To create products and experiences that customers love, firms must develop user empathy. So how do they get into the minds of users?
Qualitative Data Analysis.
Without it, companies have a half-baked understanding of their target audience. Qualitative studies unearth user needs and motivations. Gaining insight into user decision-making helps companies design better products and services.
Read on for a thorough examination of data analysis in qualitative research. Learn about the different types and how to conduct qualitative analysis. We delve into the benefits and challenges associated with it. Finally, we’ll share our favorite tools for conducting thorough qualitative analysis.
[Did someone say “favorite tools”? Wouldn’t you know, Marvin tops our list! Find out why by requesting a completely customized demo with our experts.]

What is Qualitative Data Analysis?
Qualitative data analysis is the collection, organization and interpretation of non-numerical data. This data comes in many forms — including text, audio, video and images.
Researchers collect qualitative feedback by conducting interviews, focus groups, surveys, and questionnaires. Before beginning analysis, they must structure and organize data to make sense of it.
Once data preparation is complete, researchers unearth user insights from open-ended questions. They analyze qualitative data, identifying patterns and trends that inform business decision-making.
Qualitative vs. Quantitative Data Analysis
Quantitative analysis uses numerical data to describe customer behavior. This type of analysis tells you what users are up to. Monitor website traffic, user clicks and engagement to understand how they interact with a product.
What element do users gravitate toward? How many visitors make a purchase? How many are dropping off?
Answer these questions (and more) with quantitative data. Efficient, inexpensive, and scalable, quantitative data is easy to collect and analyze.
However, quantitative data only tells us half the story.
That’s where Qualitative Data Analysis comes in. Qualitative data expressed in terms of language is subjective and unstructured. Thus, it requires thorough analysis to categorize information into themes or insights. It helps us understand why users took these actions.
Why do they click a specific button? What’s their motivation behind it? What aspects of the product experience confuse or frustrate them?
Analyzing quantitative data describes user behavior. Qualitative data analysis helps researchers discern the underlying reasons behind user behavior. Both methods complement each other, providing a well-rounded understanding of the user experience.

Benefits of Analyzing Qualitative Data
Analyzing qualitative data helps businesses make improvements that keep customers engaged and satisfied. Isn’t that the primary objective of research?
Here are some other benefits that it brings to the table:
- Context. Stay informed about factors causing changing market trends and customer attitudes. Qualitative studies aren’t bound by the limitations of quantitative methods. They explain context – something that numbers aren’t able to capture.
- Depth. Get into the weeds of people’s thoughts, feelings, and emotions. Understand different perspectives and explore complex phenomena. Unearth rich, in-depth insights into diverse user needs and experiences.
- Discovery. Qualitative research data analysis lends itself to uncovering new insights. Researchers explore data for fresh themes and understand fresh areas for inquiry. Gain perspective into user needs and make new connections with data.
- Better Products. Develop user empathy to deliver products that match what people want. Analysis of qualitative data helps companies understand user motivations, frustrations, and pain points. Incorporate feedback into products to offer experiences that delight users.
Looking for a tool that lets you dive deeper into analysis? Marvin is a centralized research repository that houses all your data. Even more important, Marvin was designed to make qualitative analysis super-simple and automatically uncover patterns and insights you’d otherwise miss.
Learn why Marvin is the perfect AI research assistant for qualitative data analysis.

Types of Qualitative Data Analysis
Qualitative data analysis is an umbrella term. Various methods offer distinct ways to dissect your data.
Types include:
Thematic Analysis
A popular data analysis method in qualitative research. Thematic analysis requires that researchers interpret and identify patterns in the data. They identify themes and code the data (i.e., assign tags by labeling categories).
Thematic analysis effectively turns large volumes of unstructured data into meaningful insights.
Narrative Analysis
Narrative analysis helps you understand how people live and make decisions. Researchers examine and interpret user stories. They look at interviews, testimonials, support tickets, and other textual data to develop context for each case.
Analyzing stories helps researchers understand user needs and pain points. It paints a rich picture of unique user experiences.
Content Analysis
In content analysis, researchers quantify common words, phrases, and ideas. Researchers analyze the meaning and relationships of words and concepts and provide context for the data.
Coding or tagging data makes it easier to identify key ideas. Classifying data helps researchers arrive at reliable conclusions about users and their interactions.
Grounded Theory
Grounded theory looks to formulate a hypothesis from real world case studies. Particularly helpful for product teams looking to examine customer interactions and experiences.
It begins with a question, and then data collection. Researchers analyze and code on the fly, establishing connections in the data. Once they’ve established a hypothesis, they test it using quantitative methods.
Discourse Analysis
Discourse analysis examines the use and meaning of language in different cultural and social contexts. Teams use it for online monitoring – to understand how customers talk about them online. Apply it to any data source – from interviews to social media to online discussion forums.
Using discourse analysis, teams seek to uncover user motivations. It’s heavily dependent on the researcher’s interpretation, though.

