Dragonfly AI's solution analyses visual content predicting the areas and elements that are likely to attract attention at first glance. This provides you with objective and unbiased metrics that can be used to make data informed decisions.

When looking at your analysis it's recommended to have the project’s visual priority elements in mind.


Does the analysis predict that the priority elements are achieving high saliency scores and will attract attention?

  • Yes - The analysis can be confidently used to help validate the recommended content.
  • No - Check what other areas or elements are predicted to attract attention (potentially distracting viewers). If these are not aligned with the visual priority objectives, can any of the elements be changed or modified to rectify these (potential) issues.


With consumers in a hurry and dozens of visual brand messages competing for their time, catching the eye is crucial. Dragonfly AI replicates how the human eye actually works so you can make sure your marketing stands out from the crowd.

Dragonfly AI provides instant real-time analysis of the creative assets you are looking at, revealing ‘hot and cool’ spots. You can use it to analyse anything you can view on a tablet, from print and live digital assets, to packaging, retail space and out-of-home advertising.

Dragonfly uses a computational model to process the visual characteristics of what someone is looking at (orientation, scale, contrast, texture, luminance) to assign a stimulus attention score to every pixel, demonstrating its attractiveness. The resulting outputs are heat maps that display what grabs human attention in the first 2-3 seconds of interaction.

The following examples contain a heat map (App) as well as a heat map containing the saliency scores. The scores in red immediately show you the top five areas of highest saliency within that visual, allowing you to see which areas of the visual are performing best from a saliency point of view. The scores in green are the mean scores and these areas are most likely NOT going to be seen in the first five seconds.

Visual saliency


Provides an instant view of where customers’ eyes are most drawn on the image or content.

This can be used to improve UI/UX and draw customers’ eyes to key focus areas of your choosing


Accurately measure areas of attention quantitatively and how they compare against key focus areas relevant for you campaign.

An interactive layer enables the user to surface the saliency values of any areas across the image


Once we have our Dragonfly AI analysis we can start to understand:

  • High and low saliency regions
  • Potential clustering of visual elements
  • Saliency averages and range
  • Saliency values of priority elements
  • Visual priority


The scene average values are indicated by the green squares when you apply the hotspot analysis to any content. The scores represent the range for the mean average values.

In the below example we can see that the scene average is 54.

The relative average value range is an important metric to identify the information density of the image collectively. This can help determine if the image is too cluttered with too many competing elements.

If the average range is high, this means there is too much information in the image with many competing elements and has a low probability of individual elements to stand out and attract attention.

  • Any areas below 45 has a low probability of attracting attention.
  • Any areas above 54 has a higher probability of attracting attention.


Visual Priority is the relative probability of attracting viewers attention.

We can establish the visual priority of key elements we are interested in analysing by looking at the the saliency scores in relation to the each other across the image

Visual priority


Regions allow you to test how specific, high-value elements are performing within the scene (e.g. A call-to-action button on a webpage). Use Dragonfly’s region mode to identify your key regions of interest.

Local Attention Score (LAS)

Your local attention score will help you quantify how attention grabbing your region is. Later, in the experimentation phase, you’ll be able to use these scores to test which versions perform better from an attention perspective.

Regional analysis interpretation

Next, learn more about Attention.

Need more information? Please contact [email protected] or any member of the Dragonfly AI team.

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