How Yamaguchi Measures GEO Performance: From Brand Mentions to Lead Quality

When Yamaguchi evaluates GEO, the first question is usually not “Did traffic go up?” It is “Was the brand correctly adopted on important questions?” That distinction matters because AI-assisted search shifts part of the decision process into the pre-click stage.

Pew’s July 2025 browsing-data study analyzed 68,879 Google searches and found that about 18% of them displayed an AI summary. On pages with an AI summary, users clicked traditional search results only 8% of the time, compared with 15% on pages without one. Clicks on sources cited inside the AI summary occurred just 1% of the time. So if a team tracks only site clicks, it may miss a meaningful share of answer-layer influence.

Pew’s October 2025 survey adds a trust dimension. While 65% of U.S. adults say they at least sometimes see AI summaries in search results, only 6% say they trust them a lot. A much larger share reports partial trust, while 46% have little or no trust in the information. That means visibility alone is not enough. Brands also need to know whether they are being represented clearly and credibly.

Google’s own documentation gives teams a practical starting point. AI-feature appearances are included in the overall Search Console Performance report under Web search, and Google recommends combining Search Console with Analytics to understand conversion and engagement quality. In other words, Google-side measurement can already start with two linked views: search visibility by page and query, and post-click quality by engagement or conversion behavior.

Microsoft is making this picture more explicit. Bing Webmaster Tools’ AI Performance public preview includes metrics such as total citations, average cited pages, grounding queries, page-level citation activity, and visibility trends over time. That is important because it shows citation-level visibility is moving from a third-party concept toward a first-party reporting model.

Based on those shifts, Yamaguchi recommends four measurement layers. The first is technical readiness: indexability, snippet eligibility, page freshness, and structural integrity. The second is answer-layer presence: where the brand appears, on which questions, and on which pages. The third is narrative quality: whether the brand is summarized correctly, oversimplified, misclassified, or stripped of its boundaries. The fourth is business impact: lead fit, sales-friction reduction, and whether inbound conversations become easier and more aligned.

For brands that also need domestic platform coverage, one more operational step matters. DeepSeek already spans web, app, open platform, and API access, and Qwen already presents an official app surface plus a broader model and agent ecosystem. That means many teams are no longer dealing with one answer environment but several. In practice, this requires a fixed question set and regular manual answer sampling on DeepSeek, Qwen, and other priority domestic platforms, not only Search Console and Bing Webmaster dashboards.

Public industry research suggests teams should not monitor only informational content. Semrush’s 2025 study indicates that commercial, transactional, and navigational queries increasingly trigger AI Overviews as well. So measurement should extend beyond educational articles into service pages, brand terms, comparison queries, and pre-purchase questions.