Eye Tracking in User Research

Everything you need to know about eye tracking, how this tool helps you learn about your users and why it’s easier than ever to try out

23 August 2022
  • AB testing
  • E-Commerce
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Most commercial eye trackers use dedicated software and an infrared camera to capture a user’s gaze

UX Research Team di Conflux

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23 August 2022
  • AB testing
  • E-Commerce
  • +3

The present and future for eye tracking

Eye tracking is a tool that UX researchers have widely adopted in recent years. Driving the use of this technology are new manufacturers and the meteoric rise of the UX field. Why the sudden interest? Because eye tracking studies deliver insights on the way a user sees and explores your web page, app or platform.

What are eye trackers and how do they work?

As the name suggests, eye trackers let us record where a person’s visual attention is focused with respect to a stimulus on a screen (e.g., a website, image, video) or in the real world. What’s more, eye tracking technology makes it possible to pinpoint the location and duration of this observation. To do this, most commercial eye trackers use dedicated software and an infrared camera to capture a user’s gaze at different sampling rates, typically from 60 Hz to 2000 Hz.


Eye tracking devices tend to be stationary and placed at the bottom of a PC screen (as seen below), but there are others designed for eye tracking experiments in the field, such as the wearable Tobii Pro Glasses.


Image 1: Stationary eye tracker setup (

But how does an eye tracker work? The key is in the infrared camera, generating a beam that strikes your pupil, creating a corneal reflection. Measuring the vector between the reflection and the pupil gives the eye’s position (Hansen and Ji, 2010). The software (e.g., Tobii Pro Studio) then matches up the eye position to the stimulus presented on the screen or in the real world.


Image 2: Corneal reflection (Blignaut, 2014)

Data from the eye tracker are obtained via two types of eye movements: fixations, where the eye remains “relatively still” for a period of time (between 60 ms and several seconds) on part of the stimulus, and saccades, which are fast eye movements between fixations and last 30-80 ms. Based on these eye movements, several metrics have been established to provide insight into user perception, including:


  • Fixation durations: length of fixation on certain areas of the stimulus (Areas of Interest – AOIs), which can be extracted in terms of average, number, and total
  • Time to the first fixation: average time before a subject sees a given area
  • Percentage fixated: percentage of subjects who saw an area or percentage of fixations on the various areas based on the total of participants


Using these metrics, we can visualize the data to make the often complex results more digestible. Prime examples are heat maps like the ones you might generate from web site analytics or sequence-focused visualizations such as gaze plots (or scan paths) that show where, when and how long the user’s attention was focused.


Image 3: Example of gazeplot (Eraslan et al., 2018)

Eye tracking to reinforce usability tests

Analysis of these metrics generates a bounty of interesting insights. With eye tracking studies we can capture the user’s visual experience in a direct and accurate way since we’re simply measuring the attention paid to a stimulus and its sections (Partridge and Nielsen 2009, Holmqvist et al. 2011, Boyko 2013).


This means that through eye tracking research we can actually study unconscious user behavior that can’t be evidenced by any other testing methods. With this information-rich data in hand we can vastly improve the user experience in some novel ways.


Specifically, Bergstrom and Schall (2014) indicate that eye tracking provides a better understanding of:


  • Distribution of attention to a stimulus: the percentage of attention given to salient areas (Areas of Interest) or less salient areas (Non-Areas of Interest)
  • Time spent viewing items: the percentage of time spent observing specific items (e.g. buttons)
  • Perceptual flow: sequences and general visual behavior of the user

By analyzing eye movements we can understand many aspects of test participants’ observations:


  • What they saw or ignored while looking at an area
  • How long they looked at or ignored an area
  • How many observations there were for the same area
  • Which area caught their attention before others
  • How much time passed before an area was seen
  • In what order certain areas were explored


For example, a pharmacy e-commerce might be interested in knowing whether the user reads the dosage or product information before buying it or if they ignore it completely. This information could be extremely relevant for clients who want to redesign a website with more salient text. For a fashion e-commerce, the goal might be to find out how users look at photos of clothing; which photo attracts the most attention (e.g. pictures at different angles), where a user’s gaze lingers while scrolling through the page, and if certain elements during checkout are ignored and create any errors.

Example of eye tracking tasks on a fashion e-commerce


Platforms stand to benefit from understanding if navigation problems are present due to buttons that confuse users and cause them to take extra steps between sections. On a site with lots of images or bold graphics, it might be useful to investigate what is being looked at, how long the content is viewed and whether certain elements distract or hide the navigation path from the user. In general, and regardless of the type of site, comparing designs in an A/B test makes for some indispensable insights. Take the image below as an example: here we see a very different observation pattern when you swap around the images and text.


Eye tracking in concert with other methodologies

While the potential for eye tracking research is clear, we should point out that it is always good to use it within a more comprehensive experimental design, with a focus on data triangulation. In UX, eye tracking is often associated with “Retrospective Thinking Aloud” where the participant is shown a video of what they saw (including a dynamic visualization with gaze plots) while being asked to describe their experience. It’s better to avoid “Concurrent Thinking Aloud” methods because they can alter the user’s visual behavior and create cognitive load.


Other strategies to ensure optimal data collection involve moderated tests, interviews, or focus groups to pair qualitative data with the eye tracker’s quantitative data.

New frontiers: semi-remote and remote eye tracking

In the last two or three years, software and applications for so-called remote or semi-remote eye tracking have also seen a lot of growth. In the case of semi-remote studies, some tools make use of a 3D camera, or a very high-quality camera attached to the PC. For fully-remote eye tracking research, a high-quality video camera or even a PC webcam can do the trick. At Conflux, we’ve actually had the opportunity to test a few examples of these fully-remote applications and the results are encouraging, with data accuracy on par with physical eye trackers.


One of the most appealing aspects of remote eye trackers is the ability to perform a higher number of tests within a given timeframe. With an in-lab setup and fixed eye tracker, equipment needs calibration and checks, which can be time-consuming, costly, and limits the sample of participants. Through careful design, any data accuracy issues can also be overcome.

If you need some backup creating your digital product or service and delivering an amazing user experience, or would like a consultation to breathe new life into a digital touchpoint, we at Conflux are here to help. When it comes to apps, websites, software and much more, our experienced and knowledgeable UX experts will guide you and your business towards creating a product best-suited to your needs.

– Bergstrom, J. R., & Schall, A. (Eds.). (2014). Eye tracking in user experience design. Elsevier.
– Blignaut, P., & Wium, D. (2014). Eye-tracking data quality as affected by ethnicity and experimental design. Behavior research methods, 46(1), 67-80.
– Boyko, E. J. (2013). Observational research—opportunities and limitations. Journal of Diabetes and its Complications, 27(6), 642-648.
– Eraslan, S., Karabulut, S., Atalay, M. C., & Yesilada, Y. (2018). Vista: Visualisation of scanpath trend analysis (sta). In Proceedings of Turkish National Software Engineering Symposium (UYMS).
– Hansen, D. W., & Ji, Q. (2009). In the eye of the beholder: A survey of models for eyes and gaze. IEEE transactions on pattern analysis and machine intelligence, 32(3), 478-500.
– Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. OUP Oxford.
– Nielsen, J., & Pernice, K. (2010). Eyetracking web usability. New Rider

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