What is UX Research?
UX (user experience) research is the systematic investigation of users and their requirements, in order to add context and insight into the process of designing the user experience. UX research employs a variety of techniques, tools, and methodologies to reach conclusions, determine facts, and uncover problems, thereby revealing valuable information which can be fed into the design process.
UX research aims to gather information from users by way of a variety of qualitative and quantitative methods, including interviews, contextual inquiries, diary studies, personas, card sorting, and usability testing. The focus is on the systematic approach to gathering and interpreting collected data. Due to this, UX research demands the structured and methodical selection and application of the most appropriate tools for information gathering. Activities can take place at the generative (ideation) and the evaluative (validation) stages of a development process. UX research helps a design team inform the design of products and services, validate its assumptions, and—ultimately—reduce the cost of delivering a successful product.
Featured UX Research Project
I have worked with over 50 clients in the last 12 years across a wide variety of projects. Below I detail one of my favourite UX research projects and outline some of the processes and methods involved in the research process.
Toyota Financial Services
I was hired to work with Toyota Financial Services and lead the research on their car purchase checkout process/flow within Norway, which were the stages which directly followed on from a user configuring a vehicle and attempting to arrange finance on, or outright purchase, the vehicle. One prototype already existed though this had not been designed to account for cultural specificities within Norway and it was known there were issues with a particular page, namely the Review Plan page, therefore I provided cultural design guidance to the designer for a new version and planned a (between-subjects) usability test which would compare the usability of each version.
10 participants were recruited for the testing with the user trials being conducted on site in Norway under controlled conditions. The sessions lasted for approximately 60 minutes, and ergonomic standards were observed with regard to the participants’ use of the facilities (ISO 9241-5:1998; ISO 9241-6:1999; ISO 11226:2000).
The testing was conducted using a laptop PC with an Intel i7 processor running Microsoft Windows 7 with a built-in LCD panel (17 inch, resolution 1400×600, 60Hz refresh rate, 32bit true colour).
The input devices used were a standard UK keyboard and mouse, and the computer was connected to the Internet through a wireless local area network connection using Internet Explorer (version 10.0) and Chrome (64.0.3282.167).
An adjacent observation screen, was used to monitor the user trials. A live feed from the participant’s computer was captured using an external video capture card (Elgato HD 60S), and recorded using both Tobii Studio (on the main usability testing machine as well as being both streamed and recorded to YouTube via a second, high spec laptop which consumed and broadcasted the feed from the video capture card. The spec of this laptop included the latest i7 Kaby lake processor, 16GB RAM, and an Nvidia GTX 1050 graphics card.
Incorporating eye-tracking into the usability testing offered following advantages:
- It allows for ‘retrospective think-aloud protocol’ to be used which can be less distracting for the user as they can be in the moment, just interacting with the UI.
- It allows cumulative heat maps and gaze plots, providing a consensus view of key areas of interest (quantitative data), which can be analysed using descriptive statistics.
- It allows real-time gaze tracking, facilitating the moderator in asking good probe questions based on a users saccadic fixations (eye movement).
For example, figure 1 shows a heat map from usability testing with an eye-tracker on the Google homepage. As can be clearly seen a gaze plot provides quantitative data and can be used to enumerate the number, and path, of fixations on page. For example the first user quickly scans the page and then returns to the top, the second doesn’t go below the fold and chooses a video, whilst the third user scans every item in detail. Descriptive statistics can be generated and analysed from this data.
Similarly eye-tracker can be very useful in determining user behaviour in a granular way which would not be possible simply by speaking to a participant. Figure 2 shows a gaze plot from a similar usability test on the Google homepage. As can be clearly seen a gaze plot provides quantitative data and can be used to enumerate the number, and path, of fixations on page. For example, the first user quickly scans the page and then returns to the top, the second doesn’t go below the fold and chooses a video, whilst the third user scans every item in detail. Descriptive statistics can be generated and analysed from this data providing useful charts and graphs for analysis.
Which version of the ‘Review plan’ page do Norwegian participants prefer? Version A or Version B?
A hypothesis that version A (the culturally adapted version) would be preferred was formulated based on an analysis of empirical evidence around cultural dimensions and user interfaces, namely work by Hofstede , Marcus , and Cyr .
A split-run test was used for the site to deduce whether or not one version performed better than the other as shown in figure 3.
Two key dimensions which were particular to Norwegian users and formed the basis of our hypothesis were: Low power distance, which equates to less structured information (i.e. not collapsed) and high power distance which equates to more highly structured information. Note, in figure 4, how similar Norway and Germany are in this regard. China is included as a culture that swings radically in the opposite direction (user interface controls such as accordions would test better in China based on these parameters).
Norway’s low score on the dimension of long term orientation equates to content focussed on truth and certainty i.e. displaying information up-front in an expanded form, not collapsed in an ambiguous form.
From the eye tracking data we are able to generate quantitative date from descriptive statistics by defining areas of interest within the webpages.
The results on the left in figure 5 show the mean fixation count for each participant in the defined areas of interest, the areas of interest are defined as the expanded page on version A and the collapsed version with accordions on version B. The software automatically calculates number of saccadic fixation count within those defined areas. We can also generate different descriptive statistics within the software such as tine unto first fixation.
A lower fixation count is better in this context, it indicates reduced cognitive load for the user and that they have found what they are looking for, rather than having to keep scanning the page looking for relevant information.
Figure 6 shows a time segment interval of saccadic activity from the areas of interest in the variant page for variant A, this is a typical number of fixations for this type of page. It can be seen that the user scanned this page from left to right and then down in a typical Z pattern. Subsequently they did an upward rescan to ensure they have not missed anything.
Similarly, figure 7 shows similar date but this time for variant B, this time the user started in a typical way, tracing across the page in a z pattern but then started repeatedly rescanning back and forth, trying to make sense of the accordion controls on the page and understand why there was no information presented at the top level. This attempt by users to try and make sense of the complicated page structure resulted in a lot of retracing and consequently many more fixations than in variant A (figure 6).
If we look at the mobile version (figure 8) the descriptive statistics again support the hypothesis that version A is objectively the more usable version.
The interesting thing here is that one of the users, indicated in the chart purple, was overly verbose and talkative and was analysing the page when they were advised to just use the site without verbalising their actions. Though we try and filter out participants from UX and design fields to avoid this, invariably you will come across someone that will slightly skew your mean averages, particularly when using lower numbers of participants (only 3 participants could be tested for this version). Even so, the data still supports the hypothesis. Figure 9 shows an analysis of a time segment interval of the saccadic activity from the actual page for version A and B. As can be seen, there are many more fixations for version B.