How should agencies benchmark ChatGPT, Perplexity, Copilot, and AI Overviews without pretending the systems behave the same?

Use a shared evaluation framework, not a shared ranking assumption.

Create a stable query set grouped by intent: category discovery, “best”/comparison, how-to, problem diagnosis, local intent, brand facts, product facts, and expert/topic association. Keep the exact prompts versioned so month-over-month comparisons are meaningful.

For each platform/run, record:
- brand mentioned or not;
- position/order when the interface meaningfully presents one;
- factual accuracy;
- sentiment/description category if useful;
- cited/source URLs;
- competitor mentions;
- whether the answer was retrieval-backed;
- date, model/product version where visible, and location/account context when relevant.

Repeat important prompts multiple times because generative outputs vary. Report mention/citation frequency across the sample rather than treating one run as deterministic.

Do not combine every platform into a fake universal “rank.” You can create normalized indicators such as share of tested prompts with accurate brand inclusion or share with first-party/earned citations, but keep platform-level results visible.

Use source overlap analysis to find domains repeatedly cited across systems. That can reveal evidence gaps or authority opportunities.

Finally, tie visibility to outcomes when possible: AI referral traffic, branded search lift, leads, assisted conversions, or user research. GEO reporting should help decide what evidence/content to improve—not manufacture precision the platforms do not provide.