etrade.com — AI Search Visibility Report
Overall score: 79/100
AI search visibility analysis for etrade.com. LLMao scored etrade.com 79/100 across 8 LLM-readiness categories including crawlability, semantic content, structured data, authority signals, and answer-engine clarity.
Analyzed URL
Category breakdown
- readability: 85/100 — Content is clear and professional, though some financial jargon is used without immediate definition.
- schema_markup: 72/100 — Valid JSON-LD present with Organization and WebSite types, but missing deeper content-level schemas like FAQ or Article.
- authority_trust: 75/100 — Strong institutional trust via Morgan Stanley association, but lacks individual author credentials for research content.
- citation_sources: 70/100 — Claims are backed by footnotes and regulatory disclosures, though external outbound links to primary data sources are limited.
- content_freshness: 65/100 — Promotional dates are current (2026), but specific publication dates for research content are not visible in the scrape.
- content_structure: 90/100 — Excellent use of semantic HTML and clear heading hierarchy. Navigation is well-organized for both users and crawlers.
- entity_definition: 80/100 — Strong brand consistency and clear 'About' context, though lacks specific term definitions on the homepage.
- technical_accessibility: 88/100 — Good meta descriptions and social tags, though heavy reliance on JS for some interactive elements.
Top recommendations
- Expand Content-Specific Schema (Schema.org Markup): Implement FAQPage schema for the FAQ section and Article/BlogPosting schema for the 'Expert research and insights' section to improve rich snippet visibility in LLM responses.
- Implement Author E-E-A-T Signals (Authority & Trust Signals): Add specific author bylines and Person schema to the 'Expert research' and 'Market news' sections to satisfy E-E-A-T requirements for financial content (YMYL).
- Expose Content Modification Dates (Content Freshness): Add visible 'Last Updated' or 'Published' dates to the research and insight cards. LLMs prioritize recent financial data.
- Define Key Financial Entities (Entity Definition): Create a dedicated 'Glossary' or 'Definitions' section with clear term definitions to help LLMs map financial entities and concepts.
- Optimize AI Crawler Access (Technical Accessibility): Ensure the robots.txt explicitly allows AI crawlers like GPTBot and ClaudeBot to ensure full indexing of research libraries.