Analyzing Data

Analyzing Data

Turning Raw Observations into Actionable Insights

Overview for this lecture presentation:

Synthesizing data from user testing and heuristic evaluations into clear, actionable UX findings.

Bridging research and design: giving you a direction for prototyping solutions to the problems you've uncovered.

Why Analyze UX Data?

• To identify patterns and themes across user behaviors
• To translate qualitative insights into design opportunities

• To uncover usability pain points backed by evidence

• To move from observation to insights (what did you learn), then action (how will you fix it?)

This is one of the most important steps in the UX process . Here is where you start connecting the dots between what users said, what they did, and what that means for your design. In this presentation, we'll talk about how to analyze your results and turn them into insights that will guide your solutions prototypes for the next few weeks.

Why Analyze UX Data?

When we analyze UX data, we’re really trying to make sense of what we observed during testing. It’s not about how many times something happened. We're mostly trying to uncover the WHY. Why did this thing happen? Why did users respond that way? We’re looking for patterns in user behavior, pain points that repeat, or moments where users feel frustrated or delighted. Our goal is to turn raw data ( like quotes, screenshots, survey results, and notes) into insights that tell a story about the user experience, what could be improved, and how we plan on fixing it for them.

Thematic Analysis

One of the most useful frameworks for analyzing qualitative UX data is called thematic analysis. This process starts with familiarizing yourself with the data, where you read through your notes, watch your recordings, and look for patterns. Next, you’ll code your data, which means tagging specific parts that stand out (maybe phrases like ‘I can’t find the button’ or ‘This feels confusing.’ ) Then you’ll group similar codes together into broader themes. These themes will become the foundation for your usability findings and your solutions.

Thematic Analysis

One of the most useful frameworks for analyzing qualitative UX data is called thematic analysis. This process starts with familiarizing yourself with the data, where you read through your notes, watch your recordings, and look for patterns. Next, you’ll code your data, which means tagging specific parts that stand out (maybe phrases like ‘I can’t find the button’ or ‘This feels confusing.’ ) Then you’ll group similar codes together into broader themes. These themes will become the foundation for your usability findings and your solutions.

Read NN/g Article

Thematic Analysis Basic Steps

• Thematic analysis helps UX researchers find meaning in qualitative data through:
• Familiarization: Immerse in your notes, recordings, and transcripts

• Coding: Highlight meaningful statements or patterns

• Theme Generation: Group similar codes together

• Review & Refine: Combine or rename themes

• Define & Report: Summarize themes into insights

From Codes to Themes

1. “Couldn’t find the checkout button” 2. “Didn’t realize the cart was clickable” 3. “Didn’t know where to go after adding an item”

• Theme: Navigation & Visibility Issues
• Design Opportunity: Improve affordances and visual hierarchy

Here’s a quick example of how you can turn multiple user quotes into one strong insight. Let’s say three people mentioned not finding or recognizing the checkout button. Instead of listing those quotes individually, we combine them into a theme. In this case, it would be something like Navigation and Visibility Issues. That’s a usability problem backed by evidence. From there, we can start thinking of design opportunities like maybe improving the hierarchy or making important actions stand out more visually.

Using Empathy to Guide Analysis

• Empathy is critical for making sense of user behavior.
• Ask yourself when synthesizing data:
• What are users feeling, thinking, or expecting?
• Why did they make certain choices?

• What frustrations or joys did they express?

• Empathy is your secret weapon. You’re not only finding errors, but you’re also trying to understand the human story behind them. Ask yourself what the user was thinking or feeling when they said something. Why did they make that choice? What expectations did the system not meet? The goal is trying to understand the user experience at a deeper emotional and cognitive level.

Visualizing Your Findings

• One of the best ways to analyze your data is visually. Here are some visual tools for data synthesis:
• Affinity maps: Cluster similar observations
• Journey maps: Plot user experience over time

• Empathy maps: Capture what users say, think, do, and feel

• Insight clusters: Link pain points to potential solutions

• The goal for these methods is to help you see the relationships between pieces of information. You can use digital sticky notes in Miro, FigJam, or even draw them on paper. The goal is to make connections visible. This also makes your findings easier to share both with classmates and in a professional context with stakeholders who may not have time to read a full report.

