Eye-tracking does exactly what is says on the tin, it tracks eye-movements. 
On the surface it really is as simple as that.

This is achieved by a combination of cameras, near-infrared light, and sophisticated algorithms that work together to determine the location of a users pupil and translate this into where their gaze is focussed. 

The ability to see through your customers’ eyes provides invaluable insights into how your product, website, or marketing outputs are being perceived. Objective quantifications of users attention allow you to make informed decisions about how to design and optimise your content to enhance your customer’s experience with your brand.

This blog post is a gentle introduction into the world of eye-tracking. Where possible I will provide examples of how different metrics can be used for analysis. However, it isn’t always as black and white as “high fixation count equals good”. Eye-movements are highly task dependent, so it is always important to clearly define your task at the beginning of your experiment so you can choose the correct metrics and processes for your analysis. 

Eye-Tracking Devices

There are different configurations of eye-trackers, suitable for various experimental set-ups. However, all eye-trackers have at least one eye-facing camera and a method of recording the users view. 

Eye-Tracking Glasses
Designed to fit and look like a traditional pair of glasses, with multiple cameras built in. The glasses have a camera facing outward, to record the users world view, and at least one camera facing the users eye(s).

Desktop or Laptop Mounted Eye-Trackers
These typically sit below the screen with cameras facing towards the users face. The screen is recorded at the same time as the camera records the eye-movements, and the fixations can be automatically mapped into the two-dimensional environment

Tablet and Mobile Phone Eye-Trackers
To record users eye-movements when using mobile devices, similarly with the desktop or laptop eye-trackers, eye-recording cameras are place below the device screen facing the users eyes. An additional camera is then used to record the activity on the device’s screen from above.


Heat Maps
A heat map can be generated using either the fixation count or the total fixation duration of the users’ data, either individually or collectively. These are a great quick and easy way to see which elements on a webpage, magazine advert or product photography attracted the most attention. A further in-depth analysis is then required to determine the indicated behaviours behind these patterns, but heat maps can be a key indicator for how AOI’s should be determined.

A heat map of 5 participants’ data on an ASOS product page

A gaze-plot is the visualisation of a scanpath, so it shows us the user’s fixations and saccades in chronological order. These are a really handy tool to gain an insight into the user’s journey. Gaze-plots are best viewed one participant at a time, as you can see below they can get quite messy quite quickly with lots of users data combined

A gaze plot of 5 participants’ data on an ASOS product page

Analysis Metrics

Fixations and Saccades
A fixation is a measure of a users eye-movement remaining stable for a minimum of 60 ms, and a saccade is the rapid eye-movement between two fixations. When plotted in chronological order, the fixations and saccades make up a scanpath. 

Area of Interest (AOI)

Areas of Interest (AOIs) are drawn onto the scene that the user has viewed, and are used to statistically analyse and compare fixations metrics between different areas. AOIs can be drawn around any elements of a scene that you want to compare, for example in the image below AOIs have been drawn around the different products, so it would be possible to statistically compare how much attention each of the products received. Additionally, it might be interesting to quantify how much time users spend looking at the top menu or the filter bar, so AOIs have also been drawn around these.

AOI’s drawn onto an ASOS product page.

Time to first fixation
The time to first fixation quite simply tells us how long it was until the user looked at a specified AOI. This metric is particularly useful when you want to understand how long it took your user to notice a call-to-action button on your webpage. Or, how long they browsed the supermarket aisle before they noticed the “Buy One Get One Free” sign.

Fixation Count
The fixation count is the number of fixations that occurred within an AOI during the eye-tracking recording. Using the image above as an example again, it might be of interest to compare which products had the highest fixation count to highlight how much attention the product, or style of image, attracts. In other task situations it might be of interest to compare which tasks required the fewest number of fixations, to see which processes are the most seamless for your user. 

Average Fixation Duration
The average fixation duration tells us the mean fixation length in any AOI, or during any time segment of a users experience.  There is evidence that demonstrates a correlation between the fixation duration and level of cognitive effort. A long average fixation duration is indicative of a higher level of cognitive effort. Particularly short fixations indicate that the user may have only briefly glimpsed over the stimuli. This is a highly task dependent fixation metric, varying results would be expected for different tasks.

Total fixation duration
The total fixation duration (also known as the dwell time) is the cumulative duration of all of the fixations on a particular AOI. Being able to quantify the amount of time spent viewing different aspects of a scene or webpage allows to clear objective comparisons of what attracts the most or least attention. 

Mouse clicks
Mouse clicks deserve and honourary mention on this list, as when they are combined with eye-tracking fixation metrics they can enhance the understanding of the user’s behaviour. When you know where and when the user has clicked, you can correlate this information with eye-movements with data; such as the time between first fixation and a mouse click on an AOI, or total fixation duration on an area before the user chose to click there. These metrics can indicate how easy (or difficult) it was for the user to identify and select the link that they wanted.

Such a seemingly simple tool can provide a wealth of valuable information. With the correct experimental design, clear hypotheses, and scrupulous analysis processes eye-tracking can  be the perfect tool to enhance understanding of consumers’ behaviour. 

If you would like to read some eye-tracking analysis in action, then check out the series we did with Formisimo, using eye-tracking to explore how users navigate their way through various online forms. To find ask about how Nudge can use eye-tracking to help you understand your customer’s experience then get in touch using the form below. 

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