Qualitative data is a type of data that provides insights into the feelings, opinions, and experiences of people. It is used in the UX/UI design process to arrive at a more holistic understanding of who the user is, what their goals are, and why they make certain decisions when interacting with a product.
What is Qualitative Data? (A Definition)
Qualitative data is defined as data that provides a detailed, in-depth understanding of a topic or phenomenon. It is typically used to answer questions about why something happens, or to understand the complex behaviors and motivations of people.
Unlike quantitative data, qualitative data cannot be counted, measured, or otherwise directly represented using numbers.
Who Uses Qualitative Data?
Most teams utilize qualitative data, in one way or another, to improve their own processes and better understand the end user.
UX researchers and designers use qualitative data to help understand the user perspective and create more user-centric products, as well as to help them interpret quantitative data.
In addition to the design team, most other teams within a company will collect and analyze qualitative data, like:
- Customer Service teams, who rely heavily on qualitative data to track trends and submit product feature or bug fix requests.
- Marketing teams, who carefully monitor phrases or characteristics of customers to better tailor their designs and messaging.
Examples of Qualitative Data in UX Design
One common example of qualitative data is an interview. In an interview, a researcher asks questions to gather insights about a person's experiences, opinions, and feelings on a given topic. The answers to these questions provide rich, in-depth data that can be used to inform the product design process.
Other examples of qualitative data collected in the UX design process are:
- Case Studies
- Video Recordings
- Audio Recordings
- Focus Groups
Why is Qualitative Data Important in UX Design?
Qualitative data is important because it helps designers understand user needs, wants, and pain points.
It's especially helpful when matched up with quantitative data, since it can then be used to create a more holistic, deeper understanding of the why behind user behavior trends.
How to Collect Qualitative Data?
Qualitative data can be collected in-person or remotely through a few common methods…
One of the most straightforward methods of collecting qualitative data is by conducting interviews with existing users or other ideal customers. An experienced interviewer will create a list of interview questions, taking special care to ensure that none of the questions are leading or will otherwise influence the interviewee.
Less time-intensive than an interview, surveys are a popular way to collect targeted feedback and ideas from participants. Surveys differ in length, ranging from a 5-star rating system (quantitative data) and an invitation to share why they rated it that way (qualitative data), to extensive, multi-page questionnaires.
If you're conducting usability testing, you might take note of certain observations during the session: facial expressions, ad hoc comments, etc.
A focus group discussion can be an incredibly valuable way to monitor qualitative trends in a demographically similar or demographically diverse group. For example, you might lead a discussion about a specific feature set within your product, and take note of trends—and outliers—in how the participants view and respond to the conversation.
How to Analyze And Utilize Qualitative Data
Some common methods of analyzing qualitative data include:
- Affinity mapping, which is a way to organize and group related data points together.
- Thematic analysis, which is a process of identifying themes within the data set and then coding those themes.
- Content analysis, which is a method of making inferences by quantifying and categorizing data.
- Discourse analysis, which is a way of analyzing written or spoken communication.
Once the data is analyzed, the product team can then use the results to decide on the best course of action on the next feature design or iteration, as well as use it to help create more impactful roadmaps for future sprints.
What Are The Pros and Cons of Qualitative Data?
- Small(er) sample size required: unlike qualitative data, you can use a much smaller dataset to arrive at accurate conclusions
- Provides context to user-related questions that can't be obtained by any other means.
- Users often find it easier to give feedback in their own words, rather than having to assign a numerical value to their experience.
- Requires time: collecting and analyzing qualitative data requires extensive planning and implementation.
- The patterns are not always obvious, and you might find that the results are inconclusive, or that you need to collect more data before it helps you answer your product usage questions.
- The data doesn't fully represent all users, so you might find that you need to run through multiple iterations of analysis and designs before you arrive at an appropriate solution.
When Should I Use Quantitative vs Qualitative Data in My UX Design Process?
Qualitative data is incredibly powerful when you want to understand why users are acting (or not acting) in a certain way, and establish a more user-centric design process.
Qualitative data, on the other hand, is useful when you want to uncover or monitor certain trends to see what's working and what isn't in your designs.