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Leaderboard›ecommerce

Thinkmill

thinkmill.com.au
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Overall score
53.7Poor
—
Rank
#374
Prev: #0
ecommerce avg
61.6
118 peers
Agent runs / week
1
Across 0 providers

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Audience
Developer
Markets
AU
Opportunity cost · e-commerce

Thinkmill could be leaving ~$7,871,000 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]. Thinkmill currently scores 53.7 on the Stunt Double Index[3], with a 46-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
46.3 pts
Above e-commerce avg
0.0 pts
Modelled revenue at risk
$7,871,000

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?
65
DiscoveryWill they pick you?
80
Information retrievalCan they read your site?
85
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
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
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.
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.
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.