Everywhere you look, if you pay close attention, you’ll spot themes. They show up in the stories customers tell, the choices they make, and the product features they love.
Patterns are hiding in plain sight, and thematic analysis in qualitative research helps you uncover them. This method gives you a systematic way to:
- Spot trends, find meaning, and tell a bigger story with your data
- Guide product decisions without losing the human story
The caveat? While it sounds simple, anyone who has done thematic analysis knows it isn’t easy. But our AI-powered qualitative research assistant, Marvin, can lighten your workload and sharpen your findings.
Book a free demo today to see how Marvin speeds up coding, tagging, and pattern discovery.
What Is Thematic Analysis in Qualitative Research?
The concept of thematic analysis is simple in theory. It’s a method for analyzing qualitative data by identifying patterns or themes.
These themes are the meaningful ideas that recur across your data. They’re critical because they give insights into user behaviors or feelings. But they can be difficult to identify without a systematic process.
Thematic analysis is one such process, designed to extract the dominant ideas you need to pay attention to.
For smaller projects, you can do it by hand using sticky notes or spreadsheets. For larger or more complex datasets, software tools help you speed things up and stay organized.
Regardless of the approach, the final result presents a clear, easy-to-follow report that effectively reflects these central themes. If, for instance, you interviewed users about your new app design, the themes could be:
- “Users struggle with login clarity.”
- “They expect faster loading times.”
- “Customers prefer simple navigation menus.”
The final report should illustrate each theme with quotes or examples directly from interviews or feedback sessions.
Inductive vs. Deductive Thematic Analysis
In qualitative research, thematic analysis can take two different directions — inductive or deductive.
Inductive analysis is akin to exploring without a map, curious to see where research will take you. In contrast, deductive analysis aims to confirm certain expectations from the very beginning.
For a clearer image, take a look at these two types of thematic analysis side by side:
| Feature | Inductive Thematic Analysis | Deductive Thematic Analysis |
| Goal | Discover themes directly from data | Check data against existing ideas |
| Start with | Raw data without preset ideas | Existing concepts or frameworks |
| Use case | Finding new insights or surprises | Testing or confirming assumptions |
| Flexibility | Highly flexible, open-ended | Structured, clearly focused |
However, a general classification of thematic analysis also includes the reflexive option.
What is reflexive thematic analysis? It’s an approach that can be either inductive or deductive. What sets it apart is its emphasis on your role in shaping themes.
This method involves your active and thoughtful interpretation of meaning in the data. You could be reflexive whether you’re discovering the unexpected or exploring known ideas with fresh eyes.
Advantages of Thematic Analysis in Qualitative Research
Thematic analysis gives you clear insights without getting lost in mountains of messy data. It’s simple yet powerful, perfect when you want results that speak clearly to your team.
Here are some key advantages:
- Flexibility: It fits many research types, from quick user tests to deep customer studies.
- Simplicity: Easy to learn, even if you’re new to qualitative research.
- Scalability: You can do it manually for small datasets or use tools for large sets.
- Clear communication: Themes are simple to explain to your team, developers, or stakeholders.
- Rich insights: Captures subtle user feelings and behaviors that are hard to catch with numbers alone.
- Practical results: You get actionable findings to guide real design and product choices.
How to Do Thematic Analysis in Qualitative Research
There is a method to transform raw user interviews and feedback into clear, actionable themes.
Below is an easy-to-follow guide inspired by the Braun and Clarke thematic analysis steps:
Step 1: Get Familiar with Your Data
Start by delving into your interviews, feedback, or observations in depth. Read or listen carefully to everything, multiple times if needed.
Take notes on what jumps out at you, and don’t rush this part. Allow yourself to absorb the overall meaning, noticing patterns or ideas that emerge.
For now, just observe – without the pressure of writing down anything. This first step helps you establish a solid foundation for identifying key themes later on.
Step 2: Start Creating Initial Codes
Next, break your data down into smaller chunks or ideas. These are called “codes”. (We have a detailed guide that explains how to create a codebook for qualitative research.)
The codes must highlight interesting bits, such as user pain points or design suggestions.
You want to keep these codes short and clear: “login issues,” “navigation confusion,” “positive feedback on color scheme,” etc.
Codes are not themes, though. By coding your research first, you lay the groundwork for finding themes across your data.