How to Analyze Qualitative Data
Here’s a run-down of the process of conducting your own qualitative data analysis:
- Set Up Study. Define your research question. This helps you choose a methodology — different methods apply to different questions. Identify the nature of the data you want to collect. Finally, take stock of resources (time, money, and effort) available for the study.
- Data Prep & Collection. Conduct surveys, interviews, and focus groups to understand more about users. Use apps like Marvin to transcribe video and audio files. Gather feedback from unconventional channels, including online forums, social media, and user reviews.
- Data Organization. Collected data needs a place to live. One where you can analyze it all in one place. As a robust research repository tool, Marvin lets you leverage the company’s existing knowledge. Search for data points across projects and conduct thematic analysis in-house.
- Data Coding. Tagging your data makes it discoverable, analyzable, and shareable.
- Start with Output. Create tags for expected answers and close-ended questions ahead of time.
- Create a Discussion Guide. Tag questions beforehand to help you navigate through interviews.
- Go Granular. Don’t be afraid to code granularly — you can always collapse codes later.
- Refine Tags. Use a global view to clean up tags. Create foundational and overarching labels. Merge overlapping labels.
Want to learn more? Head over to Marvin’s comprehensive guide on tagging user research insights.
- Hypothesize. Visualize data to analyze patterns and trends. Generate insights by examining codes and drawing meaningful correlations between them. Formulate and test hypotheses based on your findings.
- Iterate. Qualitative data analysis is an ongoing process. Constantly refine your understanding of your users and their interactions. After rolling out and testing new product features, gather feedback and go again. Close the feedback loop.

5 Best Qualitative Data Analysis Software
You know how and why qualitative data analysis is important. Now it’s time to dive into the top five tools to get you started.
1. Marvin

Bring all your data into one application. Marvin is a centralized UX research repository that connects to apps that designers and UX researchers love. Import data from Figma, Qualtrics, Asana, Miro, and Notion.
Marvin is the perfect Research Assistant. Invite the bot to your video / audio calls, and forget about taking verbatim notes. Marvin supports transcription in over 40 languages!
With industry-leading AI features, Marvin is a game changer for qualitative data analysis.
Ask AI allows you to query your data across the entire repository. Make data searchable.
Marvin summarizes lengthy transcripts and surfaces key insights. Edit and change them as you please. Use the Analyze tab to code qualitative data. Intuitive functions such as merging labels and hierarchy make thematic analysis a breeze.
Create playlists and share insights with key stakeholders. Anyone can view playlists and clips using a link. A highly collaborative tool with a clean and intuitive UI, Marvin makes data accessible.
All your research in one place. That’s Marvin.
2. Atlas.ti

Bring customer and product data into one platform with Atlas.
Import social media data for online monitoring. Create surveys and roll them out from the application itself. Analyze customer feedback in-house. Continually test prototypes and iterate on wireframes. Autocoding enables easy and quick data analysis.
Atlas is a highly collaborative platform. It caters to marketers, researchers, designers and product teams. Its powerful integrations help you assimilate data from a variety of sources.
It uses AI chatbots — an effective way to gather user feedback. Atlas has a clean UI, but its coding features aren’t intuitive. Codes require manual review (as always!). Its pricing is prohibitive for small research teams.
3. MAXQDA

A tool with longevity – MAXQDA has been around since 1989. Import interviews, surveys, and reports into the application for analysis. Analyze quantitative and qualitative data for a well-rounded understanding of users.
Create surveys with different data types in the application. Use its transcription to capture every detail from interviews and focus groups. Search and retrieve information easily with MAXQDA. Organize unstructured data and visualize it with word clouds.
Create literature reviews for easy future retrieval and referral. MAXQDA is easy to use and inexpensive. It supports multiple languages and file formats. However, setting it up requires large IT resources, and it doesn’t sync data in real time. Its UI isn’t appealing and clean, and its free version has limited functionality.
4. NVivo

Use NVivo to conduct qualitative and mixed-method research. Its transcription works on audio and video files. Automatically generate transcripts for sales calls, customer support tickets, and much more.
NVivo facilitates data organization before analysis. Conduct your analysis on the platform itself. Using autocoding, NVivo helps recognize recurring themes. Visualize your data with trees and word clouds.
Query your research by asking the application complex questions about datasets. NVivo has a clean UI, but can be difficult to pick up quickly. Transcription services aren’t housed within the app. It’s also rather expensive.
5. Qualtrics

