{"id":291553,"date":"2025-08-19T17:31:46","date_gmt":"2025-08-19T17:31:46","guid":{"rendered":"https:\/\/userpilot.com\/blog\/qualitative-data-examples\/"},"modified":"2026-03-20T08:37:09","modified_gmt":"2026-03-20T08:37:09","slug":"qualitative-data-examples","status":"publish","type":"post","link":"https:\/\/userpilot.com\/blog\/qualitative-data-examples\/","title":{"rendered":"Qualitative Data Examples and How to Find Them"},"content":{"rendered":"

\u200bAs a UX researcher, I spend my days trying to understand people. What makes them tick? Why do they do what they do within our product? While numbers tell us what is happening, like how many users signed up or clicked a button, they rarely tell us why.<\/p>\n

Qualitative data fills<\/a> in those blanks, providing rich, descriptive information that helps explain the reasons behind user behavior. But without clear examples, qualitative data can feel unstructured and difficult to interpret. And it\u2019s not always obvious what \u201cgood\u201d data looks like or how to gather it.<\/p>\n

This article will help with that by exploring qualitative data examples, collection methods, and ways to perform qualitative data analytics<\/a>.<\/p>\n

What qualitative data really is<\/h2>\n

Qualitative data, also called categorical data, is simply non-numeric data. It describes qualities or characteristics. It\u2019s about the “what it looks like,” “how it feels,” and “why someone chose to act that way.”<\/p>\n

For example, when we’re trying to figure out why a user might be struggling with a new feature, a dashboard showing a high feature drop-off rate<\/a> tells us there\u2019s a problem. That\u2019s a quantitative signal. However, qualitative data is what you gather to uncover what the problem is and why it\u2019s happening.<\/p>\n

Things like user interview notes, open-ended survey responses, session replay recordings<\/a>, or usability testing<\/a> observations. These sources of qualitative data examples don\u2019t just confirm there\u2019s a problem. They show you what the user experienced and why they abandoned the feature.<\/p>\n

What is the difference between qualitative and quantitative data?<\/h3>\n

Quantitative data focuses<\/a> on measurable numbers, such as the percentage of users who drop off at a certain step.<\/p>\n

\"Track<\/picture>
Track and visualize metrics with Userpilot<\/a>\u2019s custom analytics dashboard.<\/figcaption><\/figure>\n

In contrast, qualitative data provides insights into the subjective experiences and reasons behind those numbers. For example, feedback from a usability test<\/a> might reveal that the button label in the menu was unclear, leading to drop-offs.<\/p>\n

Qualitative data examples for SaaS product and UX research<\/h2>\n

Qualitative data, also referred to as categorical data at times, encompasses more than just user interviews<\/a>. In SaaS and UX research, it can be anything that captures the story, context, or emotion behind a user\u2019s actions. Here are some examples of qualitative data to guide your research and product decisions.<\/p>\n

Open-ended survey responses<\/h3>\n

Open-ended surveys invite users to provide in-depth feedback. Instead of limiting their answers to preset choices like closed-ended questions<\/a> do, these surveys create space for users to explain challenges or preferences in their own words.<\/p>\n

For example, say a recently launched dashboard feature is seeing low usage. You then decide to create an in-app survey with Userpilot<\/a>, asking \u201cWhat would make this dashboard more useful for you?\u201d. This might reveal useful ideas for future improvements, such as adding specific metrics, improving load times, or making data easier to filter.<\/p>\n

\"Create<\/picture>
Create custom in-app surveys for<\/a> feedback collection in Userpilot<\/a>.<\/figcaption><\/figure>\n

Interview transcripts<\/h3>\n

These are verbatim written records of conversations you\u2019ve had with users. Transcripts preserve the user’s exact wording and tone, making it easier to spot patterns, pain points<\/a>, and subtle frustrations during data analysis.<\/p>\n

For example, interview transcripts might come in handy when you\u2019re exploring why power users<\/a> haven\u2019t tried a newly launched feature. Reading the transcript could reveal they didn\u2019t even know it existed because it was buried in a secondary menu.<\/p>\n

Case studies<\/h3>\n

Case studies are in-depth accounts of how a specific customer uses your product over time. They usually explore a single challenge in detail, documenting the context, steps taken, obstacles faced, and measurable results.<\/p>\n

Because they\u2019re so thorough, case studies produce large bodies of non-numerical data that can help better shape product strategy<\/a>.<\/p>\n

For example, you might learn so much about your users during the case study interview. It can be how they use your product to achieve a specific goal or any workarounds they come up with. Such insights often help me work on product updates and<\/a> feature improvements.<\/p>\n

\"Explore<\/picture>
Explore Userpilot<\/a>\u2019s case studies for real-world qualitative data examples.<\/figcaption><\/figure>\n

Experience descriptions<\/h3>\n

These are personal narratives where users describe what happened and how they felt during a product interaction<\/a>. They can also include observations about the product’s characteristics, such as quality, ease of use, and performance.<\/p>\n

