AEO Checklist for Crypto investing Content

Implement 'Direct Answer' H2/H3 Structures for Crypto Queries

Structure content modules to answer core crypto questions (e.g., 'What is Bitcoin Halving?') in the first paragraph. Use a 'Question -> Concise Answer (30-50 words) -> Technical/Market Context' hierarchy to satisfy LLM extraction for AI search.

Optimize for 'Featured Snippet' Extraction on Token Metrics

Align content with extraction patterns: use 40-60 word definitions for tokenomics and 5-8 item bulleted lists for investment strategies. Answer engines prioritize these patterns for 'verified' crypto insights.

Leverage 'Schema.org' Speakable Property for Market Updates

Define the 'speakable' property in JSON-LD for real-time price alerts or breaking news. This aids voice-based answer engines (e.g., Gemini Live) in providing instant audio updates on market movements.

Implement 'FAQPage' Structured Data for Crypto Concepts

Map your FAQ modules on topics like 'DeFi Explained' or 'NFT Utility' to FAQPage JSON-LD. This forces Answer Engines to associate specific question-answer pairs directly with your brand entity.

Optimize for 'Fragment Loading' Performance for On-Chain Data

Ensure fast delivery of specific HTML fragments containing live price feeds or transaction data. AI retrievers (RAG) prioritize sites that can index partial data without full client-side rendering delays.

Deploy 'Machine-Readable' Data Tables for Comparative Analysis

Use standard HTML `<table>` tags for comparing token performance, exchange fees, or DeFi yields. LLMs extract data from tabular structures more accurately than from complex CSS or JavaScript visualizations.

Establish Strong 'Crypto Authority' (E-E-A-T)

Ensure 'About Us' and 'Author' profiles are clearly defined with Schema.org Person/Organization. Link via 'sameAs' to verifiable crypto profiles (e.g., Twitter, LinkedIn, CoinMarketCap profiles) for AI cross-referencing.

Verify 'Entity Linkage' in Crypto Knowledge Bases

Ensure your crypto brand/project is accurately represented in Wikidata, CoinMarketCap, CoinGecko, and relevant blockchain explorers. LLMs use these authoritative nodes to 'triangulate' and confirm entity validity.

Use 'Natural Language' Semantic Triplets for Tokenomics

Format critical tokenomics data as 'Subject-Predicate-Object' triplets. E.g., '[Token Name] has a [Max Supply] of [Number]'. This simplifies entity-relationship extraction for LLM knowledge graphs analyzing crypto projects.

Eliminate 'Hype' and Subjective Adjectives in Analysis

Strip out speculative language like 'moonshot' or 'guaranteed gains'. Answer engines prioritize objective, data-backed analysis (e.g., on-chain metrics, market cap trends) over subjective 'to the moon' rhetoric.

Optimize for 'People Also Ask' (PAA) Hooks on Altcoins

Identify related 'Edge Queries' in PAA boxes concerning specific altcoins or DeFi protocols. Create dedicated, semantically-linked sections that answer these peripheral intents within your primary resource page.

Monitor 'Attribution' in Generative Snapshots for Market Reports

Track citation frequency in Google SGE (AI Overviews) and Perplexity for market analysis. Use 'Share of Answer' as a primary KPI to measure your brand's authority in generative crypto insights.