Tutorial 7: How To Analyze Your UX Research Findings

“Hi there! 💁 I’m William, a course specialist here at CareerFoundry. I have the honor of accompanying you on the final stretch of this UX Research for Beginners Course. I’m also one of the people who works on admissions for our UX Design Program—get in touch with me if you think UX design could be your calling!”

What are we going to do today?

Welcome to tutorial seven—the grand finale of your UX research short course! Thus far, we’ve considered all the practicalities of user research, from techniques and tools to recruiting participants and actually conducting user research. So what happens next? In this lesson, we’ll show you how to analyze your user research data in order to turn it into valuable, actionable insights. In other words, how can you use the data you’ve gathered to inform the design process? We’ll lay out a clear step-by-step guide for conducting research analysis and introduce some useful techniques such as data coding and affinity mapping. We’ll also touch on the importance of sharing your findings with other stakeholders, and show you how to do so effectively. By the end, you’ll be ready to transform your user research into a meaningful plan of action!

We’ve broken this lesson up as follows:

1. An introduction to UX research analysis
2. Collect and organize your research data
3. Refer back to your research objectives
4. Explore your data to uncover findings
5. Synthesize your data
6. Share your insights
7. Practical exercise

Ready to learn all about UX research analysis? Let’s go!

1. An introduction to UX research analysis

Once you’ve conducted user research, you’re ready to enter into the analysis phase. No matter what research methods you use, the next step is to turn your raw data into valuable insights. When analyzing your research data, you’re essentially asking: What does the data mean? What does it tell you about the product you’re designing and the people you’re designing it for? Ultimately, how can you use the data you’ve gathered to inform the design process? 

How you analyze your research data will depend on the type of research conducted—qualitative or quantitative—and the techniques you used. Analyzing quantitative survey data is quite different from analyzing qualitative interview transcripts! But, regardless of whether you’re dealing with a numeric dataset or a verbal interview, you’re always on the lookout for patterns and themes that can tell you something meaningful about the user, the product, or both.

When it comes to research analysis, qualitative data tends to be a bit more tricky; it can take many different forms (interview transcripts, observation notes, diary study entries, etc.) and often ends up being rather lengthy in nature, meaning that the most valuable insights are not always immediately apparent! But fear not: our guide to research analysis focuses on qualitative data, so you’ll know exactly how to tackle those tricky interview transcripts when the time comes.

So how do you go about analyzing your user research data? Let’s find out.

2. Collect and organize your research data

We’ve said it before and we’ll say it again: qualitative research is messy business! So, the very first step in the analysis process is to gather all your research data and organize it in a way that’s both logical and manageable. Ideally, you’ll have all your research artifacts in one place. If you’ve got audio recordings, video clips or hand-written notes, you’ll first need to transcribe them or convert them to a digital format. The transcription process can be extremely time consuming, but it will help you get familiar with your data, so stick with it!

At the same time, think about how you’ll organize your files. If you’re analyzing the data gathered from interviews, it might be useful to create a folder for each participant. It’s also a good idea to create a quick profile document for each user; this way, you’ll have the person in mind as you explore their research data.

Once your digital space is organized, it’s time to whip your physical workspace into shape. You’ll need a dedicated area to work in (aside from your computer) and plenty of room to spread out, so clear your desk, have a blank wall or whiteboard at the ready, and make sure all your research artifacts are within reach.

In this first step, you’ve organized all your research data so that it’s easily accessible and ready for analysis. Time for step two!

3. Refer back to your research objectives

With your research data organized into some kind of logical system, you’re almost ready to jump into the analysis itself. Before you do, it’s important to refer back to your research objectives. Why did you conduct user research in the first place? Did you want to develop empathy for your target users, or did you want to find out if an existing product is meeting your users’ needs?

A popular turn of phrase in the UX design world is that the project dictates the process, and this is certainly true of UX research. Your initial research objectives will guide your analysis, helping you to pick out the information that is useful.

