Mastering Qualitative Data Analysis: The Step-by-Step Process & 5 Essential Methods

Mastering Qualitative Data Analysis: The Step-by-Step Process & 5 Essential Methods

Wondering how to analyze qualitative data and get actionable insights? Search no further!

This article will help you analyze qualitative data and fuel your product growth. We’ll walk you through the following steps:

  • 5 Qualitative data analysis methods.
  • 5 Steps to analysing qualitative data.
  • How to act on research findings.

Let’s get started!


  • Qualitative data analysis turns non-numerical data into insights, including customer feedback, surveys, and interviews.
  • Qualitative data provides rich insights for refining strategies and uncovering growth opportunities.
  • The benefits of qualitative data analysis include deep insight, flexibility, contextual understanding, and amplifying participant voices.
  • Challenges include data overload, reliability, and validity concerns, as well as time-intensive nature.
  • Qualitative and quantitative data analysis differ in analyzing numerical vs. non-numerical data.
  • Qualitative data methods include content analysis, narrative analysis, discourse analysis, thematic analysis, and grounded theory analysis.
  • Content analysis involves systematically analyzing text to identify patterns and themes.
  • Narrative analysis interprets stories to understand customer feelings and behaviors.
  • The thematic analysis identifies patterns and themes in data.
  • Grounded theory analysis generates hypotheses from data.
  • Choosing a method depends on research questions, data type, context, expertise, and resources.
  • The qualitative data analysis process involves defining questions, gathering data, organizing, coding, and making hypotheses.
  • Userpilot facilitates qualitative data collection through surveys and offers NPS dashboard analytics.
  • Building in-app experiences based on qualitative insights enhances user experience and drives satisfaction.
  • The iterative qualitative data analysis process aims to refine understanding of the customer base.
  • Userpilot can automate data collection and analysis, saving time and improving customer understanding. Book a demo to learn more!

Try Userpilot and Take Your Qualitative Research to the Next Level

What is a qualitative data analysis?

Qualitative data analysis is the process of turning qualitative data — information that can’t be measured numerically — into insights.

This could be anything from customer feedback, surveys, website recordings, customer reviews, or in-depth interviews.

Qualitative data is often seen as more “rich” and “human” than quantitative data, which is why product teams use it to refine customer acquisition and retention strategies and uncover product growth opportunities.

Benefits of qualitative data analysis

Here are the key advantages of qualitative data analysis that underscore its significance in research endeavors:

  • Deep Insight: Qualitative data analysis allows for a deep understanding of complex patterns and trends by uncovering underlying meanings, motivations, and perspectives.
  • Flexibility: It offers flexibility in data interpretation, allowing researchers to explore emergent themes and adapt their analysis to new insights.
  • Contextual Understanding: Qualitative analysis enables the exploration of contextual factors, providing rich context to quantitative findings and uncovering hidden dynamics.
  • Participant Voice: It amplifies the voices of participants, allowing their perspectives and experiences to shape the analysis and resulting interpretations.

Challenges of qualitative data analysis

While qualitative data analysis offers rich insights, it comes with its challenges:

  • Data Overload and Management: Qualitative data often comprises large volumes of text or multimedia, posing challenges in organizing, managing, and analyzing the data effectively.
  • Reliability and Validity: Ensuring the reliability and validity of qualitative findings can be complex, as there are fewer standardized measures compared to quantitative analysis, requiring meticulous attention to methodological rigor.
  • Time-Intensive Nature: Qualitative data analysis can be time-consuming, involving iterative processes of coding, categorizing, and synthesizing data, which may prolong the research timeline and increase resource requirements.

Quantitative data analysis vs. Qualitative data analysis

Here let’s understand the difference between qualitative and quantitative data analysis.

Quantitative data analysis is analyzing numerical data to locate patterns and trends. Quantitative research uses numbers and statistics to systematically measure variables and test hypotheses.

Qualitative data analysis, on the other hand, is the process of analyzing non-numerical, textual data to derive actionable insights from it. This data type is often more “open-ended” and can be harder to conclude from.

However, qualitative data can provide insights that quantitative data cannot. For example, qualitative data can help you understand how customers feel about your product, their unmet needs, and what motivates them.

Other differences include:

What are the 5 qualitative data analysis methods?

There are 5 main methods of qualitative data analysis. Which one you choose will depend on the type of data you collect, your preferences, and your research goals.

Content analysis

Content analysis is a qualitative data analysis method that systematically analyses a text to identify specific features or patterns. This could be anything from a customer interview transcript to survey responses, social media posts, or customer success calls.

The data is first coded, which means assigning it labels or categories.

For example, if you were looking at customer feedback, you might code all mentions of “price” as “P,” all mentions of “quality” as “Q,” and so on. Once manual coding is done, start looking for patterns and trends in the codes.

Content analysis is a prevalent qualitative data analysis method, as it is relatively quick and easy to do and can be done by anyone with a good understanding of the data.

