The challenge is that you will often not know if more conventional measures will be able to detect any existing differences or if you should use a more sensitive instrument such as eye tracking. Based on the participant sample size, past experience with similar research, as well as knowledge of the environment where the tested object is typically used, you can only try to make an educated guess.
If negative implications of poor performance are high, you should consider supplementing the research with eye tracking to make sure all your bases are covered. Even if more conventional measures manage to reveal performance differences, the eye tracking data can then be used to support these other measures. For example, number of fixations can be used to support time-on-task data, and pupil diameter can be used to support results obtained with subjective workload assessment tools such as the NASA Task Load Index (NASA-TLX).
The “Prescription Drug Labels” case study provides an example of how eye tracking helped detect differences in search efficiency and ease of information processing between drug label designs.
Measuring Attraction-Related Differences
Eye tracking is perfectly suited for answering questions such as “Which package captures the most attention?” “To which ad or product placement do the eyes go first?” “Which homepage version creates the most interest?” or “Which commercial will result in the highest emotional response?” While these are not strictly usability questions, they are not unfamiliar to many UX researchers. Companies want more engagement in their promotional material and products themselves, and what better method to help them measure their success than eye tracking.
The “Baby Product Packaging” case study describes an example of how eye tracking helped compare the attractiveness of packaging of potty chairs and baby monitors of various manufacturers, and provided benchmarks for future testing.
Case Study: Prescription Drug Labels
Why Eye Tracking?
In an effort to make drug packaging production more cost-effective and reduce regulatory review time of new drugs, Abbott created a standard template to replace all the different designs for its prescription drug labels. The main objective of the study was to investigate whether or not the proposed label template had an impact on pharmacists’ performance.2 It was important that the new design didn’t make things worse!
Considering Abbott’s concern for safety (because any label change could add to the estimated 1–2% of dispensing errors that generally occur in pharmacies), the ideal performance metric would be error rate. However, my team did not expect to see many errors in the study based on their already low (relatively speaking) incidence rate in the real world and because errors are even less frequent in a lab setting, where people know they are being watched.
Thus, while we were going to collect error data, we also decided to measure factors that could contribute to error, such as difficulties in finding information and increased cognitive processing demands. The number of eye fixations was selected as the indicator of search efficiency, while average fixation duration was chosen to measure information-processing difficulty.
How Eye Tracking Contributed to the Research
Pharmacists were shown several drawers with drugs and were asked to fill several prescriptions. As we predicted, they made too few errors for us to be able to make meaningful comparisons between the existing and new labels. Eye movement analysis showed that the new template required fewer fixations than one of the three existing designs that were tested (see Figure 2.8). This was a positive result because it indicated that the new template made the search for information on the label more efficient.
However, the new template also led to longer fixations, suggesting that more processing time was needed per fixation. Based on the location of the longer fixations, we attributed them to the closer proximity of the drug name (e.g., Lexidra) and dosage strength (e.g., 120 mg) in the new template. While the eye did not have to move to get to the next element, it had to linger longer because there was simply more information to process in that one location.
FIGURE 2.8 New label design (left) and one of the existing label designs (right) with superimposed gaze data from one of the pharmacists participating in the study.
Because the increase in fixation duration was offset by a decrease in the number of fixations, we concluded that overall, the new labels did not perform any worse than the existing labels we had tested.
Case Study: Baby Product Packaging
Why Eye Tracking?
This study aimed to understand the in-store customer experience created by the packaging of Fisher Price’s potty chairs and baby monitors, and compare it to the experience created by the competitors’ packaging. Three competitors were chosen for each product category. Following the AIDA model shown in Figure 2.9, my team decided to measure the success of the packaging in four stages of the shopping experience: Attention, Interest, Desire, and the final Action (i.e., purchase).
Eye tracking was particularly well suited for assessing the first two stages of the funnel: attention and interest. To evaluate attention, we gauged the extent to which participants noticed the packaging when approaching the product shelf. In the first 15 seconds in front of the shelf, which packages grabbed their attention and which were just glanced at and dismissed? To assess interest, we measured how much time participants would spend examining the packaging when making their purchase decision (see Figure 2.10).
FIGURE 2.9 The AIDA funnel representing four stages of the decision-making process. The product package or ad must grab the audience’s attention, engage their interest, and build a desire for the product offering, which finally leads to action.
FIGURE 2.10 Participant examining one of the potty trainer packages. Each of the 24 study participants had to choose the potty chair that she would purchase.
How Eye Tracking Contributed to the Research
The study results showed that the Fisher Price potty chair packaging was comparable to the packaging of First Years and Munchkin in the amount of attention it initially received, while packaging by Summer attracted the most attention. However, when participants were asked to take their time and decide which potty chair they would purchase, there was no difference in dwell time between the four packages. Even though participants were given unlimited time, each package was looked at for an average of only 16 seconds before the final decision was made.
In the baby monitor category, Fisher Price placed second, together with Safety First and Sony. Graco’s packaging