Imagine you’re designing a budgeting app for students. Your research objective is to understand who your target users are and what their motivations might be for using your app, so you’ve conducted several in-person interviews. When analyzing this research, you’ll be looking for anything that tells you about the user, such as demographic data, information about their lifestyle, and quotes that might reveal how they feel about the product or the topic of budgeting in general. While there may be other interesting information in there, it’s not necessarily relevant if it doesn’t relate to your research objective; that’s how you’ll dig out the real gems!

4. Explore your data to uncover findings

With your research objectives firmly in mind, you’re ready to get hands-on with the data. During this phase of analysis, you’ll comb through all your research looking for relevant themes, patterns, and stories. To make sense of your qualitative research, a good first step is to assign codes to the data. This is especially useful for structuring long pieces of text, such as interview transcripts. Let’s explore coding in more detail now.

Coding your qualitative data

A code is essentially an index or label which categorizes different chunks of text within, say, an interview transcript. Through the process of coding, you can work through the transcript line by line, highlighting phrases or sections of interest and assigning a relevant label. Bear in mind that a code is just a description or summary of what’s being said; it’s not an interpretation. Coding long bodies of text will help you to identify key themes in your user research (more on that later!).

Let’s demonstrate the coding method with a simple example. Imagine you’re designing a dating app. You’re interested in finding out what kinds of dating services your audience has used in the past, how often they use dating services, and how they feel about them in general. What codes might you come up with for the following snippet of text? We’ve highlighted some potentially “codable” phrases in bold.

“I’m not a fan of online dating. I have a Tinder account, but I only log in once every few months or so. I haven’t tried any other dating services. It just doesn’t seem to work for me.”

Appropriate codes for these bold phrases might be “doesn’t like online dating”, “Tinder” or “dating app”, “frequency of usage” and “online dating doesn’t work”. Can you see how we’re effectively labelling different elements of what the user has said? You can simply write your codes next to the relevant text, and remember: they don’t need to be highly sophisticated! All you’re doing at this stage is labelling different sections within the transcript. It’s perfectly fine to end up with lots of different codes, as well as repetitions.

Once you’ve coded all your data—so, in this case, all of your interview transcripts—what you’ll have in front of you is a rather messy collection of different codes. The next step is to group similar codes together into broader categories, or themes. 

Identifying key themes

While codes serve to highlight interesting information, themes require you to actively interpret the data. You’ll now sort your codes into overarching categories which you’ll then label. These are your themes, and they should start to give you an idea of what information was most prevalent or useful across your user interviews. The codes “dating app”, “online dating” and “speed dating” could all be grouped under the theme “dating services”. Codes such as “difficulty finding local matches” or “lack of replies and engagement” could be thematized as “pain points”.

It’s important to bear in mind that grouping your data into themes is a highly iterative process. Be prepared to go back and forth between your original interview transcripts, your codes, and the emerging themes. At this stage, it’s worth writing all your codes and themes onto sticky notes which can be easily moved around. This is where your big blank wall will come in handy!

Affinity mapping

Another useful technique for grouping and understanding your research data is affinity mapping. First, you’ll go through all your data, pulling out quotes and observations of interest—much like the coding exercise from before. You’ll write each point on a separate sticky note and then pick one sticky note as your starting point, which you’ll place on a blank wall or whiteboard.

Next, you’ll find similar notes and stick them around the first, creating a cluster of Post-its that all share a particular theme. Once you feel that the cluster is complete, pick a label for the overall theme and stick it next to that cluster. Repeat the process with new clusters and themes until you feel like you’ve exhausted all avenues. Feel free to duplicate your sticky notes if they fit into more than one cluster.

Eventually, you’ll end up with an entire wall or whiteboard filled with sticky notes and, most importantly, themes. In the next step of your user research analysis, you’ll use these themes to draw meaningful insights from your data.

5. Synthesize your data

Synthesis can be defined as the process of creating spontaneous concepts and ideas based on the facts you’re analyzing. In the case of user research, this means turning your themes into something meaningful; you know what your themes are, but what are they telling you? Before we look at how to synthesize your data, let’s first define the difference between findings and insights. These terms are often used interchangeably but actually mean different things (especially in the context of user research) so it’s important to get them right!