Affinity Maps:

• Affinity mapping is a really popular UX tool, especially for finding pain points. You put insights on sticky notes (quotes, observations, and results) from your user tests and start grouping them based on similarity.
• For example: Maybe several comments are about ‘confusing navigation’ or ‘unclear buttons. These all go together. As you cluster these pieces, you’ll start to see larger patterns or themes emerging. This is a great way to move from raw data to structured insights.

• Affinity Maps help organize qualitative data into clusters of related ideas
• Use it to:
• Sort post-it notes or quotes by shared meaning
• Identify recurring themes and patterns
• Reveal relationships between user frustrations and goals

Affinity Maps:

Read NN/g Article

Journey Mapping

We've already talked about journey maps, but you can use them to show your user’s path through a product or service, from their first touchpoint to their goal. You map what users are doing, thinking, and feeling at each step. This helps you see where frustration builds up or where delight happens. For your current project, a journey map could show how a user navigates your system during a usability test, like logging in, completing a task, or checking out. You can use these visualizations to pinpoint exactly where the experience breaks down.

• Journey Maps visualize the steps users take when completing a task.
• Use it to:
• Understand the sequence of user actions
• Identify pain points and high points
• Reveal opportunities to improve the overall experience

Read NN/g Article

Matrix Mapping

• Matrix mapping is perfect when you have a lot of findings and need to prioritize them. You can use a simple 2x2 matrix, where for example, impact on the user is placed on the x-xis, and the frequency of occurrence is placed on the y-axis. This helps you visualize which issues are both common and critical. For example, if five users struggled with navigation and it prevented them from completing their goal, that’s high impact and high frequency. It should go at the top of your priority list. This technique is especially helpful as you start deciding what to prototype next week.

• Matrix Maps compare different findings or variables to find priorities.
• Use it to:
• Rank usability issues by impact vs. frequency
• Visualize which problems matter most to fix
• Support data-driven design decisions

Read Matrix Article

Empathy Mapping

• Empathy Maps help visualize what users say, think, do, and feel.
• Use it to:
• Build deeper understanding of users’ mindsets
• Capture emotions and motivations
• Align the team around user-centered design

• Empathy maps are all about perspective. They help you capture what your users are saying, thinking, doing, and feeling during their interaction with your system. For example, a user might say, ‘I can’t find where to upload my file,’ but what they’re really feeling is frustration or anxiety about making a mistake. By separating these layers, you start to design for emotion as well as function. Empathy maps make it easier to see the human side of your data.

Read NN/g Article

Insight Clusters

• Insight Clusters connect findings to potential solutions.
• Use it to:
• Group themes into actionable design directions
• Link insights to possible “How Might We” questions
• Prepare for prototyping and iteration

• Insight clusters are the bridge between research and design. After you’ve identified your major themes, start grouping them into potential opportunity areas. For instance, you might cluster insights around ‘navigation,’ ‘trust,’ or ‘aesthetics.’ Under each cluster, list the related user quotes and possible ‘How Might We’ questions. These clusters will directly inform your next phase: creating low, medium, and high-fidelity prototype solutions. Think of them as your blueprint for where to focus your energy.

Read NN/g Article

Turning Insights into Design Opportunities

Once you’ve identified your themes and patterns, you’ll start reframing them into design challenges. A great way to do this is through ‘How Might We’ questions. This format keeps the focus on possibility. It’s open-ended, actionable, and optimistic. For example, if users struggle to find settings, your question becomes: How might we make important settings more discoverable? You’ll use these HMW questions next week when you begin prototyping possible solutions.

• Transform insights into “How Might We” (HMW) statements:
• Example Insight 1: Users forget where key settings are.

• HMW 1: How might we make important settings more discoverable?

• Insight 2: Users feel overwhelmed by options.

• HMW 2: How might we simplify the interface without losing power?

Key Takeaways

• To wrap up, remember that analysis is where the research turns into design. Your goal is to look for themes, not individual quotes. Use tools like affinity maps to connect ideas visually. Then, turn those insights into design opportunities through ‘How Might We’ questions. This week’s work will form the foundation of your prototype concepts, so take your time exploring the data deeply before jumping to solutions.

• Organize your findings into themes, not isolated comments
• Use visual tools to see connections

• Turn insights into action with “How Might We” framing

• Your analysis should bridge research and design

That's it for now!

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This video is 3 minutes long and discusses the Thematic Analysis. For a deeper dive on this subject, view the NN/g article on the previous slide.