From “never mentioned" to the default recommendation in AI travel planning

The Challenge

This operator already had what most hotel groups want: a distinctive, personality-driven guest experience and prime, city-center locations.

But when travelers began planning trips inside AI assistants (“best boutique hotel in Midtown,” “where to stay near Union Square,” “pet-friendly hotel in the Gaslamp,” “unique boutique hotel in Seattle”), the recommendations skewed toward:

• mega brands with massive web footprints,
• OTAs and listicles that AI systems frequently cite,
• competitors with clearer “reference-style” signals (FAQs, amenities, policies, neighborhood context).

In short: they were famous to humans, but fuzzy to machines, and AI answers were stealing the first impression.

The Solution

They adopted Meridian to turn a highly differentiated hotel brand into an AI-readable, citation-friendly one without losing voice, vibe, or design.

1. Built an “AI Travel Prompt Map” for every city

Meridian tracked the exact prompt categories that drive bookings:

• “Where to stay” (city + neighborhood + landmark intent)
• “Best boutique hotel for ___” (style, couples, solo, work trips)
• “Pet-friendly boutique hotel”
• “Walkable to ___” (venues, convention centers, theaters, stadiums)
• “Direct booking vs OTA” comparisons
• “Unique hotel experience” intent (the stuff that makes this brand this brand)

2. Built property pages into “AI answer pages”

Meridian’s Website Insights revealed a classic hospitality gap: beautiful pages, but not enough high-signal, structured clarity.

So the team shipped:

• Amenities + policies written in “reference language” (the way AI quotes/cites)
• FAQ blocks built around traveler intent (parking, pet fees, check-in, cancellation)
• Neighborhood context (“what you’re near” + why it matters)
• Stronger internal linking from city hubs → properties → offers
• Schema/structured data improvements so assistants could reliably interpret each location
  
  

3. Won the citations AI trusts most

Meridian surfaced off-site credibility opportunities that disproportionately affect AI recommendations:

• authoritative city travel guides + “where to stay” roundups
• venue / event pages listing “recommended nearby hotels”
• local tourism and neighborhood resources
• brand-and-property profile pages that AI assistants commonly cite

4. Ran a weekly “Visibility Sprint”

Every week:

• identify the prompts where they should be recommended but weren’t
• ship the highest-leverage on-page fixes + content updates
• close citation gaps where competitors were getting referenced instead

The Outcome

By week twelve, Meridian tracking showed significant improvements:

• Direct booking revenue: +18% uplift vs baseline (attribution-based; driven by city + neighborhood intent)
• AI-driven demand: 4.1× increase in AI-influenced sessions landing on property pages and offer pages
• Shortlist dominance: the brand appeared in the final shortlist (top 5) for ~60% of tracked “best boutique hotel in ___” prompts
• Higher-quality traffic: +31% increase in “book-now-intent” sessions (property page → booking engine starts)‍
• Brand pull-through: +27% lift in branded search volume in markets where visibility moved fastest