Data-Driven UI/UX: Combining Facts and Designers’ Fantasy
User experience and user interfaces are without doubt two of the most critical elements of modern IT systems as they have a major impact on their overall acceptability. Designing exceptional user experiences (UX) and user interfaces (UI) is as much an art much as it is science. Moreover, the acceptance of a user interface by an end-user can be very subjective. A user interface that is highly appreciated by one user might be uninteresting or unacceptable for another. The designer’s experience and expertise could alleviate this subjectivity but it cannot completely eliminate it. There are still many cases where designers consider a UI/UX result as excellent yet at the same time, end-users perceive it as poor or problematic. Therefore, UX/UI experts are increasingly seeking ways for collecting and evaluating users’ feedback and opinions on UI/UX elements as a means of bridging the experts and users’ perceptions and subsequently relaxing any risks of non-acceptance. For this purpose, data-driven approach to designing user interfaces and user experiences have emerged and are nowadays used extensively.
Data-Driven Approaches to UI/UX Design emphasize the regular collection of users’ feedback about alternative UI/UX options (e.g., design concepts, menu items, interface elements, user interfaces’ layouts) as a means of alleviating uncertainty about users’ subjective opinions and ending up with acceptable designs. Moreover, data-driven approaches tend to collect information about users’ preferences early on, as a means of properly bootstrapping the design process, through profiling the target users and proposing designs that are viable for them.
Data-driven design is supported by a wide range of different and in several cases complementary methods. In order to select the proper methods, it’s important to understand what is driving the need for data-driven design processes:
Different methods can be used in order to collect and analyze data from end users including:
All of the above data collection methods require proper sampling and recruitment of the target participants. Data collection should be always balanced in terms of socioeconomic factors that affect user experiences such as gender, age, and profession. Nevertheless, the sampling should always refer to the target group of the application, which puts additional constraints on the sampling process.
One of the most popular mechanisms for data-driven UI/UX Design is A/B testing of alternative user interfaces or experiences. In principle, A/B testing refers to the comparative evaluation of an alternative solution (i.e. the B option) against an already implemented option (i.e. the A option). In the case of UX/UI Design, A/B testing can be used to evaluate alternative designs. In particular, two appropriately sampled user groups are engaged in the comparative testing and evaluation of alternative user interface and interaction mechanisms. The two groups engage in the practical use of the application for a specified period of time, for which data are collected and analyzed. The type of data to be collected and used is driven by the target business goals. They could include information on actual sales, data about the speed of navigation as well as engagement data.
Overall, A/B testing is a data-intensive evaluation methodology, which relies on pragmatic data rather than on risky and sometimes subjective assumptions. A/B testing is structured as a four-step process, including (i) Engaging users and collecting data; (ii) Defining a hypothesis (e.g., regarding sales achieved or users engagement); (iii) testing the hypothesis based on the collected data and (iv) evaluating the alternative options based on the outcomes of the test.
An effective, responsive and ergonomic UI/UX Design is nowadays one of the main assets of a product or service. Its acceptance is directly associated with users’ engagement and ultimately with sales and revenues. Development teams must, therefore, consider the merits of data-driven methodologies, which alleviate uncertainty, avoid subjective considerations and bridge the gaps between different stakeholders’ viewpoints. When the data-driven methodologies are made an integral element of the development and operations lifecycle it will ensure an efficient and effective software solution.
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