Structured data helps AI systems understand your content's meaning, not just its text. Schema markup provides explicit information about entities, relationships, and facts that AI can process efficiently—increasing the likelihood of accurate citations.
This article covers the most important schema types for GEO and how to implement them for maximum AI visibility.
Why Structured Data Matters for AI
AI systems face an interpretation challenge: understanding what content actually means rather than just reading words. Structured data solves this by providing machine-readable definitions of key information.
Example: The text "John Smith has 15 years of experience" is ambiguous to AI. Is John Smith a person? A company? What kind of experience? Schema markup explicitly defines: this is a Person with 15 yearsOfExperience in a specific field.
Well-structured data enables AI to extract precise information for citations, understand relationships between entities, match content to specific query types, and validate information across sources.
Essential Schema Types for GEO
Organization
Defines your business entity with name, address, contact info, social profiles, and founding details. Essential for brand recognition and local citations.
LocalBusiness
Extended organization schema for businesses with physical locations. Includes opening hours, service areas, payment methods, and geo-coordinates.
Person
Defines individuals—authors, founders, experts. Includes credentials, affiliations, and areas of expertise. Critical for E-E-A-T signals.
Product
For e-commerce: defines products with pricing, availability, reviews, and specifications. Essential for shopping-related AI queries.
Service
Defines services offered, including pricing, availability, and service areas. Important for service-based businesses.
FAQPage
Structures question-and-answer content. Directly maps to how users query AI, making extraction straightforward.
Article / BlogPosting
Defines content pieces with author, publication date, and topic. Helps AI understand content freshness and attribution.
Review / AggregateRating
Structures review data and ratings. AI uses this for trust signals and recommendation context.
Implementation Best Practices
1. Use JSON-LD Format
JSON-LD is the preferred format for schema markup. It's easier to implement, maintain, and doesn't interfere with your HTML structure.
2. Be Comprehensive
Include all relevant properties, not just required ones. More data gives AI more to work with. Add contact info, social profiles, founding date, area served, and other available properties.
3. Nest Related Schemas
Connect related entities through nesting. An Article should include its author (Person), publisher (Organization), and about topics. This creates context AI can follow.
4. Validate Your Markup
Use Google's Rich Results Test and Schema.org validator to ensure your markup is error-free. Invalid schema provides no benefit.
5. Keep Data Current
Outdated schema data creates trust issues. Update prices, availability, hours, and other time-sensitive information regularly.
Schema Patterns for Different Industries
Local Services
Focus on LocalBusiness, Service, and Review schemas. Include service area, hours, and aggregate ratings prominently.
E-commerce
Product schema is essential, with complete specifications, pricing, and availability. Add Offer schema for promotions.
Professional Services
Emphasize Person schemas for practitioners with credentials. Add Service and FAQPage schemas for common questions.
Content Publishers
Article and BlogPosting schemas with full author information. Include Organization as publisher.
Measuring Schema Impact
Track these indicators to measure structured data effectiveness:
- Rich result appearances in Google Search Console
- AI visibility scores before and after implementation
- Citation accuracy—whether AI extracts correct information
- Entity recognition in AI responses
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