Your ChatGPT Referrals Grew 5×. How Much Came From AEO?
A 2026 natural experiment separates raw ChatGPT referral growth from the platform tailwind with an on-domain control.
Research desk
Tests, protocols, observations, and evidence reviews for questions that cannot be answered responsibly with opinion alone.
From this desk
The featured page offers a strong introduction. The remaining cards help readers move through this editorial section without turning the archive into a service menu.
Read the featured method
Use a preregistered protocol to compare accessibility trees, HTML, rendered pages, and screenshots while keeping tasks and scoring consistent.
Latest research
Each article states the method, retained evidence, unresolved limits, and the boundary on what the result can establish.
A 2026 natural experiment separates raw ChatGPT referral growth from the platform tailwind with an on-domain control.
A preregistered local test for comparing matched weekdays with and without Business Profile posts while preserving calls, clicks, directions and confounders. Results pending.
A preregistered test separating mentions, citations, recommendations, and referrals across bounded SaaS recommendation tasks. Results pending.
A consent-based experiment for testing whether a crawlable first-party review page is retrieved or reflected in repeated AI answers. Results pending.
A preregistered 25-page protocol for comparing transfer size, raw HTML, server-extractable text, rendered text, composition, and main-content density. Results pending.
A preregistered protocol for comparing accessibility-tree, static HTML, rendered HTML, and screenshot representations on identical browser-agent tasks.
A preregistered test for DeepSeek V4 Flash through Perplexity model-gateway and agent routes, with identical inputs and separate retrieval, citation, latency, and quality fields.
A preregistered protocol for comparing GPT-5.6 Fast and standard service above 272K input tokens across latency, cost, errors, and task quality.
A preregistered protocol for comparing AI-answer video citations with Google’s top 100 web results without turning a vendor claim into a universal search rule.
A useful standard
A useful study makes its conditions visible. Treat each result as evidence within a declared sample and environment, not as a universal platform rule.
The question, inputs, environment, measures, and stopping rule should be understandable before the result is interpreted.
Timeouts, absent results, rejected cases, and contradictory observations are data, not material to hide.
A measured association or one platform observation does not automatically establish causation or a permanent ranking rule.
References and next steps
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