Attention Insight

What Stevin gets out of Attention Insight

Attention Insight does what used to need an eye-tracking lab, but in seconds: upload a design and you get an AI-predicted heatmap of where attention goes. For iterative creative work that is the real win, because you can check ten variants in the time a real study takes just to set up. One thing to stay honest about: it is a prediction based on patterns, not a measurement of real users. That makes it strong for catching clear misses early, a CTA that disappears, a product image that gets buried, and weaker if you treat it as hard proof. Stevin uses Attention Insight to automatically check whether the CTA, the product, and the brand get the right attention in a new creative. Problems get flagged before the ad goes live, and you see that check before media budget goes behind it.

What you pair it with

Attention Insight gives you a predicted heatmap, fast and cheap, but it is a model trained on patterns, not a real user measurement, so treat it as a first filter and not as proof. It works on your designs, so the gain is in using it early and often while you iterate. On its own a heatmap is a snapshot with no validation afterward. In practice: use it to pull clear misses out of the visual hierarchy before an ad goes live and let real user data give the final confirmation, so you gain speed without confusing the prediction with reality.

Common mistakes

• →Taking the predicted heatmap as a hard measurement instead of a first filter.
• →Only applying it to the final version, when the gain is in fast iteration.
• →Not laying real campaign data next to it to confirm or disprove the prediction.