Thematic Analysis Software: 5 Top Tools & How It Works

Customer feedback – you’d think you could never get enough of a good thing. But, once you reach a certain point, there’s just too much to analyze – especially if you’re doing manual analysis. So, how do you turn a mountain of feedback into clear, meaningful insights?

Qualitative research plays a crucial role in understanding and interpreting this qualitative data. Traditional methods of analyzing free-text comments can be expensive and involve hours of sorting through walls of text in a spreadsheet and coding responses by hand.

Thematic analysis software facilitates the collection, analysis, and organization of such data, making it more efficient and less expensive to derive insights from your qualitative data.

This article focuses on the thematic analysis of feedback collected at scale. Applying thematic analysis to large quantities of feedback helps quantify themes that impact business metrics. This is the first step to any data-driven change and the continuous improvement all businesses crave.

For tips on manually analyzing your feedback (if that’s your only option), check out our guide: How to analyze your feedback in 10 minutes using word spotting. You can also download our toolkit, which includes a spreadsheet template to help you get started.

Short on time? Jump down to Top 5 Thematic Analysis Softwares & Tools.  

Or, skip to the key takeaways and FAQs section at the end of this post.

What is thematic analysis?

Thematic analysis is a form of qualitative data analysis. The output of the analysis is a list of themes mentioned in text. These themes are discovered by analyzing words and sentence structures through qualitative text analysis, which is crucial in identifying patterns and insights.

For example, take these three sentences:

These comments include both positive and negative sentiments. There are two key themes expressed in different words:

• The helpfulness of flight attendants
• Customers needed help setting up a baby cot

Thematic analysis can be applied to any text. Interviews, conversations, product feature requests, and open-ended questions in surveys or reviews are all well suited to this analytical method.

Thematic analysis vs. sentiment analysis

The answer to thematic analysis versus sentiment analysis is not an either-or. In fact, sentiment analysis is often part of a qualitative analysis solution, encompassing both thematic and sentiment analysis.

Sentiment analysis captures how positive or negative the language is. It finds emotionally charged themes and helps separate them during a review.

Using the same flight attendant example, you can see there is one positive and two negative mentions of a theme:

If you only apply sentiment analysis, one person is happy, and two are unhappy. When you layer thematic analysis on top, it tells you what they were happy or unhappy about. Combining thematic and semantic analysis in qualitative data analysis software results in better accuracy and nuance.

Read our comprehensive guide for a more in-depth sentiment analysis and qualitative data review.

Qualitative data analysis software

Qualitative data analysis software is a powerful tool designed to help researchers and analysts make sense of qualitative data—such as text, images, and videos—and better understand its nuances.

Key features of qualitative data analysis software include:

• Data import and organization: These tools allow you to import and organize qualitative data from various sources, such as open-ended survey questions, reviews, and free-text feedback. This feature ensures that all your data is centralized and easily accessible.
• Coding and annotation: The ability to code and annotate qualitative data is essential for identifying patterns, themes, and meanings. This feature helps in systematically categorizing data for deeper analysis.
• Data analysis: Qualitative data analysis software offers various techniques, such as content analysis, thematic analysis, and discourse analysis, to analyze your data. These techniques help you uncover insights that might not be immediately apparent.
• Visualization: Visualization tools, such as charts, graphs, and maps, are integral to qualitative data analysis software. They help in presenting data in a more digestible and visually appealing manner.
• Collaboration: Many qualitative data analysis tools offer real-time collaboration features, making it easier for teams to work together on projects. This is particularly useful for large-scale research projects involving multiple stakeholders.

Researchers can efficiently analyze qualitative data by leveraging these features, uncovering valuable insights that drive informed decision-making.

What is thematic analysis AI software?

Thematic analysis AI software, a qualitative data analysis tool, automates thematic analysis and is crucial for interpreting qualitative data effectively. Businesses use it to find themes in customer feedback.

Do your customers rate comfort over affordability? Would they pay more for faster service? Thematic analysis software can help you find (and act on) the answers.

The best thematic analysis tools are autonomous, meaning:

• You don’t need to set up themes or categories in advance,
• You don’t need to train the algorithm — it learns on its own,
• You can easily capture the “unknown unknowns” to identify themes you may not have spotted on your own.

Want to see an example of Thematic analysis in action? You can trial Thematic for free here.

