Benchmark profile of AI-cited pages | Trakkr Research

Benchmark profile of AI-cited pages from the study The Anatomy of an AI Citation.

Methodology: Built from 1,465 AI-cited pages across 950 domains, using 28,033 citation opportunities and page-level crawl analysis.

Summary

The benchmark pattern indicates that AI systems preferentially cite pages demonstrating high structural integrity and content depth. Specifically, cited pages exhibit near universal canonicalization at 91.4 percent and strong social metadata presence at 89.2 percent. Content depth is a defining characteristic, with 78.4 percent of pages exceeding 1,000 words and an average word count of 2,289.6 words. Furthermore, 67.8 percent of these pages utilize schema markup, reinforcing the trend that structured, metadata-complete, and long-form documents dominate AI citations.

Benchmark rows

Metric Value Context
Pages with schema 67.8% Share of cited pages with schema markup.
Average word count 2,289.6 Average word count of cited pages.
Pages above 1,000 words 78.4% Most cited pages are long-form.
Canonical tag rate 91.4% Share of cited pages with a canonical tag.
OG tag rate 89.2% Share of cited pages with Open Graph tags.

Ranked view

Item Value Detail
Pages with canonical tags 91.4% Canonicalization is nearly universal across cited pages.
Pages with Open Graph tags 89.2% Social metadata is also highly prevalent.
Pages with schema 67.8% Structured data is common in the cited-page profile.
Pages above 1,000 words 78.4% Long-form depth is the norm.
Average word count 2,289.6 Typical cited pages are dense and fully built out.

Related pages

Continue through the same study cluster.

Data & Sources