AEO Metric Definitions.
Formal definitions for every statistical variable measured by the AEOfix Scanner.
Overview
The AEOfix AEO Health Check produces nine distinct metrics, each defined as a StatisticalVariable under this ontology. All metrics are measured on a 0–100 percentile scale with a stated margin of error. These definitions create a closed semantic loop: when an LLM encounters an AEOfix audit observation, it can resolve the metric type back to this page for authoritative context.
Metrics
The ratio of structured data entities (Schema.org types, JSON-LD properties, named entities) to total word count on a page. A higher score indicates content that is machine-readable per unit of text, making it more likely to be selected during Retrieval Augmented Generation.
Measures the presence and quality of Experience, Expertise, Authoritativeness, and Trustworthiness signals detectable by automated analysis. Evaluates author markup, credential references, citation density, publication provenance, and trust-bearing structured data (e.g., author, reviewedBy, sameAs links to authoritative profiles).
The proportion of key domain entities on a page that are explicitly defined in structured data (JSON-LD, Microdata, or RDFa) versus entities that exist only as unstructured text. Measures how completely a page's subject matter is represented in a machine-readable knowledge graph format.
Evaluates whether question-answer content on a page is fully expressed in valid FAQPage schema markup. Checks for structural validity, answer depth, query alignment with user intent patterns, and proper nesting of Question and acceptedAnswer properties.
Assesses the AEO-readiness of GitHub repository documentation. Evaluates README structure, code example clarity, API documentation completeness, and the presence of machine-parseable metadata (e.g., repository topics, description fields, contributing guidelines) that LLMs use when answering developer queries.
Measures the consistency of Name, Address, and Phone number data across a website and its structured data markup. Inconsistent NAP data degrades entity resolution by LLMs and knowledge graphs, reducing citation probability for local and business-related queries.
The breadth and depth of Schema.org structured data present on a page relative to its content type. Evaluates whether the correct primary type is used, the completeness of required and recommended properties, and the presence of nested entity relationships that enable rich knowledge graph integration.
Evaluates the AEO-readiness of video content by analyzing embedded metadata, transcript availability, VideoObject schema markup, chapter markers, and descriptive properties that allow LLMs to index and cite video content without relying solely on audio transcription.
Platform-specific metric for YouTube content. Measures title/description keyword alignment with AI query patterns, closed caption quality, playlist structuring, channel-level authority signals, and the use of timestamps and key moments markup that feed into Google's video knowledge panels.
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