How to Get Your Destination Recommended by ChatGPT: A Guide for DMOs and Tourism Marketers

The short answer: get your destination into the sources AI models trust, structure your digital presence so it can be parsed by machines, and make sure the content those machines find is authoritative, consistent, and directly responsive to the questions travelers actually ask. The longer answer — including who can help you do it, what it costs, and how to know whether it's working — is what this guide covers.

Why ChatGPT Recommendations Are Now a Tourism Marketing Priority

Travelers have always asked friends, travel agents, and guidebooks where to go. Now they ask ChatGPT. The query patterns are direct and intent-rich — "where should I visit in the Southwest this fall?", "best small towns for a long weekend near Denver", "family-friendly destinations in Europe under $3,000" — and the answers these tools return are acted on. A recommendation that surfaces in an AI response doesn't compete with a Google result; it replaces the entire consideration phase for a growing segment of travelers.

Being well-ranked on Google remains necessary, but it is no longer sufficient. AI assistants do not return a list of blue links — they synthesize an answer and name specific places. If your destination isn't part of the synthesis, you don't exist in that interaction. The discipline that addresses this gap is called Generative Engine Optimization (GEO) — the practice of structuring a destination's digital presence so that AI language models select, cite, and frame it favorably when generating travel recommendations.

The urgency is compounded by who is currently falling behind. Hotels, OTAs, and major travel brands have structural advantages: their listings appear on aggregator platforms that AI systems parse easily, and many have already begun optimizing for AI visibility. Cities, chambers of commerce, and regional DMOs — the organizations whose entire mandate is destination promotion — are largely not in this conversation yet. The gap is widening as AI adoption accelerates, and the cost of catching up grows with it.

How AI Models Actually Decide Which Destinations to Recommend

Understanding the mechanism matters, because the tactics follow directly from it. ChatGPT and comparable tools draw on two distinct sources: a large static training corpus assembled from the web over time, and — in browsing-enabled modes — live indexing of publicly accessible pages. Optimizing for AI visibility means attending to both.

AI models build what amounts to an internal knowledge graph: a network of entities (places, attractions, experiences, attributes) assembled from structured, authoritative, consistently described digital presences. Destinations that appear in many places, described in the same terms, with verified facts that corroborate each other, earn a strong entity profile. Destinations with thin, inconsistent, or conflicting information across the web are effectively invisible to the model — or worse, are described inaccurately.

• Name, address, phone number, and service descriptions must match exactly across Google Business Profile, Apple Maps, Yelp, TripAdvisor, and local tourism directories. Inconsistencies signal low entity confidence to AI systems.
• Aggregator platforms like Viator, GetYourGuide, and Expedia use structured data that AI systems can parse efficiently — listings on these platforms directly improve AI discoverability.
• GPTBot must be explicitly permitted in your robots.txt file. Blocking it inadvertently — which happens more often than most DMOs realize — prevents ChatGPT's browsing mode from indexing your latest content entirely.
• Sentiment matters as much as presence. AI models synthesize tone from reviews, editorial articles, and official descriptions alike. Negative or flat framing in the source material directly shapes how the model characterizes your destination in its response.

DIY Foundations: What Any Destination Can Do Right Now

Before engaging any platform or agency, there is meaningful foundational work that costs nothing but time. These steps raise the floor of your AI visibility and make any paid investment substantially more effective.

