Prediction Analysis and Eye-Tracking Software

By definition, predictive eye-tracking refers to an AI-based option for eye-tracking analysis. It depends on data pre-collected using various eye-tracking software to simulate human visual activity on screens.

Like AI-generated heatmaps, predictive eye-tracking devices give the areas where people are most likely to look in a website.

Its primary focus is on feigning the initial three to five seconds of the possible areas people will look at when they first interact with the website. This way, it’s possible to determine your web page’s visual hierarchy and if people notice crucial bits of information on their first impression on the page before clicking on anything.

Note that results from a predictive heatmap aren’t similar to click or mouse movement heatmaps, so predictive analysis complements rather than replacing other tools.

Reliability of Eye-Tracker Software for Prediction

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.

Role of Eye-Tracking Devices in Prediction Analysis

You might wonder about the underlying principles that predictive eye-tracking technology uses. As mentioned, it uses an AI algorithm that studies pre-collected data to advance itself. To develop a complex deep learning algorithm, we must train it on thousands of images from real eye-tracking studies.

Amid the analysis of these images, the algorithm recognizes and systemizes the patterns it learns from the user attention flow. As a result, the algorithm can precisely predict what might attract the most interest in the webpage or advertisement.

The more data an algorithm analyzes, the smarter it gets in predicting and directing people’s gazes — currently, the visual saliency of academic data accuracy stands at 95%. Most commercial predictive algorithms have somewhere around 90% accuracy.

Who Should Use Predictive Analysis?

The flexibility of predictive eye-tracking allows the technology to be used in many ways.

Its use is most prevalent among marketers, web designers, and optimization strategists to maximize traffic on their most vital content.

Predictive eye-tracking is helpful, from the preliminary analysis of live websites to comparing potential new designs in the mock-up phases. It also tends to be used mainly by UX specialists and agencies with fortes in helping clients expand their websites.

Note that predictive analysis is significantly different from other analytic tools because it is not a passive background-running tool. Mainly, it’s used by active people who are constantly working to enhance their designs.