a destination, the AI continually surfaces and recommends the hospitality property over the long term.
Hospitality brands should request verified first-party AI reporting (such as GSC Generative AI reports or Bing AI Citation metrics) to confirm that a publication possesses genuine algorithmic authority. A high social following or legacy Domain Authority score does not guarantee that search engine AI models trust or cite that creator’s website. Verifying AI citation volume ensures your media budget is spent on partnerships that build permanent search equity.
Yes. AI engines utilize Retrieval-Augmented Generation (RAG) to scan a wide pool of relevant index documents to answer specific user prompts. A webpage with clear semantic hierarchy, high information gain, and accurate entity definitions can be cited as a primary source inside an AI Overview even if it ranks in positions 4 through 10 in traditional search results.
Amber S. Hoffman, Founder The FS Agency
Amber S. Hoffman is the author of Before the Booking and Before the Itinerary, and a travel content publisher. She helps hotels, resorts, and destinations understand how AI systems read, describe, and recommend them.