User experience (UX) is more than just how a person interacts with a product or service. It also encapsulates that person’s attitudes and emotions associated with that experience, as well as their perceptions of the system itself. That said, UX considers every element that shapes the experience — from the way a product provides convenience, to the way it makes a user feel. It works hand-in-hand with UI design, which refers to the visual design of a product or service.
In this regard, it’s important to note that the effectiveness of UX design doesn’t solely rely on best practices or UX theories. These are information gathered from a generic audience and, although useful for providing overarching UX design principles, do not necessarily dictate the best design decisions for every project. Different projects have differing requirements and, most crucially, different target users. Therefore, what may work with one may not have the same results with others. You need data and research, on top of theory, to make informed and insightful design decisions — which is where analytics plays a major role.
What is Analytics?
Who better to provide feedback and suggestions for a particular UX design than from the users themselves and how they interact with it? Analytics in general refers to the analysis and communication of meaningful patterns of data collected from a specific audience. When undertaken correctly, organizations and individuals can use massive amounts of information from and about their customers for better decision-making.
There are two types of data that can be gathered: qualitative and quantitative. UX Booth elaborates on the differences between both types, explaining that qualitative data is gathered through user research, while quantitative data can be gained from analytics tools. The former is done in order to understand people’s behavior or why they do certain things. Meanwhile, the latter is focused on identifying the specific actions users do and how many users do it. User research may take days or weeks to see results, but quantitative methods can show hard facts in real-time.
Researchers often employ both methods in order to gather rich information. This perhaps is the easiest part of the process. The crucial part is how they would make sense of all this data. Analytics experts Ayima note that data is only useful if you know how to interpret it, and the information gathered is only as good as your ability to study and apply it in decision-making. There are many instances where two people look at a the same set of data and end up with different conclusions. This may be due to differing approaches or perspectives on user information, or can perhaps stem from internal biases that affect how people and organizations find meaning and patterns of information. Hence, it is important to undertake a scientific approach to data, while also exercising plenty of empathy for the end-users who interact with a design.
Why does Analytics matter to UX Design?
Designing apps and websites used to be about creative talent rather than an exact science. But as UX Planet’s piece on data-driven design points out, data can help designers create a design that is not only aesthetically pleasing, but also functional and responsive. A well-designed app or website can encourage visitors to stay longer and engage with the material. When armed with data, UX designers are able to:
- Optimize the customer journey
- Discover reasons behind high exit rates (or where users leave and why)
- Boost shares and increase conversions
- Find relevancy in certain design elements
- Tailor-fit the content to the intended user
As you can see, vital information on user behavior and patterns of engagement (or non-engagement) can drastically help improve design outcomes when used correctly. It is especially useful when done continuously, not only to help point out existing problems in UX design and interface, but also to measure the effectivity of the implemented solution (or set of solutions). In this regard, UX Matters listed a few ways of recording and measuring outcomes of design changes. These include A/B testing (best for testing two or more variations of a page) and multivariate testing (for studying different combinations of elements on a single page). In addition, multi-page testing can help designers look into how changes on different site pages can affect a user journey.
What tools can you refer to?
There are many analytics tools designers can utilize in order to study data. The type of analytics tool used depends on what exactly researchers are looking for. If the budget allows, it is recommended to use several types in order to paint a full picture.
Web Analytics – These are the most accessible of all types of analytics tools, since they are often free. Web Analytics tools are used for gathering information on website traffic and provide valuable insights into what visitors do on a website. However, these tools usually don’t offer explanations for the quantitative data reported, and will need further support or inference.
Heatmap Analytics – These tools take analytics a step further by showing how exactly visitors interact with a website or app. They apply heat maps on pages for researchers to recognize patterns, like where the visitor clicks, or in the case of an app, where the user swipes or taps.
Session Recordings – Use this type of analytics to track entire visitor sessions. Session recording tools will show full reports of how the visitor navigates a website, revealing entry points and exit points, as well as which page the user lingers on the longest.
Real-time Analytics – Tools like Clicky provide real-time details on each visitor, including demographics and which device is used. They often involve more sophisticated methods, and are useful if your website receives a large amount of active visitors per day.
Now that we have established the importance of data to UX design, don’t forget to apply these research-based principles on your own portfolio. RookieUp previously compiled a list of projects for those who are just starting out in a career in UX design. These can help you hone your skills on how to create good designs based on data.
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