AI Answer Engine Citation Behavior An Empirical Analysis of the GEO16 Framework
This work addresses the problem of citation quality in AI answer engines for publishers, providing a practical auditing framework, but it is incremental as it builds on existing quality metrics.
The study introduced the GEO-16 framework to audit AI answer engines' citation quality, finding that overall page quality strongly predicts citation, with specific thresholds like a GEO score of at least 0.70 and 12 pillar hits leading to substantially higher citation rates in a dataset of 1,702 citations across three engines.
AI answer engines increasingly mediate access to domain knowledge by generating responses and citing web sources. We introduce GEO-16, a 16 pillar auditing framework that converts on page quality signals into banded pillar scores and a normalized GEO score G that ranges from 0 to 1. Using 70 product intent prompts, we collected 1,702 citations across three engines (Brave Summary, Google AI Overviews, and Perplexity) and audited 1,100 unique URLs. In our corpus, the engines differed in the GEO quality of the pages they cited, and pillars related to Metadata and Freshness, Semantic HTML, and Structured Data showed the strongest associations with citation. Logistic models with domain clustered standard errors indicate that overall page quality is a strong predictor of citation, and simple operating points (for example, G at least 0.70 combined with at least 12 pillar hits) align with substantially higher citation rates in our data. We report per engine contrasts, vertical effects, threshold analysis, and diagnostics, then translate findings into a practical playbook for publishers. The study is observational and focuses on English language B2B SaaS pages; we discuss limitations, threats to validity, and reproducibility considerations.