For instance, a tester might describe feeling \u201clost\u201d in your statistical analysis dashboard because metrics weren\u2019t labeled in plain language, forcing them to guess what each number meant.<\/p>\n

Categorical data<\/h3>\n

Categorical data, also known as nominal data, is qualitative information that\u2019s grouped into labels or categories instead of numerical values. Using this data makes it easier to spot trends at scale and track how often a theme appears with a particular frequency.<\/p>\n

For example, categorizing customer support<\/a> tickets under labels like \u201cbilling issue,\u201d \u201cfeature request,\u201d and \u201cbug\u201d to see which category is most common. Even though you can count how many responses fall under each label, the data itself is qualitative. It represents a category, not a measurable amount.<\/p>\n

A subtype of categorical data is ordinal data, which arranges these categories into a meaningful order, while remaining qualitative. For example, rating customer satisfaction levels<\/a> from \u201cvery dissatisfied\u201d to \u201cvery satisfied.\u201d<\/p>\n

Observation notes<\/h3>\n

Qualitative observations are written records taken by a researcher while watching someone use the product. They often capture visible actions along with any inferred reasoning. Later, these notes are often turned into labeled data so recurring behaviors can be quickly identified and categorized.<\/p>\n

Notes may also include contextual factors, such as environment, device type, or distractions that might influence behavior.<\/p>\n

For instance, I observe that a user hovered over several icons before choosing one. Based on this, I might suggest in my notes that the icons aren’t intuitive enough.<\/p>\n

Videos and recordings<\/h3>\n

These are screen, audio, or video captures that show how users interact with your product in real time. They let you replay sessions to catch subtle usability issues<\/a> you might miss in the moment.<\/p>\n

For example, a screen recording shows a user repeatedly opening the wrong settings menu when trying to change their password. This points to poor menu labeling or an unclear information hierarchy.<\/p>\n

Community posts or product reviews<\/h3>\n

These qualitative data examples include user-generated feedback shared in public spaces, such as forums, review platforms, or app stores. They\u2019re often candid opinions that can highlight issues or feature requests<\/a> we might not hear through formal channels.<\/p>\n

For example, a community forum might have a thread where multiple users share workarounds for a missing integration. Reviewing these posts could reveal how critical the feature is and how current gaps are impacting adoption.<\/p>\n

\"Product<\/picture>
Monitor feedback on review sites for unique insights into user needs.<\/figcaption><\/figure>\n

Qualitative data collection methods<\/h2>\n

In my role, I work with various forms of qualitative data daily. Each type offers a unique lens into our users’ world, helping us make informed decisions about our product experience<\/a>.<\/p>\n

\"Qualitative<\/picture>
Examples of qualitative data collection methods via Userpilot<\/a>.<\/figcaption><\/figure>\n

User interviews and focus groups<\/h3>\n

There\u2019s no substitute for direct conversation. User interviews<\/a> and focus groups are goldmines for qualitative data. I get to sit down with users, ask open-ended questions<\/a>, and let them speak freely about their experiences. This helps us understand their mental models, pain points, and desires in their own words.<\/p>\n

For example, when we’re exploring new features, I might ask a user to describe their current workflow and what challenges they face. Their detailed responses give us direct insight into unmet needs, which is invaluable for product ideation<\/a> and feature prioritization<\/a>. We also use these sessions to identify customer pain points<\/a> that quantitative data might only hint at.<\/p>\n

But there’s a catch. Good qualitative data analysis<\/a> requires good data, which requires recruiting the right participants.<\/p>\n

I use Userpilot<\/a> to make this easier. By triggering in-app surveys to targeted segments, I\u2019ve been able to find ideal users for interviews and beta testing much faster.<\/p>\n

If you’re curious about how I did it, check out my detailed process at “How UX Researchers 4X Usability Test Response Rates With Userpilot<\/a>.”<\/p>\n

\"Segment<\/picture>
Segment who you\u2019re sending interview and focus group invites to with Userpilot<\/a>.<\/figcaption><\/figure>\n

Open-ended survey responses<\/h3>\n

While surveys often collect numerical data, the open-ended questions provide rich qualitative feedback. After a user finishes an onboarding flow, for instance, we might ask, “What was most confusing about getting started?” or “What could have made this experience smoother?” These are critical product onboarding feedback questions<\/a>.<\/p>\n

We use Userpilot<\/a> to launch these in-app surveys<\/a> at critical points in the user journey. Suppose the analysis process shows a new user hasn\u2019t tried a key feature in their first week. So we trigger a survey asking what\u2019s holding them back.<\/p>\n

We can also target these surveys by user roles or plan type, like showing a different question to admins vs. end users. This way, we gather qualitative data that is relevant for that specific segment<\/a>.<\/p>\n

The qualitative responses help us understand why users might be giving a low NPS score, for instance. Our platform allows us to create these surveys<\/a> easily, and then we analyze the text-based feedback to find common themes and improve the experience.<\/p>\n

\"Perform<\/picture>
Perform qualitative data analysis on NPS responses with Userpilot<\/a>.<\/figcaption><\/figure>\n

