Experiment Design for GEO

Specify the intervention, target prompt group, platform, expected observable change, timeframe, and reason. 'Improve AI visibility' is not testable; 'publish a maintained integration matrix and observe whether integration prompts cite it more often' is closer to an actionable hypothesis.

Capture repeated pre-change observations and preserve prompts, answers, citations, dates, and settings. Limit simultaneous changes when practical, and annotate unavoidable confounders such as product launches, major press, competitor events, or platform updates.

Use comparison prompt groups or pages where appropriate, but do not imply laboratory control over a live answer ecosystem. Set the observation window based on crawl, publication, and market realities rather than a sales deadline.

Report effect direction, consistency, sample size, and alternative explanations. A changed answer after publication is an observation, not proof of causation. Replicate promising tests and look for supporting evidence in citations, logs, discovery traffic, or downstream behavior.

Q: Can GEO be A/B tested like a landing page? A: Sometimes limited comparisons are possible, but external systems and variable answers reduce experimental control.

Q: What should happen after a null result? A: Check implementation and measurement, preserve the learning, and revise or retire the hypothesis rather than rewriting the result.