How does thematic analysis software work?

Thematic analysis tools automatically analyze customer feedback to identify and extract themes.

These tools are also highly effective in supporting mixed methods research, providing technical and methodological support for research teams that use various methodologies.

Natural language processing (NLP)

Natural language processing (NLP) is a subcategory of Linguistics and AI. NLP enables computers to analyze large amounts of natural language, aka text.

When a computer attempts to model the meaning of words, sentences, and text, we call it natural language understanding (NLU). NLU is a sub-area of natural language processing.

What about text analytics? This term commonly refers to NLP and NLU in business settings.

Some thematic analysis softwares use NLP and NLU to find themes in a text, often displaying the analysis in analytic tools and dashboards.

However, more modern and advanced thematic analysis AI tools use Large Language Models.

Large Language Models (LLMs) in Thematic Analysis AI

The goal of thematic analysis software is to automate theme discovery in text. By leveraging the LLMs, thematic analysis software, like Thematic, can efficiently process vast amounts of qualitative data and extract language nuances, context, and sentiment. This gives you a more detailed understanding of the underlying themes present in customer feedback.

For example, our software can cluster related themes, pinpoint significant trends over time, and suggest actionable recommendations based on the analysis. This saves time and enhances the accuracy and depth of the qualitative analysis.

How is this different from using a machine learning (ML) AI model? Our guide on Large Language Models for Feedback Analysis goes in-depth into machine learning versus large language models.

Word embeddings

Word embeddings is a deep learning algorithm that finds similar words and phrases in text data, even at volume, and spits out a model. In other words, a word embeddings model translates our language (a vocabulary) to a computer’s language (vectors).

Thematic uses a custom word embeddings implementation to turn feedback into a hierarchy of themes:

Benefits of using thematic analysis software

Several compelling benefits make thematic analysis software an essential tool for researchers and analysts:

• Increased efficiency: One of thematic analysis software's most significant advantages is the automation of many tasks involved in thematic analysis, such as coding and data analysis. Automation saves time and effort, which means researchers can focus on interpreting results rather than manual data processing.
• Improved accuracy: Human error and bias can significantly impact the accuracy of thematic analysis. Thematic analysis software reduces these risks by providing consistent and objective analysis, ensuring more reliable results.
• Enhanced collaboration: Thematic analysis software often includes features facilitating collaboration among researchers and analysts. This makes working on qualitative data analysis projects easier as a team, ensuring that insights are shared and discussed in real-time.
• Higher-quality insights: Thematic analysis software can provide deeper insights into qualitative data by automating the analysis process. It helps identify patterns, themes, and meanings that may not be immediately apparent, leading to a more comprehensive understanding.
• Cost-effective: Traditional thematic analysis methods can be time-consuming and labor-intensive, often requiring significant resources. Thematic analysis software can be more cost-effective by reducing the need for manual coding and data analysis, ultimately saving time and money.

These benefits make thematic analysis software an invaluable tool for anyone looking to analyze qualitative data efficiently and effectively.

The limitations of using ChatGPT in place of thematic analysis software

For smaller datasets, you can use ChatGPT to analyze customer feedback. Prompting ChatGPT with specific questions about the data can generate initial thematic categories and even summarize findings. However, the interpretive nature of thematic analysis requires a nuanced understanding of context, which ChatGPT may not fully grasp, potentially leading to oversimplified or inaccurate theme identification.

However, there are significant limitations to using ChatGPT for thematic analysis. The model lacks the sophisticated analytical capabilities and structured methodologies of dedicated thematic analysis software, which can offer more robust tools for coding, categorizing, and visualizing themes. These tools include reliability checks and collaborative features for deeper, more rigorous analysis. Ultimately, while ChatGPT can assist in generating initial insights, using thematic analysis software will give you a more comprehensive and systematic approach to qualitative data analysis and reduce the risk of misinterpretation.

Why you need thematic analysis software

Now that you know what thematic analysis tools do, what about the why?

Here’s how companies can benefit from adding thematic analysis software to their tech stack:

Save time and increase accuracy

Time is a scarce resource when running a business. Thematic analysis software can save your team hundreds of hours a year and prevent them from making wrong decisions.

Many companies still analyze feedback using Excel. This is time-consuming and not scaleable, even for small businesses. Thematic analysis software will help you be more effective.