The advantages of content analysis process

  • Rich insights: Content analysis can provide rich, in-depth insights into how customers feel about your product, what their unmet needs are, and their motives.
  • Easily replicable: Once you have developed a coding system, content analysis is relatively quick and easy because it’s a systematic process.
  • Affordable: Content analysis requires very little investment since all you need is a good understanding of the data, and it doesn’t require any special software.

The disadvantages of content analysis process

  • Time-consuming: Coding the data is time-consuming, particularly if you have a large amount of data to analyze.
  • Ignores context: Content analysis can ignore the context in which the data was collected which may lead to misinterpretations.
  • Reductive approach: Some people argue that content analysis is a reductive approach to qualitative data because it involves breaking the data down into smaller pieces.

Narrative analysis

Analysing qualitative data with narrative analysis involves identifying, analyzing, and interpreting customer or research participants’ stories. The input can be in the form of customer interviews, testimonials, or other text data.

Narrative analysis helps product managers to understand customers’ feelings toward the product identify trends in customer behavior and personalize their in-app experiences.

The advantages of narrative analysis

  • Provide a rich form of data: The stories people tell give a deep understanding of customers’ needs and pain points.
  • Collects unique, in-depth data based on customer interviews or testimonials.

The disadvantages of narrative analysis

  • Hard to implement in studies of large numbers.
  • Time-consuming: Transcribing customer interviews or testimonials is labor-intensive.
  • Hard to reproduce since it relies on unique customer stories.

Discourse analysis

Discourse analysis is about understanding how people communicate with each other. It can be used to analyse written or spoken language. For instance, product teams can use discourse analysis to understand how customers talk about their products on the web.

The advantages of discourse analysis

  • Uncovers motivation behind customers’ words.
  • Gives insights into customer data.

The disadvantages of disclosure analysis

  • Takes a large amount of time and effort as the process is highly specialized and requires training and practice. There’s no “right” way to do it.
  • Focuses solely on language.

Thematic analysis

Thematic analysis is a popular qualitative data analysis method that identifies patterns and themes in data. The process of thematic analysis involves coding the data, which means assigning it labels or categories.

It can be paired with sentiment analysis to determine whether a piece of writing is positive, negative, or neutral. This can be done using a lexicon (i.e., a list of words and their associated sentiment scores).

A common use case for thematic analysis in SaaS companies is customer feedback analysis with NPS surveys and NPS tagging to identify patterns among your customer base.

The advantages of thematic analysis

  • Doesn’t require training: Anyone with little training on how to label the data can perform thematic analysis.
  • It’s easy to draw important information from raw data: Surveys or customer interviews can be easily converted into insights and quantitative data with the help of labeling.
  • An effective way to process large amounts of data if done automatically: you will need AI tools for this.

The disadvantages of thematic analysis

  • Doesn’t capture complex narratives: If the data isn’t coded correctly, it can be difficult to identify themes since it’s a phrase-based method.
  • Difficult to implement from scratch because a perfect approach must be able to merge and organize themes in a meaningful way, producing a set of themes that are not too generic and not too large.

Grounded theory analysis

Grounded theory analysis is a method that involves the constant comparative method, meaning qualitative researchers analyze and code the data on the fly.

The grounded theory approach is useful for product managers who want to understand how customers interact with their products. It can also be used to generate hypotheses about how customers will behave in the future.

Suppose product teams want to understand the reasons behind the high churn rate, they can use customer surveys and grounded theory to analyze responses and develop hypotheses about why users churn and how to reengage inactive ones.

You can filter the disengaged/inactive user segment to make analysis easier.

The advantages of ground theory analysis

  • Based on actual data, qualitative analysis is more accurate than other methods that rely on assumptions.
  • Analyse poorly researched topics by generating hypotheses.
  • Reduces the bias in interpreting qualitative data as it’s analyzed and coded as it’s collected.

The disadvantages of ground theory analysis

  • Overly theoretical
  • Requires a lot of objectivity, creativity, and critical thinking

Which qualitative data analysis method should you choose?

We have covered different qualitative data analysis techniques with their pros and cons but choosing the appropriate qualitative data analysis method depends on various factors, including:

  • Research Question: Different qualitative methods are suitable for different research questions.
  • Nature of Data: Consider the type of data you have collected—interview transcripts, reviews, or survey responses—and choose a method that aligns with the data’s characteristics. For instance, thematic analysis is versatile and can be applied to various types of qualitative data, while narrative analysis focuses specifically on stories and narratives.
  • Research Context: Take into account the broader context of your research. Some qualitative methods may be more prevalent or accepted in certain fields or contexts.
  • Researcher Expertise: Consider your own skills and expertise in qualitative analysis techniques. Some methods may require specialized training or familiarity with specific software tools. Choose a method that you feel comfortable with and confident in applying effectively.
  • Research Goals and Resources: Evaluate your research goals, timeline, and resources available for analysis. Some methods may be more time-consuming or resource-intensive than others. Consider the balance between the depth of analysis and practical constraints.

