Eric Van Buskirk

Eric Van Buskirk is research director at Clickstream Solutions. With Kevin Indig, he co-led what they describe as the first independent UX study of Google's AI Overviews.

The method relied on think-aloud recordings: people spoke while they read AI answers, revealing confusion, trust moments and scanning patterns that aggregate click data cannot show. That filled a gap for marketers who only had impression and CTR charts.

Think-aloud work is slower and smaller than log analysis, which is exactly why it helps. It surfaces the human moments that dashboards flatten.

Clickstream Solutions' vantage point in behavioral data makes Van Buskirk a natural partner for studies that need both session context and qualitative method. The AI Overviews project sits at that intersection, which is why it filled a gap pure click reports could not.

AI Overviews changed the search results page enough that old mental models of the ten blue links stopped being enough. Van Buskirk's contribution was behavioral evidence about how humans actually process the new module.

Paired with Indig's distribution through Growth Memo, the study reached practitioners quickly and became part of how teams brief AI overview projects. Designers and SEOs could argue from observed behavior instead of from hunches about where eyes go.

His architect seat on the TGA 20 is earned by method: independent UX evidence on a surface that was reshaping discovery while most measurement still looked only at clicks.

For product and content teams, the think-aloud clips are often more persuasive than another CTR table. Hearing a user hesitate on an AI overview changes how people design the page around it.

Architects earn their place by changing the evidence toolkit. Van Buskirk did that by putting think-aloud UX beside clickstream habits for AI Overviews.

Begin with the AI Overviews UX study published with Kevin Indig. Watch for the think-aloud method as much as the headline findings; the method is the reason the work stands out next to purely quantitative AI overview reports.

Independent UX evidence also gives brands language for executive caution. Instead of guessing whether AI Overviews help or hurt, teams can point to observed reading behavior and design tests around it.