I\u2019ve seen the same approach work for others, too. For example, Unolo used Userpilot\u2019s in-app NPS surveys<\/a> to collect feedback directly inside their product. They targeted customers with lower scores and responded quickly to any negative feedback, helping reduce churn by 0.5% to 1%.<\/p>\n

As Unolo’s product manager puts it, “The NPS feature gave us feedback almost instantly. This helped us get in touch with customers more quickly and understand their concerns with the product, which, in turn, helped us reduce our churn rate.\u201d<\/p>\n

If you\u2019re curious about how we handle survey data, you can explore our approach to analyzing open-ended questions<\/a>.<\/p>\n

Observations via session replays<\/h3>\n

Sometimes, users can’t articulate their struggles, or they might even tell us one thing while their actions show another. That’s where session recordings<\/a> come in. Watching actual user sessions allows me to observe their natural behavior in our product. I can see where they hesitate, make rage clicks<\/a>, or get stuck.<\/p>\n

\"Study<\/picture>
Study user actions to uncover friction points with Userpilot<\/a>\u2019s session replays.<\/figcaption><\/figure>\n

As a UX researcher, I use session replay<\/a> to identify friction points that might not appear in survey responses or quantitative research. Userpilot’s session replay feature<\/a> lets me jump to specific events in a user’s journey, making it easier to pinpoint exactly where they experienced confusion or frustration. This visual data provides invaluable context for the “why” behind user actions.<\/p>\n

\"Collect<\/picture>
Collect data on user behaviors with Userpilot<\/a>‘s session replays.<\/figcaption><\/figure>\n

Customer feedback and support interactions<\/h3>\n

Every support ticket<\/a>, every chat with a customer success manager, every piece of direct feedback is a form of qualitative data. When users reach out for help, they’re often describing a problem in their own words, which reveals the nuances of their experience. We use tools to aggregate this feedback.<\/p>\n

Our in-app resource center<\/a> is a great example; it lets users search for answers and, if they can’t find them, connect with support. These interactions are constantly informing our understanding of what users need. Our customer success team often pulls these insights to help address issues proactively and reduce customer support load<\/a>.<\/p>\n

You can also use Userpilot’s<\/a> search term analytics to track what users are looking for in your help center<\/a>. Analyzing these search terms lets you identify missing topics users look for but can’t find, highlighting content gaps for you to improve.<\/p>\n

\"Identify<\/picture>
Identify content gaps in your help center with Userpilot<\/a>‘s search analytics.<\/figcaption><\/figure>\n

Diary studies and user journals<\/h3>\n

For longer-term insights into user habits and evolving needs<\/a>, diary studies or user journals are fantastic qualitative methods. Participants log their experiences over a period, giving us a chronological, personal account. These could be daily reflections on using a new feature or weekly updates on how our product fits into their broader workflow.<\/p>\n

I keep it simple, using tools like Google Docs and shared spreadsheets to collect these entries. But you can also use dedicated research platforms such as SurveyMonkey, dScout, or Recollective.<\/p>\n

Once you\u2019ve gathered enough entries, look for patterns<\/a> by grouping similar themes. Or run a thematic analysis to check if user actions and motivations match an existing hypothesis that supports your research objectives. Also, use in-app prompts and push notifications<\/a> to remind participants to regularly submit updates, helping maintain consistent feedback.<\/p>\n

How to conduct qualitative data analysis<\/h2>\n

Qualitative and quantitative research work best when used together. However, the tricky part is knowing how to combine the two. For example, suppose our New Users Activation Dashboard<\/a> shows the exact conversion rate from sign-up to a key “Aha!<\/a>“<\/a><\/u> Moment.<\/a> But without qualitative data, we wouldn’t know why some users convert while others drop off.<\/p>\n

I use Userpilot’s<\/a> analytics engine to connect these dots. For instance, if our funnel analysis<\/a> shows a drop-off at a specific step, I can review session recordings or<\/a> survey responses from those users to understand their struggles. This helps link specific behaviors with the reasons behind them.<\/p>\n

Once I have that context, I start qualitative data coding. This is where statistical and qualitative data methods, like categorizing and frequency counting, help quantify and group together recurring themes.<\/p>\n

Userpilot<\/a> makes this easier by capturing feedback from multiple sources, like NPS surveys<\/a> or in-app forms, and storing it alongside product usage data<\/a>. With all the data in one place, I can link recurring themes with user behavior much quickly, and even create statistical models to forecast user churn or adoption.<\/p>\n

From insights to action: Making product improvements<\/h2>\n

\u200bFor me, qualitative data is about making them make sense. The interviews, surveys, session replays, and observation notes I\u2019ve shared here are the tools I use to spot the real friction points, validate what users need, and decide what to fix first.<\/p>\n

When I connect these insights with our quantitative data, I can prioritize changes that move adoption and retention. That\u2019s the difference between \u201ccollecting feedback\u201d and building a product people stick with.<\/p>\n

If you want to get there faster, book a Userpilot demo<\/a> and I\u2019ll show you how!<\/p>\n","protected":false},"excerpt":{"rendered":"

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