Learn / Guides / Qualitative data analysis guide
How to use qualitative data analysis to understand customer behavior in 5 steps
What and how people feel is part of any experience—and the more emotionally connected people feel to your product, the more likely they are to buy it, use it, and recommend it to others.
But how can you measure users' emotional connection with your brand and product? That's where qualitative data analysis comes in.
Qualitative data analysis involves organizing and assessing voice-of-the-customer (VoC) feedback, ideas, opinions, values, and behaviors to better understand the user experience (UX) and discover people's emotional connections to your brand and product.
Through it, you can learn what they want from your product—and how and when they'll use it—to deliver a delightful user experience.
In this guide, we’ll decode qualitative analysis and give you a step-by-step process to follow, so you can approach your qualitative data analysis with confidence and competence.
How qualitative data analysis helps you understand users
Qualitative data analysis (QDA) is the process of analyzing and interpreting qualitative data collected through focus groups, interviews with open-ended questions, personal observations, and secondary research data stored in audio, video, text, and other formats.
QDA is based on an interpretative philosophy, where you analyze data at both the descriptive (surface) and interpretive (deeper) levels to tell a coherent story by connecting and establishing a relationship between data points based on themes and trends.
The data here is typically text-based, descriptive, and unstructured. Analyzing it helps you understand your customer’s mindset and behavior, which ultimately helps teams develop better products.
How to collect qualitative customer data
Before analyzing qualitative data, you need to collect the necessary data to get insights into the feelings and meaning behind customer behavior. The good news is there are multiple ways to go about this. For example, You can collect qualitative data by:
Observing user behavior
Creating user focus groups
Distributing customer surveys
And that's not it: you can use various QDA tools to simplify data collection. For example, survey tools and feedback widgets give your customers the option to freely express their thoughts, and tools like session recording software help you better understand how users interact with your website. (Keep reading: we'll discuss qualitative data collection in more detail later in the article.)
Benefits of qualitative data analysis
We agree. ✋
One of the greatest benefits of QDA is being able to tap into what motivates a particular user behavior—for example, why someone abandons their cart, misses a step in their product onboarding, or doesn't renew their subscription.
Other benefits of qualitative data analysis are:
More insightful answers: instead of asking users questions with specific ‘yes' or 'no’ answers, qualitative research lets them freely express their thoughts and views without any pre-set constraints. They can take their time to think and carefully address the questions before answering. Plus, as this information is based on their personal thoughts, ideas, and past experiences, you’re more likely to receive authentic answers.
Greater flexibility: QDA focuses on gaining as many insights as possible from a relatively small sample size. This makes it more flexible than quantitative research analysis, as it allows greater spontaneity. Qualitative methods mostly ask open-ended questions that aren't exactly worded in the same way with each customer, so you can adapt the interaction as you see fit to get insights.
As a result of these benefits, the analysis process unfolds more naturally, providing rich, contextual data to better inform your product direction and messaging.
Challenges of qualitative data analysis
Since qualitative data can be subjective and is collected from sources like customer surveys and 1:1 interviews, you can face challenges like limited sample size and observation biases that may limit the usefulness of qualitative data.
Some other, more specific challenges of QDA are:
Sample-related issues: limited sample size is a key challenge of qualitative data, and performing extensive qualitative research with hundreds of participants might be out of the question due to high costs. Also, participating in a research study is a choice—some users may simply choose not to respond to your questions.
Observation bias: the insights you gather from analyzing qualitative data are open to misinterpretation and observer bias, which can influence results. For example, users may change their behavior or performance when being observed (also known as the Hawthorne effect). In fact, you can also unconsciously influence your participants with your beliefs and expectations (known as the observer-expectancy effect).
Unfortunately, these unavoidable challenges mean your qualitative sample will never have a representative overview of all the different people visiting your website or interacting with your brand, which is important to remember when interpreting test results.
How is qualitative data analysis different from quantitative data analysis?
So, what’s the qualitative vs. quantitative user research debate all about? We're so glad you asked. 😉
It's not 'qualitative or quantitative data', it's 'qualitative and quantitative data'—you need them both to fully understand your users and provide a better customer experience. Here's why:
Qualitative data answers why and how and leverages customers' opinions, motivations, and observations to help you explore more in-depth ideas about improving the user or customer experience, messaging, and branding.
On the other hand, quantitative data answers what, where, and when, and gives you hard facts about user behavior based on measurable, numerical data points.
For example, you can use quantitative tools like Google Analytics with Hotjar Heatmaps to see how many users have visited your website, the percentage that converted into leads or sales, and how many scrolled below the fold of your product page (all quantitative data).
But you’ll need to use qualitative tools and methods like surveys, feedback widgets, and interviews to understand why some visitors converted and some didn't, to learn about their common pain points, and which product features they find most useful (all qualitative data).
In experimentation, qualitative data is most useful as a guardrail… and as a hypothesis generator. … I use it to help me diagnose problems with the UX as well as generate potential solutions. From there, I run an experiment (which is where the quantitative data comes in).
A 5-step guide to an effective qualitative data analysis process
Here's a step-by-step guide to help you analyze qualitative data.
