Overall score
48.0Poor
▼ 8.8
Rank
#416
Prev: #330
ecommerce avg
61.6
118 peers
Agent runs / week
3
Across 0 providers
In today's image, the granite arches of the Alabam
- Locales
- en
Opportunity cost · e-commerce
Rock on, Milky Way! could be leaving ~$8,840,000 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]. Rock on, Milky Way! currently scores 48.0 on the Stunt Double Index[3], 2.1 points below the e-commerce peer average, with a 52-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
52.0 pts
Below e-commerce avg
2.1 pts
Modelled revenue at risk
$8,840,000
Estimate assumes a $50M annual e-commerce revenue baseline. Claim your domain to replace this placeholder with your reported revenue.
Category breakdown
Brand awarenessCan agents recognise you exist?
55
DiscoveryWill they pick you?
65
Information retrievalCan they read your site?
75
Market rankingWhere do you sit in the lineup?
15
AccuracyDo they tell the truth about you?
70
Task completionCan an agent complete a task on behalf of a user?
45
Delegated accessDo you let agents in, safely?
12
▼ 88.0
Contact & communicationCan an agent reach a human?
15
By agent provider
By agent provider
Session quality, 30-day rolling, N ≥ 10 per provider
Claude
Anthropic
ChatGPT Agent
OpenAI
Gemini
Google
Perplexity
Perplexity
Copilot
Microsoft
Browserbase Operator
Browserbase
Where agents get stuck
Public summary · full session replay available to verified ownersmediumBrand awareness
No Organization JSON-LD. Agents can’t tell who owns the site at a glance.
lowBrand awareness
Fewer than two social profiles linked. Agents struggle to triangulate the brand.
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.
lowAccuracy
No privacy policy linked from the homepage. Agents can’t cite policy when asked.
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.
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.