Dragonfly AI

What Stevin gets out of Dragonfly AI

Dragonfly AI has a wider reach than most attention tools: it predicts where the eye goes on everything, from a digital ad to packaging or a shop window. For FMCG and retail, where the visual impact does not stop at the screen, that is a real difference. The nuance is that it predicts saliency, not sales. A heatmap shows where attention lands, but whether that attention leads to a purchase is a different question. Teams get stuck there: attention gets confused with effect. Stevin uses Dragonfly where physical and digital meet and links the saliency results via API to what actually happens afterward in your channels and your sales data, so a design choice does not just stand out but also delivers. Every recommendation with impact goes past a person first.

What you pair it with

Dragonfly gives you a uniform attention metric across digital and physical, which is rare, but it measures attention and not revenue. Count on a link with your sales and campaign data to give the heatmaps meaning, and on someone who weighs whether the attention also leads to a purchase, otherwise you optimize for standing out instead of for results. The analysis is cross-channel, so the gain is in connecting that one metric with what actually happens after the attention. Without that bridge it stays a pretty map with no outcome.

Common mistakes

• →Confusing attention with effect: a heatmap says nothing about a purchase.
• →Optimizing for standing out instead of for results.
• →Not linking the saliency scores to your sales or campaign data.
• →Using the tool where the visual impact reaches no further than the screen.