What is the most useful way to interpret the schema data? | Trakkr Research

Interpret it as a benchmark profile rather than a magic lever. The schema data shows what cited pages tend to have in common, which helps teams design better page templates even when it does not prove strict causality.

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

Direct Answer

Mostly, interpret it as a benchmark profile rather than a magic lever. The schema data shows what cited pages tend to have in common, which helps teams design better page templates even when it does not prove strict causality.

What this means

This approach turns correlation data into an operating rule that teams can use to prioritize template updates, content refreshes, and measurement frameworks.

Evidence table

Metric Value Why it matters
Pages with schema 67.8% Share of cited pages with schema markup.
Person schema lift 9.4x Person schema over-indexes heavily on cited pages.
FAQPage lift 2.4x FAQPage is overrepresented relative to the web average.

Frequently Asked Questions

How common is schema markup on cited pages?

Pages with schema account for 67.8% of cited pages in the study.

Which specific schema types show the highest correlation with citations?

Person schema lift is 9.4x, heavily over-indexing on cited pages, while FAQPage lift is 2.4x relative to the web average.

What to do next

Related pages

Continue through the same study cluster.

Data & Sources