Thematic analysis software can also help avoid human errors. When people look at a dataset, they tend to view it through the lens of their own experiences and biases. They also might miss something unintentionally.

For example, we once tested Thematic against a human coder, Kate, when analyzing student feedback at a university.

Thematic found that students wanted better food/lunch options. Kate found the same issue but at a much lower frequency. Why? When Kate looked at the student feedback, she tagged only one key issue per comment. Thematic tagged every issue mentioned in each student's comment. Once the university improved food on campus, student satisfaction increased.

Using thematic analysis software, coders like Kate no longer have to code feedback manually. Instead, they can use their expertise to interpret the results and drive actions.

Quantify customer feedback

When discussing quantitative customer feedback, metrics like Net Promoter Score (NPS) often come to mind. And while NPS scores can be useful snapshots of customer satisfaction, they don’t always tell the whole story.

• Why did one of your most loyal customers rate you an eight instead of a 10?
• Why did a detractor churn while a promoter doubled their monthly orders?
• What impact on NPS will we see by taking action to address a specific customer pain point?

Thematic analysis software helps you find these answers. It turns text feedback into the hard data you need to report and measure an initiative's success.

“This has made it much easier to get projects across the line, with hard data that we can use to measure success of an initiative." - said one Thematic user on G2 Crowd.

“Better yet, we can see how specific themes impact NPS scores!” - shared another.

Make data-driven decisions and track results

Thematic analysis software can turn feedback into hard data for making decisions and tracking progress.

You can see whether your actions make a difference in real-time, adjust your response, and fine-tune your solution. This can save an incredible amount of time and money in many situations.

Let's go back to our university example. To start with, a high percentage of students disliked campus food. The university implemented initiatives to address this, and then they re-surveyed students.

Thematic analysis shows whether students noticed and what other issues are now rising.

Here’s an example of how Thematic visualizes this in its platform:

You can see that by and large the university’s efforts were successful. If they’d like to continue working on student satisfaction, they'll need to dig into what's causing issues with computer equipment, getting to campus, and social events.

How to choose thematic analysis software

There are many qualitative data analysis software options on the market, but they don’t all perform to the same standards. Before you decide which text analysis solution to adopt, you’ll want to research both the features and capabilities of each possibility on your shortlist.

Some software focuses more on audio files or interview transcripts, providing qualitative data analysis for focus groups and product research projects. Others are more flexible and geared toward the continuous flow of customer reviews, support requests, and NPS surveys.

When evaluating different feedback analytic tools, here are some tips on what to look for:

• Decide on specific feedback analysis goals: Determine what type of insights you need – customer sentiment, product feedback, or employee engagement. This will help narrow down options and ensure the chosen tool aligns with your business goals.
• Look for autonomous theme identification: This will reduce the manual effort and time required for qualitative data analysis, allowing researchers to focus on interpretation and strategic decision-making. It also helps minimize human bias, leading to more objective insights.
• Check integration capabilities: Can the tool integrate seamlessly with your existing systems, such as CRM, project management, or survey platforms? Integration is essential for smooth data flow and more comprehensive analysis across different touchpoints.
• Consider scalability and support: Choose a tool that can handle the volume of qualitative data you’re working with and scale with your business as it grows. Evaluate the vendor's level of customer support – a robust support system is essential for troubleshooting and maximizing the tool's capabilities over time.

You can download our free buyer's guide for more hacks and insights into evaluating potential vendors.

Top 5 Thematic Analysis Softwares & Tools

Qualitative data analysis software with thematic analysis functionality

Your use case and budget will greatly influence the thematic analysis software you use. But what's important is that it meets your company's individual needs.

Let’s take a closer look at Thematic and other highly-rated solutions:

1. Thematic

Thematic’s core focus is automating feedback analysis. Why? So you have the time (and tools) to focus on the more exciting parts of analyzing data and reporting on your findings. Thematic is best suited for anyone who collects feedback from various sources, such as surveys, live chat, complaints, and reviews.

How Thematic stands out in the software crowd:

• Scalability: Thematic can handle exponentially more data than human teams. This means you can scale your data analysis without increasing staff numbers.
• Real-time analysis: You can analyze customer feedback in real-time and take immediate action.
• Accuracy and depth: Thematic’s Generative AI autonomously enhances the quality and depth of insights.

