When travellers ask an AI assistant for a family-friendly hotel in Spain, the answer draws on a mix of sources before anyone visits a provider’s website. Which sources form those answers, and which brands appear, is therefore a relevant question for travel and hospitality companies. Peak Ace analysed 1,000 standardised prompts across ten industries to investigate this. This article presents the travel and hospitality findings and places them in the context of the wider benchmark.
Note: This article is an excerpt from the LLM Industry Benchmark. The complete analyses, including results for each industry, are available in the benchmark itself.
In short:
- In our benchmark, comparison formats lead three out of four search intents for travel and hospitality.
- For criteria and advice questions, how-to guides take the lead.
- Category pages rank second in three of four intents, so bundled destination and accommodation information plays a larger role than in many single-format answers.
- All findings refer to this prompt set, this time period, and this setup.
Methodology and data corpus
The travel and hospitality findings draw on the same data corpus as the wider benchmark. The following sections describe its structure and the key figures derived from it.
Prompt set and structure: The prompt set comprises 1,000 standardised bottom-of-funnel prompts, with 100 commercially relevant prompts per industry across ten topic clusters. The prompts are brand-neutral and geared towards purchasing and booking decisions.
Search intent: Each industry covers four search intents, with 25 prompts per intent:
- Best/Top/Test Winner: Recommendation questions, such as “Which hotel is best for a family holiday in…?”
- Comparisons: Contrasts, such as “Which is better, A or B?”
- Prices/Where to buy: Questions about costs and booking sources, such as “Where can I book and compare…?”
- Criteria/Advice: Questions about selection criteria, such as “What should I pay attention to when…?”
Market, language and time period: The prompts are in German and geared towards the German market. Responses were analysed from 1 to 31 August 2026.
Systems and repetitions: Gemini, ChatGPT, and Google AI Overviews were observed. Each prompt was executed 15 times, five times per system. Individual responses were considered samples, which allows for a better classification of fluctuations.
Data source: The data comes from Peec AI exports. Fifty brands per industry were monitored, and the report focuses on the top 20 brands by visibility.
Key figures:
- Visibility: Shows how frequently an entity appears in the responses to the prompt set.
- Share of Voice (SOV): Describes the proportion of an entity among all mentions in the observed dataset.
- Sentiment: Rates the tone of the responses on a scale of 0 to 100.
- Retrieval: Means that a source has been accessed to inform a response. It does not automatically mean that the source was cited.
Search intents and source patterns in travel and hospitality
Search intent appears to influence the type of source that shapes a response. Travel and hospitality follows the cross-industry pattern for all four intents, although the order of formats differs in some lists.
The table shows the leading source types per intent for the sector, in descending order:
| Intent | Leading source types in travel and hospitality (descending) | Leading type across all industries, share of retrieval |
|---|---|---|
| Best/Top/Test Winner | Comparison; Category page; Listicle; How-to guide | Comparison 28.31% |
| Comparisons | Comparison; Article; Product page; Listicle | Comparison 32.65% |
| Criteria/Advice | How-to guide; Category page; Article; Listicle | How-to guide 22.33% |
| Prices/Where to buy | Comparison; Category page; How-to guide; Listicle | Comparison 22.64% |
The cross-industry shares describe the overall retrieval mix, not shares specific to travel and hospitality. The benchmark publishes the order of leading source types for each sector, not their individual retrieval shares. “Other” is a residual category and not a specific format.
Four patterns stand out in the travel and hospitality data:
- Comparisons lead three out of four intents. They appear not only in explicit “A versus B” questions, but also in recommendation and pricing questions.
- Category pages come second in three lists. Recommendation, advice and pricing questions draw on pages that bundle destinations and accommodation options.
- How-to guides lead advice questions. They also appear in recommendation and pricing lists, which suggests that planning support complements the evaluation of options.
- Listicles appear in all four lists but lead none. Round-ups are part of the mix, but they do not dominate any intent in this sector.
The pattern is not universal across industries. This may indicate that the relevant formats depend on the decision a traveller is making, so the sector serves primarily as a starting point for the analysis.
Travel and hospitality in context
For the sector, the benchmark reports the following snapshot values, compared with the average across all ten industries:
| Metric | Travel and hospitality | Average across all ten industries |
|---|---|---|
| Visibility | 4.45% | 6.94% |
| Share of Voice (SOV) | 3.40% | 3.72% |
| Sentiment (0 to 100) | 55.1 | 54.58 |
Concentration scores describe how mentions are distributed within the top 20 of the sector. The HHI for travel and hospitality is 692, and the top 3 share is 34 percent. This means that about a third of the normalised top 20 SOV falls on three entities, while 66 percent is spread across the remaining entities. This may indicate a relatively broad distribution of mentions in this setup. These metrics are based on normalised SOV shares and are not market shares.
Sentiment describes the tone of responses in the dataset. It is not an assessment of the quality of a travel experience, a provider’s reputation, or real customer satisfaction.
Which entities appear most often?
