/ Index
Sign in
Claim this domain
Overall score
45.0Poor
—
Rank
#435
Prev: #304
travel avg
62.7
21 peers
Agent runs / week
2
Across 1 providers

Reserve now & choose from our collection of luxury hotel and resorts as you plan your next travel experience. Join our members Club for exclusive benefits.

Audience
Developer
Locales
enx-defaultjazh_cn
Opportunity cost · travel

The Ritz could be leaving ~$14,960,000 in bookings on the table every year

Stunt Double’s 2027 agent-traffic model projects 34% of itinerary search and booking sessions in the travel sector will be initiated or completed by AI agents[1][2]. The Ritz currently scores 45.0 on the Stunt Double Index[3], 5.2 points below the travel peer average, with a 55-point gap to the ideal agent experience (100). The loss figure below applies that gap to the projected agent-driven slice of a typical annual bookings volumebaseline for this sector — it is a directional estimate, not a measured conversion rate.

Gap to leader
55.0 pts
Below travel avg
5.2 pts
Modelled bookings at risk
$14,960,000

Estimate assumes an $80M annual bookings baseline. Claim your domain to replace this placeholder with your reported bookings.

Sources
  1. [1]Gartner (2025) — Intelligent Agents in AI Really Can Work Alone. Here’s How.
  2. [2]Adobe Analytics (2025) — Generative AI traffic to U.S. retail sites (weekly trend)
  3. [3]Stunt Double (2026) — Stunt Double Index methodology & scoring rubric

Category breakdown

Brand awarenessCan agents recognise you exist?
65
DiscoveryWill they pick you?
65
Information retrievalCan they read your site?
65
Market rankingWhere do you sit in the lineup?
15
AccuracyDo they tell the truth about you?
80
Task completionCan an agent complete a task on behalf of a user?
20
Delegated accessDo you let agents in, safely?
0
Contact & communicationCan an agent reach a human?
35

By agent provider

By agent provider
Session quality, 30-day rolling, N ≥ 10 per provider
Claude
Anthropic
0.0
ChatGPT Agent
OpenAI
100.0
Gemini
Google
0.0
Perplexity
Perplexity
Copilot
Microsoft
Browserbase Operator
Browserbase

Where agents get stuck

Public summary · full session replay available to verified owners
mediumBrand awareness
No Organization JSON-LD. Agents can’t tell who owns the site at a glance.
lowBrand awareness
Missing Open Graph tags. Link previews degrade in agent chats.
lowDiscovery
No llms.txt. You miss the emerging standard for giving agents a curated map of the site.
mediumInformation retrieval
No JSON-LD structured data. Agents must infer entities from markup.
mediumMarket ranking
No Product/Service schema on the homepage. Agents can’t easily compare offerings.
lowMarket ranking
No review or aggregateRating schema. Agents have no signal for ranking vs peers.
mediumMarket ranking
No price visible to non-JS clients. Agents may report "pricing not disclosed".
mediumAccuracy
No JSON-LD at all. Every factual claim must be inferred from prose.
mediumTask completion
No visible primary call-to-action in the server-rendered HTML. Agents have nothing concrete to click on behalf of a user.
highTask completion
No reachable entry point for delegated tasks — nothing at `/pricing`, `/signup`, `/cart`, `/book`, or similar responds without auth.
lowTask completion
No server-rendered form detected. Agents without JavaScript can’t submit anything.
mediumTask completion
No mention of delegated auth primitives (passkey, OAuth, MCP). Agents must drive a full browser session.
highDelegated access
No MCP manifest. Agents can’t auto-discover tools or delegated capabilities.
highDelegated access
No public API docs detected. Agents have no scoped way in; they must drive a browser.
lowDelegated access
robots.txt doesn’t mention any agent user-agents. The policy for agents is implicit.
mediumContact & communication
No contact email visible in server-rendered HTML. Agents can’t fall back to email.
lowContact & communication
No help, support, or FAQ hub linked. Agents can’t self-serve an answer before reaching out.