A customer experience management platform. Qualtrics XM lets you gather data from numerous customer touchpoints for analysis in one place. Use its variety of integrations (including Marivn!) to bring your data onto the application.
Uncover user sentiment from text. Session recordings give you play-by-play footage of the user experience. Leverage powerful predictive analytics to keep customer opinion at the heart of decision-making.
Update analysis with real-time data to gather updated customer feedback. Identify unique business opportunities and validate product ideas. While Qualtrics is an analysis powerhouse, it can be complex for first-time users.
Common Mistakes to Avoid in Qualitative Data Analysis
Researchers tend to commit these mistakes when conducting qualitative data analysis. Don’t fall prey to:
- (Lack of) Planning. Poorly designed studies produce inaccurate data and misguided decision making. Avoid vague research questions, insufficient framework, and weak data collection methods. Have a plan for organizing and storing research data to turn results into meaningful and actionable insights.
- Bias. You can’t avoid it. Participant or researcher bias always exists. Take steps to mitigate these biases. Avoid leading questions and acknowledge bias. Address researcher and participant background, potential biases, and how those impact the study.
- Human Error. Studies are prone to human error. Check for and factor errors into results. Researchers misinterpret data — by overgeneralizing findings and conducting inadequate literature reviews. Be thorough!
- Neglecting Data Privacy. Research involves companies collecting sensitive user information. Take it seriously! Establish ethical procedures that ensure data privacy and security. Clearly describe ethical measures in place to put participants at ease.
- Incorrect Analysis. Use suitable and rigorous methodology for studies. Researchers often overlook performing qualitative research in conjunction with quantitative studies. Triangulating and testing data for reliability & validity is a must.
- Not Documenting Work. Researchers routinely adjust focus and research design as their studies progress. It’s critical to document one’s methodology and explain choices made while collecting and analyzing data. This is why tools with automated tagging and notes ensure you won’t miss a single important insight.
Qualitative research is an agile and reactive discipline. Researchers must keep these factors in check to avoid erroneous conclusions.

Frequently Asked Questions (FAQs)
Let’s address some frequently asked questions about Qualitative Data Analysis:
What Tool Do You Recommend to Analyze Qualitative Data?
Marvin is the one-stop shop for all your user research.
The only end-to-end UX research repository, Marvin houses ALL your data — quantitative and qualitative. Its powerful integrations allow you to import data from applications you know and love. Don’t disrupt your workflow, augment it.
With industry-leading AI features, Marvin automates several stages of the research process. AI facilitates the following tasks:
- Thematic Analysis. Marvin helps group unstructured textual data into key themes. This aids in quantifying and visualizing data. Researchers can make inferences based on these.
- Trend Analysis. Sit back and let Marvin conduct analysis on your data. It identifies key patterns across projects that you might’ve missed.
- Emotional Analysis. Measure the sentiment behind the text to decipher how users really feel.
By taking care of cumbersome preliminary tasks, researchers can focus on deeper analysis. What are you waiting for? Give Marvin a test drive today! Sign up for a free demo.
How Long Does it Typically Take to Conduct Qualitative Data Analysis?
Long story short? It depends.
When conducted properly, Qualitative Data Analysis is rigorous and systematic. It’s time-consuming and labor-intensive. Researchers spend hours analyzing one transcript.
The duration of a study depends on several factors, including:
- Data Volume
- Complexity
- Study Size
- Research Type
- Researcher Expertise
Fortunately, modern technology takes care of some manual tasks of yesteryear.
Automated transcription helps researchers focus more on moderating interviews. Summarization provides a birds eye view of studies and gives researchers the gist of customer interactions. AI is also capable of coding data (mind you, this needs supervision!).
Leave the drudgery of the research process to your Qualitative Data Analysis tool. Divert your attention to unearthing insights. Save time with Marvin’s qualitative analysis.
How Can I Manage and Store Qualitative Data Securely?
Companies collecting personal and identifiable information (PII) must store data with utmost care. Protecting client confidentiality is essential for gaining and maintaining customer trust.
Establish best practices that ensure data privacy and security:
- Secure Digital Storage. Use storage services that are secure and encrypted.
- Backup & Recovery. Choose reliable data storage for backup. Regularly backup data to prevent data losses beyond your control.
- User Anonymization. Blur faces, use fictitious names, and mask personal details from clips and text data. Obtain user consent before disseminating any information.
- User Access. Control who can view what. Prevent unauthorized access by monitoring user permissions. Provide different users with different rights (view-only vs. edit vs. admin)
- Data Disposal. Use effective data destruction practices to ensure that confidential data is handled correctly.
Choose a tool that safeguards user data. One that adheres to local (and industrial) data privacy and security guidelines.
Want a safe haven for user data? Marvin is GDPR, HIPAA, and SOC2 compliant. Learn more about Marvin’s robust data protection and filters.

Conclusion
Analyzing qualitative data is the bedrock of good research.
Qualitative data reveals the why behind the numbers. It educates researchers about their users and their expectations from a product. Companies share this information with key stakeholders to improve their processes.
Marketing, customer success, and product development teams incorporate customer feedback into their work. They create effective business strategies and tailor products to reach their target audience.
The result? Personalized user experiences and happier customers. Increased revenue soon follows. Not to mention customers who become advocates for your brand. Wins all round!
The right tool for qualitative research data analysis expedites aspects of the research process. If you haven’t already, begin your qualitative data analysis journey today!