Step 3: Search for Bigger Themes
Now, look carefully at your codes and start grouping the related ones. These groups become your broader themes.
For instance, codes such as “unclear buttons,” “poor readability,” and “confusing labels” might form a theme called “interface clarity.”
Organizing codes into themes allows you to see larger patterns. It reveals insights about your users’ overall experiences or opinions.
Step 4: Refine Your Themes
Time to polish those initial themes by double-checking each group. Ensure that the codes within each theme are cohesive and accurately reflect your data.
Combine themes that feel too similar or split large themes into smaller, clearer ones.
Refining should help each theme clearly represent distinct, meaningful insights. This step will make your findings more focused and easier to understand.
Step 5: Define and Name Your Themes
Now, give each theme a clear, descriptive name that quickly captures its meaning.
For example, instead of “App Issues,” name it “User Frustrations with App Speed.”
Write a short description to explain exactly what each theme covers. Clear naming and definitions help stakeholders or designers instantly grasp the key insights. This makes your UX research report highly actionable.
Step 6: Create and Share Your Findings
Finally, wrap things up by presenting your themes in a straightforward, engaging report or presentation.
Include descriptions of each theme, illustrative quotes or examples from users, and your interpretation of their significance. Keep it clear and focused.
This last step turns your analysis into something your product team can act on. It should help improve user experience and guide product decisions.
Best Thematic Analysis Software to Use
As data piles up, thematic analysis can get overwhelming. Thankfully, great software tools can speed up the process, reduce errors, and reveal insights you’d otherwise miss.
Here are some top thematic analysis tools to consider:
#1. Marvin
Marvin is a qualitative data analysis platform and UX research repository. Our tool’s AI workflows automate the tedious parts of thematic analysis, turning hours of research into insights in minutes.
With Marvin, you can transcribe interviews in over 40 languages. It also takes live notes, generates precise timestamps, and tags your data with relevant themes.
Our tool can also accommodate all your existing research. You can bring videos, audio, surveys, and notes into this easy-to-search, centralized repository. Once its AI spots patterns, trends, and emotional insights, you can move on to reporting.
Pull out quick clips, highlight reels, and create kanban boards to share with your entire team. If you’re looking to streamline your UX research workflow, Marvin is the perfect choice.
Book a free demo today to discover why Marvin is the best software for thematic analysis.
#2. Dovetail
Dovetail is another customer insights hub that can help with qualitative research projects. It supports thematic analysis with low-friction coding tools and allows fast organization.
Its reporting features focus on producing clean, visual outputs for sharing findings with stakeholders or clients. And the platform prioritizes a polished user experience.
However, the AI features may sometimes lack accuracy, and pricing can add up quickly depending on your needs. That’s why you might want to consider some Dovetail alternatives.
Did you know? Marvin ranks as the best Dovetail alternative on G2.
#3. Condens
This platform specializes in real-time collaboration for qualitative research teams. It allows multiple users to tag, code, and discuss findings simultaneously.
The built-in summarization features let you synthesize insights into clear, actionable outcomes. If you’re looking for speedy collaboration and collective decision-making with your thematic analysis, Condens might help.
Just keep in mind that it’s not particularly accommodating with multilingual content.
#4. Aurelius
As a research platform, Aurelius focuses on integrating research insights into product and UX development workflows.
It supports mapping themes directly to the next steps and strategic initiatives. And reporting tools emphasize clarity and alignment for both internal teams and external stakeholders.
Aurelius was built for those looking to operationalize research within broader product strategies. One thing to keep in mind is that when it comes to reporting, the tool’s customization options are somewhat limited.
Thematic Analysis in Qualitative Research Example
Examples can make the concept of thematic analysis easier to understand, so here’s one.
Let’s say you conducted user interviews about a mobile banking app. Your goal with this research is to improve the user experience.
First, you carefully review interview transcripts, highlighting key points users frequently mentioned. These can be “slow loading times,” “confusing navigation,” “security concerns,” etc.
Next, you group similar points into broader themes: “performance issues,” “usability challenges,” and “trust and security.”
Your final themes will emerge clearly:
- Performance issues: Users often mentioned frustration with slow app loading and crashes.
- Usability challenges: Navigation confusion and complicated menus caused user irritation.
