Citation Experiment
Citation Experiment — A controlled test that measures how a specific content change affects AI citation rates. The workflow is: capture baseline citations → make a content change → re-measure citations → compare results. Citation experiments provide data-driven evidence of what AEO/GEO optimizations actually work.
Citation experiments are the AEO equivalent of A/B tests. The process is: capture your current citation rates across tracked prompts (baseline), make a specific content change (e.g., add JSON-LD structured data, rewrite headings as questions, increase evidence density), wait for AI engines to re-index, then re-measure citation rates and compare against the baseline.
This data-driven approach eliminates guesswork. Instead of hoping that a content change improves your AI visibility, you measure the actual impact. CiteRank's experiment feature automates this workflow: set a baseline, make your change, trigger a re-measurement, and get a statistical comparison showing whether your citation rates improved, declined, or stayed the same.