1. Select a qualitative data research method
Each method of qualitative data analysis has a unique angle and impact on the way you'll organize and understand your results, so choose the method that best suits your team, resources, and objectives. Some methods of qualitative research are:
Grounded theory analysis
How do you choose the appropriate method? Use a customer-first approach: consider what you're trying to learn about your customers, why you want to learn it, and how you'll use the insights you uncover, then choose the type of method that will help you reach your goals.
For example, if you're analyzing data to develop theories 'grounded' in quantitative data, you might choose grounded theory analysis. But if you want to explore people's lived narratives related to a phenomenon or event, using narrative analysis methods would be a better fit.
Note: learn more about the different methods of qualitative data analysis in the QDA methods chapter of this guide.
2. Collect and organize your qualitative user data
The next step is to collect your qualitative data and get it ready for analysis. Here are some tools and methods you can use:
Observations: observe and monitor how users behave in their natural setting—for example, with the help of Hotjar's Observe tools—and make detailed notes and highlights about user behavior trends.
Interviews: hold one-on-one informal and unstructured interviews with users and ask open-ended questions about the reasons, opinions, and motivations behind their choices and behavior. (E.g. how do you think this product could help you? Can you describe how you would use this product?) An important tip for mastering user interviews: give space. Take long, deliberate pauses to let the person know you're interested in hearing more about their experience and opinions.
Surveys: distribute on- or off-site polls and questionnaires to collect open- or closed-ended responses from website visitors. Hotjar’s Ask tools help you collect diverse and actionable qualitative feedback in an organized and effective way.
Focus groups: create a group discussion to ask questions that will lead to productive conversations. Focus groups can be used as part of regular usability testing to learn more about users' opinions of your site, but not about how they use it.
Once you've collected your qualitative data, organize it for consistency—for example, by type, format, or theme. This also means transcribing any non-written documents to make them easier to analyze later. Some tools and methods you can use to connect and organize your data are:
Uploading it into a research repository to unify it with other data
Using a transcription service like Rev
By the end of this second step, you should have collected your data, transcribed it into text, and made it easily accessible in one place. You can then analyze the data consistently, making it easier to summarize and uncover valuable insights.
3. Code your qualitative data
In terms of qualitative data analysis, coding involves labeling and organizing your data by writing 2–5 word 'codes' to summarize paragraphs of text in a way that lets you identify themes—and relationships between themes.
To do this manually, take small samples of your qualitative data, and assign a set of codes or categories. Then label each piece of feedback systematically to identify patterns and meaning.
For (a very basic) example, if you were looking for different types of animals to appear in your data, your initial codes could be 'dogs', 'lizards', and 'ants'. In the process of labeling, you could also label or categorize 'dogs' as 'mammals', 'lizards' as 'reptiles', and 'butterflies' as 'insects'.
Creating these code categories organizes and enriches your data, allowing you to make new connections between different groups of codes. If you want to take a larger sample of data, be sure to revise and refine your codes for accuracy and consistency.
If manual coding doesn't sound like fun, you can also use an automated coding solution to simplify the process—try using a thematic analysis software like MAXQDA or NVivo to save yourself some time. These tools will give you a broad feel for what themes appear in your qualitative data, and will help you go deeper into the material for in-depth analysis.
Whether you code your data manually or use a tool to help, don’t forget to validate your data while you're at it: check the accuracy of your research and consider the reliability of each method to produce accurate and consistent data. This ensures your data isn’t flawed, which will eventually give you more meaningful insights.
Note: learn about some of the qualitative data analysis tools you can use in the QDA tools chapter of this guide—stay tuned!
4. Analyze your data and develop hypotheses
Now it’s time to analyze your qualitative data to find meaningful insights (also known as 'the fun part').
With your data organized and coded, thoroughly review what you've collected to identify recurring themes, phrases, and words commonly used by your website visitors, as well as any unusual or unexpected expressions. Also, note any topics you did expect to come up during the analysis, but didn't.
Next, use the insights from your data to make conclusions and develop hypotheses about your next steps. Think of it as summing up the learnings from the research to validate your next steps.
For example, if customers say your product isn't user-friendly, highlight the specific challenges and blockers they face in their user journey to identify which product features or experiences should be improved, removed, or added to improve UX.
In another example, you might find that an unoptimized website is a leading factor behind customer churn. Use what you learn from your qualitative data research to propose and prioritize optimizations—perhaps you need to update your website navigation to improve UI, or optimize your web design for a mobile experience.
The main idea is to define your learnings, and formulate next steps.
5. Share your learnings and put them into action
The last step of qualitative data analysis is reporting on your data—to tell the user's and customer's story—and sharing your findings and ideas with stakeholders.
Outline key suggestions and next steps, and decide on a meaningful course of action based on your analysis. You can create a proposed roadmap using tools like Miro or Trello, or put together a traditional report. Data visualization tools and software like Tableau and Microsoft Power BI also help facilitate cross-functional collaboration and communication by making it easy to share data and align your next steps with user-centric business goals.
Get rich, in-depth insights with qualitative data analysis
Qualitative data analysis helps you make sense of customer behavior—beyond the numbers and quantitative metrics—and helps teams uncover valuable insights to better understand user behavior and build better products.