Eyetracking has been a heavily debated subject within the field of User Experience Design and in particular within the Usability community. Some argue that eye fixations don’t necessarily equal attention or understanding, and question whether eyetracking should be used to support traditional usability findings. However, the other side argues that when eye tracking data is used in conjuction with traditional usability techniques, it can provide deep insight into where participants look during a task which can help us determine why usability issues are present within an interface.
Eyetracking metrics overload
How do I know what metrics to use?
While some metrics can generally be used in a wide variety of usability studies (see list below), it is important to know what you’re going to want to study even before your testing begins.
- Identify success metrics: What, in context of a specific task, constitutes success for each page in your interface? Is it clicks on a call to action? Interactivity with a certain element of a page? Reading blocks of content? Knowing what you want to measure will give you a sense of what metrics will be needed.
- Identify which metrics apply for each area of interest: Some metrics may apply to certain areas of interest but not to others. For example, you may define success for a page as clicks on a call to action and visibility of brand elements (e.g. taglines). So, you may focus more on number of clicks and time to click for calls to action, and moreso on total observation time for non-clickable branding elements.
- Focus on the basics: Don’t try to force insights out of every eyetracking metric. Just because someone looked at an image 5 times doesn’t mean that it should be changed to try and make people look at it 10 times. When picking metrics to analyze, pick metrics that will lead to actionable and informed insights.
Top eyetracking metrics to focus on for usability studies
- Percentage Fixated – (also known as Participant Percentage in some versions of Tobii Studio) The percent of participants who fixated at least once on an area of interest. Let’s say you’re trying to determine how many people noticed a call to action during a task. This metric can show you how many participants fixated on this call to action. Using this metric in conjunction with observation length and mouse click count (explained below), you can see out of those who saw an element of a page, did they look at it long enough to register it in their memory (this can be subjective), and out of those who saw the element, how many clicked on it in context of the task?
- Observation Length – Across all fixations on a media element (page), how long a participant looked at a specific area of interest. Observation length can generally give you the best indication of the division of attention various elements of a page receive. You may notice a block of text receives less than a second of total observations, indicating users are not reading the messaging in full. Or you may see in conjunction with the percentage fixated metric that while an element of a page may not have been seen by a lot of participants, when it was seen, it received a lot of attention. As with all metrics, qualitative questioning can help determine the reasons why some elements were looked at for longer than other elements.
- Time to First Fixation – The time it takes for a participant to first fixate on a specific area of interest. Taken on its own, this metric doesn’t reveal much. However, when compared to other areas of interest, time to first fixation can show you across the board which elements of the page are drawing a user’s attention in the context of the task they are asked to perform. You may find that elements of the page completely unrelated to completing a task are competing with those that are related to the task.
- Mouse Click Count – This metric is primarily helpful when looking at the paths that users take from a single decision point within an interface. If given a task to find a specific piece of information, where do users click first? If users interact with faceted navigation, which elements of that navigation do they click on most? While not specific to eye tracking, the eyetracker’s ability to record this data can provide valuable information in conjunction with other eye tracking metrics and usability findings.
The version of the eyetracking analysis software that I have used does not include the more recent additions of “visit duration” and “visit count,” which I imagine could also be used in the context of usability studies to determine the relative amount of time spent on various pages in context of a single user task. Other metrics may have benefits in context of specific goals of a usability study, so always consider all of the data elements available to you when performing an eye tracking analysis.
If you’ve used eye tracking in the context of usability studies before, what metrics have you found to be most useful? Are there any other primary metrics that can best help draw usability insights?