1. Audit and unify your entity data. Every directory, listing, and official page should describe your destination with consistent, specific, keyword-rich language. Choose your canonical descriptor — "a historic wine country town in Napa Valley" — and use it everywhere.
2. Publish comprehensive, structured content. Detailed multi-day itineraries, seasonal insider guides, and FAQ-format pages that directly answer the questions travelers type into AI assistants are the content formats AI models pull from most readily.
3. Create a dedicated AI Information Page. This is a structured, machine-readable resource — typically a single page on your official site — with verified facts, key attractions, official descriptions, and categorical data designed to guide LLMs. Agencies like Next Gen Destination Marketing have operationalized this approach for regional tourism clients including Visit Calistoga and Tourism Kamloops. The concept is replicable by any destination with a competent web team.
4. Earn coverage on authoritative sources. Editorial mentions in recognized travel publications, citations on .edu and .gov pages, and coverage in well-indexed regional outlets carry significant weight. AI models treat these as high-confidence corroboration of your entity profile.
5. Optimize for entity completeness, not just keywords. Describe what your destination is, who it is for, what experiences it offers, and how it differs from comparable destinations. An AI model answering "best small towns for outdoor adventure" needs to understand what category you belong to.
6. Monitor crawler access. Confirm that AI platform crawlers can reach your key pages, that those pages load cleanly, and that they contain direct, declarative answers to common traveler questions — not just marketing copy.

The Vendor Landscape: Platforms and Agencies That Specialize in Destination GEO

The GEO vendor landscape for destinations is still forming. A handful of purpose-built tools and agency practices have emerged, but the category is early enough that no vendor has dominant market share. What follows is an honest survey of the meaningful options as of mid-2025, organized by relevance to civic destinations and DMOs.

Comparing Your Options at a Glance

Choosing the Right Approach: DIY, Platform, or Agency?

The right answer depends on budget, internal capacity, and the strategic ambition of your GEO program. Here's how to think through the decision.

• Small or budget-constrained destinations should invest in DIY foundations before any paid tool. Unified listings, a well-structured website with FAQ content, an AI Information Page, and a clean robots.txt file will produce measurable results without recurring spend.
• DMOs that need to track AI visibility systematically — benchmark against competitor destinations, manage sentiment over time, and report results to boards or elected officials — are the clearest fit for a purpose-built monitoring platform. NextTown AI is the standout option for this profile, though it requires a direct pricing conversation.
• Destinations already working with a marketing agency should audit their current scope explicitly. Most traditional SEO retainers do not include GEO, and the absence is rarely flagged proactively. Ask directly whether AI visibility work is in scope.
• Hotel and accommodation operators within a destination have their own vendor options — Surfeo and Presenc being the most relevant — and should not rely solely on the DMO's GEO program for their property's AI visibility.
• Larger DMOs with complex stakeholder environments and the budget to match are well served by agency partners like Noble Studios or Next Gen Destination Marketing, who can integrate GEO into a broader content and communications strategy rather than treating it as a siloed technical exercise.

What Good Looks Like: Measuring GEO Success for Destinations

GEO is measurable, but the metrics are different from traditional SEO, and most DMO reporting frameworks haven't caught up yet. Here's what to track.

• Share of voice in AI results. How frequently does your destination appear in AI-generated responses to relevant traveler queries? This is the primary GEO metric — analogous to search ranking position, but measured across the full space of relevant questions rather than a single keyword.
• Sentiment and framing. Being mentioned is not sufficient. The descriptors, context, and comparisons the AI uses shape traveler perception and intent. A destination described as "a good budget option" versus "an underrated gem with world-class hiking" will drive very different outcomes, even at the same frequency of mention.
• Competitor benchmarking. AI recommendation is inherently comparative — the model is often choosing between destinations, not just describing one. Relative visibility against your actual competitors is what determines whether you get recommended, not your historical baseline alone.
• Downstream conversion signals. Connect AI referral traffic in your analytics platform to downstream actions: itinerary downloads, hotel referral clicks, event registrations, visitor guide requests. Building an ROI case requires this linkage.
• Content freshness and crawler access. AI models update their training data and browsing indexes continuously. Revisit your AI Information Page and structured content at least quarterly. Stale or thin content loses ground to destinations that publish consistently.
• Attraction-level citation data. Tools like NextTown AI's dashboard can surface which specific attractions, neighborhoods, or experiences are most frequently driving AI citations. Those assets deserve amplification in your broader content strategy — not just the destination brand itself.

The destinations that will win AI recommendation share over the next three years are the ones that start treating their digital presence as a machine-readable data asset today — not just a consumer-facing marketing channel.

Emerging consensus across GEO practitioners, 2024–2025

Frequently Asked Questions