Emphasis on the center
In addition, the heatmaps created by AI models often overemphasize the center if the model does not observe other elements to base its prediction on. In the following images, we also see a small bias regarding the brand name.
heatmap bias on the center
In the second step, we performed
A/B test using AI models for eye tracking. These campaigns were also tested in latest database products by the companies themselves. For example, by publishing two versions of a campaign during the same period on social media. The goal was to compare the results and see how AI models make predictions in an A/B test.
This part of the research shows that the predictions of predictive eye tracking tools are in line with the success of campaigns in the real world. AI models can provide valuable information without the need to perform an A/B test in the real world. This would save time and costs.
However, there are limitations when using AI models for A/B testing. Current predictive eye tracking antarctica business directory visual elements. They cannot read text, so they cannot provide additional information about the difference between two versions of an email. This can also be the case with posters that are less visual and contain more text. Or if the visuals do not show a big difference. In such cases, you can ask yourself whether it is currently worth investing in an unobvious but effective channel of attraction eye tracking.
Or is it better to evaluate your campaign based on traditional marketing theories?
While AI is a scalable option and can provide valuable information, it’s important to explore its biases and limitations. Biases in using AI for eye tracking include its tendency to predict more attention for faces, brand names, and the middle of videos. This should be considered in your analysis. Additionally, AI can be more efficient for testing visual campaigns or content rather than textual ones.