RealEyes

What Stevin gets out of RealEyes

RealEyes uses webcam AI to measure how viewers respond to video second by second: where the attention is, which moments trigger emotion, where they drop off. For video ads that is more valuable than for a still image, because with video the problem is often in the flow, the mid-clip drop-off you cannot see in a heatmap. It does need real viewers and therefore time, so it is not an instant check like a predictive heatmap. That is the trade-off right there: you get richer data, but use it where video production is expensive enough to be worth the effort, not for every single post. Stevin brings the RealEyes data into the creative feedback process, so for each video ad you see where viewers drop off and whether the message lands. Drop-off points and weak moments surface as concrete fixes before you spend media budget, and the choice of what to change stays with the person.

What you pair it with

RealEyes measures with real viewers via webcam, which means you need time and a test panel, unlike a predictive tool that is done in seconds. That makes it not a check for every post, but for video with enough budget behind it to be worth the effort. The data is rich but sits apart from your production process, so you need something that brings the findings back to the creative team. In practice: use RealEyes for your more expensive video ads before you spend media budget and let a layer on top surface the drop-off moments as concrete fixes, so the measurement actually lands in the work.

Common mistakes

• →Using RealEyes for every single post, when the turnaround only makes it worthwhile for more expensive video.
• →Not feeding the findings back to the creative team, so the measurement stays stuck in a report.
• →Only discovering drop-off points after launch because the pre-test gets skipped.