- Trust and security: Many users felt uncertain about data safety and privacy.
Presenting these themes to your product team will provide actionable next steps to address user pain points directly:
Performance Issues
- Prioritize app performance improvements in the next sprint to reduce load times and prevent crashes.
- Allocate QA resources to test performance across devices and network conditions to ensure stability.
Usability Challenges
- Simplify the app’s navigation structure to align with common user behaviors and mental models.
- Introduce a first-time user experience (FTUE) with in-app guidance to reduce onboarding friction.
Trust and Security
- Update the user interface to highlight privacy and security features more clearly and transparently.
- Implement visual cues and confirmations for security settings, such as two-factor authentication, to build trust.
This thematic analysis example demonstrates the practical power of clearly identifying and communicating user feedback.
Best Practices for Conducting Reliable Thematic Analysis
While effective thematic analysis takes a little extra effort, it pays off with stronger insights.
Follow the good practices below to keep your work sharp, clear, and valuable for your team.
- Always keep a research diary as you work through the data.
Write down the early thoughts, hunches, and surprising patterns you notice. This helps you stay aware of your own biases, making your final insights easier to trust.
- Be consistent with your coding approach from start to finish.
Set clear rules for what counts as a code and what doesn’t. Apply those rules carefully, even when you get deep into dozens of interviews or feedback sessions.
- Balance your code’s details with brevity to spot patterns quickly.
Strong codes act like breadcrumbs, leading you straight to the themes that matter.
Keep yours detailed enough to hold meaning but short enough to reflect patterns easily. Avoid vague labels such as “user comment” or “general complaint.”
- Review your themes with a second set of eyes when possible.
A teammate can catch things you missed or question shaky groupings. If working alone, take a break before reviewing to come back with a fresh mind.
- Tie every theme back to clear evidence in your data.
Quotes, notes, or transcripts should clearly support why a theme matters. Never build a theme on a feeling alone. Your users’ actual words must back it up.
- Limit the number of final themes to keep them powerful and easy to act on.
Too many themes will overwhelm your product team and dilute the impact of your findings. Focus on the strongest insights that truly change the user experience.
Frequently Asked Questions (FAQs)
Consider these thematic analysis FAQs before applying this method to your qualitative research:
What’s the Difference: Content Analysis vs. Thematic Analysis?
Content analysis focuses on counting and categorizing specific elements within the data. It looks for measurable patterns, such as word frequency or topic appearance.
By contrast, thematic analysis seeks to uncover deeper meanings and recurring ideas. It explores how patterns form and what they reveal.
Both methods organize data, but with different goals.
How Long Does Thematic Analysis Typically Take?
Thematic analysis can take anywhere from a few days to several months. Smaller projects with limited interviews often finish quickly, while larger studies with complex data require more careful review and coding.
Team size, coding methods, and reporting standards also influence timing. That’s why careful planning and a tool like Marvin help keep the process efficient and manageable.
Can Thematic Analysis Be Used in Mixed Methods Research?
Yes. While quantitative data may reveal what users do, a thematic analysis of open-text responses explains why they do it.
Use thematic analysis before, after, or alongside quantitative analysis to uncover patterns that numbers alone can’t reveal. When integrated well, the themes you identify can inform survey design, explain trends, or validate statistical results.
Can AI Tools Assist in Thematic Analysis?
Indeed, AI-powered tools like Marvin can accelerate coding and help detect recurring ideas in large datasets. These tools highlight common phrases or emerging themes, which researchers can then review and validate.
Microsoft used Marvin to analyze over 80 hours of interviews and synthesize more than 2,000 notes. Our AI research assistant extracted deeply nuanced, qualitative insights for the development of their Responsible AI Maturity Model.
Conclusion
Thematic analysis is powerful but demands time, focus, and rigorous attention to patterns across messy data.
If you want insights that drive real product change, you need to stay organized, consistent, and thoughtful throughout the process. And that’s where Marvin can make a real difference, speeding up every step of your thematic analysis.
From transcribing interviews to smart tagging and uncovering hidden patterns you might otherwise miss, Marvin helps. It allows you to move faster without sacrificing depth or accuracy.
Create your free account today and distill your qualitative research into crystal-clear themes. It’s the first step toward smarter product decisions.