In this prompt set, Booking.com (10 percent visibility), Google Maps (8 percent) and TUI (8 percent) are the three entities with the highest visibility during the observation period. This is a snapshot of the benchmark dataset, not a ranking of brand strength or market leadership.
These three entities serve different tasks in a travel decision. Booking.com aligns with questions about accommodation, Google Maps with location-based orientation, and TUI with package holidays. The presence of platforms alongside travel providers may indicate that commercial questions cover more than the selection of a single hotel.
What this means for GEO and Digital PR
The data reveals a trend: depending on the intent, responses in this sector draw on different types of sources. The aim is not to be visible everywhere, but to understand which questions a brand can answer well and where visibility is useful. Our results provide clues for the following priorities:
- Handle answer types separately. A comparison should explain suitability alongside advantages and disadvantages. A guide should clarify the criteria for a trip. A category page should offer useful distinctions between options rather than serve only as a route to individual listings.
- Keep information consistent across sources. Location, accommodation features, inclusions and booking conditions should match on the website, in comparisons and in third-party listings. Contradictions may increase the risk of inaccurate or outdated statements in generated answers.
- Link criteria to relevant offers. How-to guides lead advice questions. Selection criteria can be explained clearly and connected to suitable accommodation or holiday offers, so that the advice and the offer do not diverge.
- Prioritise Digital PR. Comparison and editorial sources appear frequently in the retrieval mix. Identify the most important third-party sources per intent, destination, traveller profile or holiday type, and check what information they carry about your brand. Not every publication is equally relevant.
- Assess each traveller task separately. Questions about a hotel, a booking platform and a package-holiday provider represent different decisions and need different question sets.
- Treat monitoring as an ongoing task. Brand mentions and retrieved sources can be observed separately, ideally with a consistent prompt set. Analysis by intent and system complements traditional search and booking metrics.
Expert tip: Gordon Herenz, Team Lead Content Strategy & Digital PR at Peak Ace, recommends a holistic content strategy. Build comparison and list formats where they make sense, seek placement in relevant rankings and listicles, and strengthen citable authority on independent third-party sources through consistent Digital PR.
Limits of explanatory power
The results are valid only for this specific prompt set, time period, and setup. They do not represent a complete measurement of the visibility of the sector or of any brand in AI systems. In particular, the following should be noted:
- The prompt set shapes the outcome. Different questions or wording can lead to different answers.
- Individual responses are samples. Small differences should not be overinterpreted.
- Three systems are not one system. Gemini, ChatGPT, and Google AI Overviews cannot simply be equated, and patterns may differ by system.
- Retrieval is not citation. A retrieved source does not necessarily appear as a citation in the final answer, and a brand’s presence in a source does not automatically lead to a brand mention.
- Knowledge and search are not separate. Therefore, no causal mechanisms can be derived from the source mix.
- Dialogue and personalisation are missing. Typical booking situations cannot be reliably described on this basis.
- Sentiment is not a quality assessment. It describes the tone of the responses, not actual traveller satisfaction.
Read the full benchmark
This article provides an excerpt. The complete LLM Industry Benchmark includes, among other things:
- The source mix for each of the ten industries and all four intent types
- Visibility, SOV and sentiment within the benchmark
- HHI and top 3 share within the top 20 of each industry
- The leading entities observed per industry
- The cross-industry shares of the source types
- The industry scorecards and the complete methodology
Download the full LLM Industry Benchmark.
Already had a look at our benchmark and want to strengthen your brand’s presence in AI? Get in touch with Peak Ace’s LLMO & GEO team.
FAQ: Travel and hospitality in LLMs
How visible are travel and hospitality brands in AI systems?
A single, cross-system visibility figure cannot be quantified reliably, because there is no unified and complete measurement methodology across all models, markets, prompts, and points in time. In our benchmark, travel and hospitality reaches an average visibility of 4.45 percent for this prompt set, aggregated across Gemini, ChatGPT, and Google AI Overviews.
Which travel and hospitality brands appear most often?
In our benchmark, Booking.com, Google Maps and TUI appear most often in this prompt set and time period. These results describe the observed dataset, not who is generally the most visible.
Which sources do LLMs use for travel questions?
In our benchmark, comparison formats lead recommendation, comparison and pricing questions, while how-to guides lead advice questions. Category pages come second in three of four lists. This applies only to the examined prompt set and time period.
Does AI visibility depend solely on one’s own website?
Comparisons, articles, guides, category pages and other third-party formats play a significant role. It is therefore worthwhile to review specific URLs and separate your own content from relevant third-party sources.
Is retrieval the same as a citation?
Retrieval means that a source has been accessed. It does not automatically mean that the source is cited in the final answer or that the brand is mentioned.
How can travel brands test whether their measures are effective?
In three steps: a source and content audit, a check of the timeliness and consistency of key information, and then a remeasurement with the same test questions under the same conditions.
What is a GEO strategy?
Generative Engine Optimisation (GEO) combines SEO, content, Digital PR and monitoring to improve visibility in AI-generated answers. Measures are most effectively prioritised according to the questions a target audience asks and the sources it relies on.