How to perform qualitative data analysis process in steps

With all that theory above, we’ve decided to elicit the essential steps of qualitative research methods and designed a super simple guide for gathering qualitative data.

Let’s dive in!

Step 1: Define your qualitative research questions

The qualitative analysis research process starts with defining your research questions. It’s important to be as specific as possible, as this will guide the way you choose to collect qualitative research data and the rest of your analysis.

Examples are:

  • What are the primary reasons customers are dissatisfied with our product?
  • How does X group of users feel about our new feature?
  • What are our customers’ needs, and how do they vary by segment?
  • How do our products fit into our customers’ lives?
  • What factors influence the low feature usage rate of the new feature?

Step 2: Gather your qualitative customer data

Now, you decide what type of data collection to use based on previously defined goals. Here are 5 methods to collect qualitative data for product companies:


  • Review sites

  • User interviews
  • Focus groups

We recommend using a mix of in-app surveys and in-person interviews. The former helps to collect rich data automatically and on an ongoing basis. You can collect user feedback through in-product surveys, NPS platforms, or use Zoom for live interviews.

The latter enables you to understand the customer experience in the business context as you can ask clarifying questions during the interviews.

Try Userpilot and Easily Collect Qualitative Customer Data

Step 3: Organize and categorize collected data

Before analyzing customer feedback and assigning any value, unstructured feedback data needs to be organized in a single place. This will help you detect patterns and similar themes more easily.

One way to do this is to create a spreadsheet with all the data organized by research questions. Then, arrange the data by theme or category within each research question.

You can also organize NPS responses with Userpilot. This will allow you to quickly calculate scores and see how many promoters, passives, and detractors there are for each research question.


Step 4: Use qualitative data coding to identify themes and patterns

Themes are the building blocks of analysis and help you understand how your data fits together.

For product teams, an NPS survey might reveal the following themes: product defect, pricing, and customer service. Thus, the main themes in SaaS will be around identifying friction points, usability issues, UI issues, UX issues, missing features, etc.

You need to define specific themes and then identify how often they occur. In turn, the pattern is a relationship between 2 or multiple elements (e.g. users who have specific JTBD complain of a specific missing feature).

You can detect those patterns from survey analytics.


Pair themes with in-app customer behavior and product usage data to understand whether different user segments fall under specific feedback themes.

Following this step, you will get enough data to improve customer loyalty.

Step 5: Make hypotheses and test them

The last step in qualitative research is to analyze the data collected to find insights. Segment your users based on in-app behavior, user type, company size, or job to be done to draw meaningful decisions.

For instance, you may notice that negative feedback stems from the customer segment that recently engaged with XYZ features. Just like that, you can pinpoint friction points and the strongest sides of your product to capitalize on.

How to perform qualitative data analysis with Userpilot

Userpilot is a product growth platform that helps product managers collect and analyze qualitative data. It offers a suite of features to make it easy to understand how users interact with your product, their needs, and how you can improve user experience.

When it comes to performing qualitative research, Userpilot is not a qualitative data analysis software but it has some very useful features you could use.

Collect qualitative feedback from users with in-app surveys

Userpilot facilitates the collection of qualitative feedback from users through in-app surveys.

These surveys can be strategically placed within your application to gather insights directly from users while they interact with your product.

By leveraging Userpilot’s in-app survey feature, you can gather valuable feedback on user experiences, preferences, pain points, and suggestions for improvement.

Benefit from NPS dashboard and survey analytics

With Userpilot, you can harness the power of the NPS (Net Promoter Score) dashboard and survey analytics to gain valuable insights into user sentiment and satisfaction levels.

The NPS dashboard provides a comprehensive overview of your NPS scores over time, allowing you to track changes and trends in user loyalty and advocacy.

Additionally, Userpilot’s survey analytics offer detailed insights into survey responses, enabling you to identify common themes, uncover actionable feedback, and prioritize areas for improvement.

Build different in-app experiences based on the insights from qualitative data analysis

By analyzing qualitative feedback collected through in-app surveys, you can segment users based on these insights and create targeted in-app experiences designed to address specific user concerns or enhance key workflows.

Whether it’s guiding users through new features, addressing common user challenges, or personalizing the user journey based on individual preferences, Userpilot empowers you to deliver a more engaging and personalized user experience that drives user satisfaction and product adoption.


The qualitative data analysis process is iterative and should be revisited as new data is collected. The goal is to constantly refine your understanding of your customer base and how they interact with your product.

Want to get started with qualitative analysis? Get a Userpilot Demo and automate the data collection process. Save time on mundane work and understand your customers better!

Try Userpilot and Take Your Qualitative Data Analysis to the Next Level

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