Combining AI with a human touch: how Thematic works

In the Themes Editor, you can adjust themes to make results more relevant to your goals and priorities.

This combination of AI, LLM, and a human touch provides you with a list of themes that are:

• Contextually accurate
• Varied enough to cover all of the topics in your dataset
• Meaningful for you and your business

Once you have your themes list, Thematic displays your analysis through customizable dashboards and analytical tools.

These tools let you:

• Measure the importance of each theme
• Compare each theme across different segments of your data, such as demographics or tenure.
• Calculate each theme’s impact on satisfaction, loyalty, churn, and spend metrics.

Here’s what that process looks like:

2. Dovetail

Dovetail is a user research platform for UX researchers running small one-off research studies. Thematic analysis is one of its key features. It makes it easy to manually analyze text, tag specific parts of feedback with themes, and organize these themes. It's great for collaborating effectively with others and building up research repositories.

If you visit their website, you can see some animated examples of the software in action, transcribing audio files and pulling insights from interview transcripts. Search for tags in an interview, and see an immediate count of all instances of what you’re looking for.

Key thematic analysis features include:

• Highlighting to tag text
• Organizing taxonomies
• Sentiment analysis
• Graphical reporting

3. Relative Insight

Relative Insight leverages advanced natural language processing and comparative analytics to derive meaningful insights from textual data. The software automates the identification of themes and trends across multiple data sets. It highlights differences and similarities, which makes it particularly valuable for businesses looking to refine their messaging or improve customer engagement.

However, compared to other thematic analysis solutions, the software is much more complex. Because of its sophisticated features and functionalities, the learning curve for new users can be steeper, which may set UX and CX research teams back.

Key thematic analysis features include:

• Comparative analysis
• Natural language processing
• Customizable reporting

4. XM Discover

XM Discover by Qualtrics is a software platform with thematic analysis functionality. It allows users to systematically identify and categorize themes within open-text responses, empowering decision-makers to understand sentiments and trends effectively.

One key advantage of the software is that it’s an integrated platform that facilitates comprehensive survey design, data collection, and reporting—all in one place. However, like Relative Insights, it’s a complex system that requires extensive training to start analyzing data.

Key thematic analysis features include:

• Integrated survey design
• Text analysis
• Comprehensive reporting
• Real-time feedback

5. MAXQDA

MAXQDA is a leading qualitative data analysis software. It has a user-friendly interface with features like mixed methods support for qualitative and quantitative research.

This tool has comprehensive coding capabilities that allow users to create detailed codes and subcodes for nuanced analysis, facilitating deeper insights into complex data sets. However, a potential drawback is that MAXQDA's advanced features may lead to increased file sizes and processing demands, especially when handling large datasets, affecting performance and speed.

Key thematic analysis features include:

• Advanced coding system
• Mixed methods support
• Data visualization
• Made for team collaboration

Getting started with thematic analysis

Tackling thematic analysis involves several steps to ensure thorough and insightful research of qualitative data:

• Defining the research question: The first step is clearly defining the research question or objective. This provides a focused direction for the analysis and ensures that the data collected is relevant.
• Collecting data: Gather relevant qualitative data. This data can come from various sources, including surveys, interviews, and focus groups.
• Preparing data: Before analysis, cleaning and organizing the data is essential. This step involves removing any irrelevant information and structuring the data so that it is easy to analyze.
• Choosing thematic analysis software: Selecting the right software that aligns with your research needs and offers features that facilitate efficient and effective analysis is critical.
• Coding and analyzing data: Use the chosen software to code and analyze the data. This involves identifying patterns and themes within the data and categorizing them accordingly.
• Interpreting results: The final step is to interpret the thematic analysis results. This involves identifying the key patterns, themes, and meanings that emerge from the data and understanding their implications for your research question or objective.

By following these steps, researchers and analysts can effectively use thematic analysis software to analyze qualitative data and gain deeper insights into their research questions or objectives.

Ready, set, analyze!

Researchers and insights professionals love the efficiencies thematic analysis software unlocks. It saves time and money and is just as accurate as human analysis (and, in some cases, even more precise).

Book a demo with our team to see how Thematic works on your data. They'll be able to set up a free trial on your dataset so that you can discover and review Thematic’s full functionality.