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Leaderboard›ecommerce

Movie Tickets Movie Times

fandango.com
Claim this domain
Overall score
57.0Poor
▲ 2.2
Rank
#327
Prev: #363
ecommerce avg
61.6
118 peers
Agent runs / week
2
Across 0 providers

Buy movie tickets in advance, find movie times, watch trailers, read movie reviews, and more at Fandango.

Audience
Consumer
Locales
en
Opportunity cost · e-commerce

Movie Tickets Movie Times could be leaving ~$7,310,000 in revenue on the table every year

Stunt Double’s 2027 agent-traffic model projects 34% of product discovery and checkout sessions in the e-commerce sector will be initiated or completed by AI agents[1][2]. Movie Tickets Movie Times currently scores 57.0 on the Stunt Double Index[3], with a 43-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 e-commerce revenuebaseline for this sector — it is a directional estimate, not a measured conversion rate.

Gap to leader
43.0 pts
Above e-commerce avg
0.0 pts
Modelled revenue at risk
$7,310,000

Estimate assumes a $50M annual e-commerce revenue baseline. Claim your domain to replace this placeholder with your reported revenue.

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?
80
DiscoveryWill they pick you?
65
▲ 15.0
Information retrievalCan they read your site?
85
Market rankingWhere do you sit in the lineup?
35
AccuracyDo they tell the truth about you?
80
Task completionCan an agent complete a task on behalf of a user?
55
Delegated accessDo you let agents in, safely?
0
Contact & communicationCan an agent reach a human?
20

By agent provider

By agent provider
Session quality, 30-day rolling, N ≥ 10 per provider
Claude
Anthropic
0.0
ChatGPT Agent
OpenAI
0.0
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.
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.
mediumAccuracy
No JSON-LD at all. Every factual claim must be inferred from prose.
highTask completion
No reachable entry point for delegated tasks — nothing at `/pricing`, `/signup`, `/cart`, `/book`, or similar responds without auth.
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.
highContact & communication
No contact page linked or reachable. Agents acting on a user’s behalf have nowhere to send a message.
mediumContact & communication
No contact email visible in server-rendered HTML. Agents can’t fall back to email.