Findings vs. insights

A finding is a fact or statement that simply tells us what is happening. It doesn’t tell us why, or provide us with a meaningful solution. An insight, on the other hand, describes an aspect of human behaviour or user motivation. It enables us to see how we might go about solving a particular user problem.

Let’s go back to our budgeting app example. One finding might be that “users tend to use a mixture of different apps in order to track their budget”. From this finding, you might derive the following insight: “There is currently no all-in-one solution for budgeting, so users need to rely on multiple apps.” Can you see how the insight hints at a possible solution? Based on that insight, you might choose to design a one-stop-shop budgeting app for your target users.

With that in mind, let’s return to the task at hand: synthesizing your research data.

How to synthesize your research data

You’re now going to comb through all your themes and clusters from step three in order to pull out findings that can potentially be turned into insights. You’ll work through each theme, starting with those you consider to be high priority depending on your research objectives. So, if your goal is to find out more about your target users, you’ll start with those themes that are likely to contain key user insights. How you organize your themes, findings, and insights is up to you—we find that the sticky note and whiteboard system works well! Here’s an example of how the synthesis process might look in action:

Theme: Pain points → Finding: Several users gave up on their purchase, stating that the checkout process required too many details to be filled out → Insight: The current checkout process is too complex and long-winded.

As you synthesize each of your themes and their subsequent findings, you should start to see a range of useful insights emerging. When you’re ready, identify all the most crucial insights and list them in a document. That’s it—synthesis complete!

6. Share your insights

The final step in the analysis phase is to share your insights with your team. User research is only truly valuable if everyone can learn from it, after all! You may choose to simply share your insights via a Google doc, or you might present them in person; as long as all key stakeholders understand what research you conducted, why you did it, and what you learned, it’s up to you how you choose to share your work.

Having shared your insights with the team, you’ll then turn them into something actionable. Often, your research insights will become your problem statement—in other words, the user problem that you will aim to solve. You might also turn your insights into “how might we” questions, rephrasing a problem as a design opportunity! Let’s try this with our example insight from step four. The insight “There is currently no all-in-one solution for budgeting” could be turned into a how-might-we question as follows: “How might we enable users to satisfy all their budgeting needs in one place?”

This enables you to start considering your research insights in terms of a concrete user problem and, eventually, a solution. Ultimately, the insights you uncover through user research and subsequent analysis will guide the next steps in your design process, showing you what you need to focus on and why. That’s what you call making smart design decisions!

7. Practical exercise

We’ve now covered the user research process from top to bottom! Are you ready to tackle one last practical exercise?

For today’s task, you’re going to get hands-on with codes and themes. Here is an interview with CareerFoundry alumnus Jeff Buchanan who, after working as a pastor for almost twenty years, retrained as a UX designer. In the interview, Jeff shares what it was like to make a career change a bit later on in life, as well as some of the challenges and joys he encountered along the way. Putting your UX researcher hat on, can you code one or two paragraphs of the interview transcript and organize them into themes? Before you get started, it might be helpful to pick one of the following research objectives. This will guide your analysis!

1. What are users’ main motivations for making a career change?
2. What challenges and pain-points do users encounter when studying online?

While this interview wasn’t conducted as part of user research, it will get you thinking in terms of codes, themes, findings, and insights—all key components of qualitative user research analysis!

It’s a wrap!

That brings us to the end of tutorial seven, which means you’ve almost completed your UX research short course—way to go! We hope you’ve enjoyed learning all about user research and that you now feel equipped with some useful UX research techniques. There’s just one more step needed to get you across the finish line: don’t forget to take our final UX research quiz!

If you’d like to learn more about user research and the UX field in general, be sure to check out the following resources:

• UX research as a specialist role: The rise of the UX researcher
• What does a UX researcher do? The ultimate career guide
• How to build a UX research portfolio: A step-by-step guide