Usually, when people first hear of predictive eye tracking, they ask about reliability. Is it trustable enough to make a business decision based on it?
To determine the accuracy of the Attention Vision Algorithm, MIT Researchers compared a result of the algorithm’s use with their data set encompassing images tainted with eye-tracking data. This comparison was achieved using an Area under ROC curve (AUC-Judd) metric.
The results were that attention insight is among the most accurate tools for predictive eye-tracking analysis. According to these researchers, attention insight doesn’t need to perform an actual eye-tracking study. Instead, just by checking attention patterns on screen-based impetuses like web pages and advertisements, they can execute a precise predictive analysis.
However, many companies offering visual heatmap services use only the somewhat modified versions of conventional prediction algorithms. On most occasions, these algorithms are not sophisticated enough to completely predict people’s gaze on websites, especially for the cheaper options.
Still, the premium versions of the algorithm that can offer expressive insights, particularly for digital content, exist.
For example, at Gaze Point, we capture actual website eye-tracking data to use in lab-based research where respondents wear and use eye-tracking devices. We also incorporate machine learning to understand the design factors that drive eye movements.
Predictive analysis technology can be an effective tool if you trust it. For example, by assessing the element visibility of your design before launching a website, you can take in massive conversion rates. Many companies are also using it to detect and make necessary improvements and create customer-oriented advertisements.