Overall score
44.0Poor
—
Rank
#439
Prev: #0
saas avg
41.7
360 peers
Agent runs / week
1
Across 1 providers
- Locales
- en
Opportunity cost · SaaS
Charles Schwab could be leaving ~$3,808,000 in new ARR on the table every year
Stunt Double’s 2027 agent-traffic model projects 34% of trial, signup and plan selection sessions in the SaaS sector will be initiated or completed by AI agents[1][2]. Charles Schwab currently scores 44.0 on the Stunt Double Index[3], 6.1 points below the SaaS peer average, with a 56-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 recurring revenuebaseline for this sector — it is a directional estimate, not a measured conversion rate.
Gap to leader
56.0 pts
Below SaaS avg
6.1 pts
Modelled new ARR at risk
$3,808,000
Estimate assumes a $20M ARR baseline with agent-sourced expansion. Claim your domain to replace this placeholder with your reported new ARR.
Category breakdown
Brand awarenessCan agents recognise you exist?
10
DiscoveryWill they pick you?
60
Information retrievalCan they read your site?
45
Market rankingWhere do you sit in the lineup?
10
AccuracyDo they tell the truth about you?
45
Task completionCan an agent complete a task on behalf of a user?
45
Delegated accessDo you let agents in, safely?
100
Contact & communicationCan an agent reach a human?
45
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
Missing Open Graph tags. Link previews degrade in agent chats.
lowBrand awareness
Fewer than two social profiles linked. Agents struggle to triangulate the brand.
mediumBrand awareness
Homepage meta description is missing or too short for agent snippets.
lowDiscovery
No canonical URL. Agents may conflate duplicate pages as separate entities.
highInformation retrieval
Homepage ships little text to server-rendered HTML. Agents without a browser see an empty shell.
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".
lowAccuracy
No canonical URL. Agents may cite ephemeral query-string variants.
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.
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.
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.