By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
Home > Blog >
How to Use AI for Qualitative Data Analysis
Home > Blog >
How to Use AI for Qualitative Data Analysis

How to Use AI for Qualitative Data Analysis

Theertha Raj
February 15, 2024

This is part of a new Looppanel blog series on AI and UX Research. Read the rest of the series for more on AI tools for UX research, or ChatGPT Prompts for faster research.

One of the most frequent pain points for qualitative researchers?

The time it takes to manually code, analyze and synthesize huge amounts of data from calls, focus groups, interviews, or even open ended surveys.

It’s often described as… 

Tedious. Time-consuming. Drowning. Manual. Overwhelming.

Even though we love the puzzle of de-coding people’s behavior and needs, sometimes it just feels like too much information to make sense of in a couple of hours or days. 

Especially with tight deadlines and demanding stakeholders, researchers often don’t have hours (or days) to spend re-reading transcripts, sorting through scrappy notes, and coding or tagging data in Miro boards and excel sheets.

Luckily, we’re on the verge of an exciting wave in technology. 

According to the 2023 State of User Research Report, 20% of researchers are currently using artificial intelligence for research, with another 38% planning to join them in the future.

Can AI do thematic analysis?

While AI is not perfect, it is an amazing assistant to help you extract, organize and qualitatively code or theme your data so you can find insights faster.

Skeptical? Just try this AI-powered research assistant (free for 15 days!), and get back to us.

In this article, we’ll look at:

  • What is thematic coding in qualitative analysis
  • Pros and cons of doing thematic analysis manually
  • Can AI be used in qualitative research?
  • Tips and tricks on using AI for data analysis

What Is Qualitative Data  Analysis?

Qualitative data analysis is the process of analyzing qualitative data (usually interviews recordings / transcripts / notes, written feedback, etc.) to identify patterns, themes or insights. 

Qualitative data analysis often requires thematic analysis / qualitative coding (labeling your data points with themes) and affinity mapping (clustering data based on these themes to find patterns).

Thematic analysis also makes it easier for collaborators to analyse data with you, and build insights together. 

What are the 5 methods to analyze qualitative data?

You can do thematic coding and analysis two ways—manually or digitally.

How to do qualitative data analysis manually

If you’re manually analyzing data, you’re using Post-Its and whiteboards or a digital version of the same like Miro or Mural.

The manual process involves extracting relevant data, tagging or grouping it and then reviewing insights at the end.

If you’re analyzing user interviews, usability tests or focus groups for example, you can re-listen to your calls to make notes, or rely on ones taken during the interview.

Usually folks add their notes to an excel sheet or Miro board and start looking for patterns across calls there.

For an in-depth walk through of how you can run qualitative data analysis manually, check out these pieces:
- How to analyze user interviews

- Many modes of research synthesis

- Affinity mapping: An ultimate guide

How long does it take to analyze qualitative data?

Short answer: it can take anywhere from a few days to a few months to analyze qualitative data.
Long answer: it depends on how much data you’re dealing with, how robust your findings need to be, and how much time you have to work with.

Academic research? Sure, take 6 months to code your interviews, dig into the details. Your published paper needs to cross a very high bar of accuracy—take your time reviewing your data.

Working at a research agency or a fast moving company? You probably have a few days to get to the insights, and while the results need to be accurate, you’re not going to peer-reviewed any time soon.

The other factor that affects how long it takes is of course, how much data you’re dealing with.

2-3 calls? Just a few hours or days!
10-20 calls? 500 open ended survey responses? 5 focus groups?
Strap in. This is going to take a few days to a few weeks.

Based on our research, for an average project (say 50-10 1 hour in-depth interviews), most qualitative researchers take 4-5 working days.

Example of manual coding in qualitative data analysis on Looppanel, by highlighting sections of the transcript and adding tags
Example of manual coding in qualitative data analysis on Looppanel, by highlighting sections of the transcript and adding tags

Pros of Manual Qualitative Data Analysis

A lot of researchers like to do manual qualitative data analysis, despite the time and labor it requires. Here are a few reasons why.

1. You have complete control over the data

Some researchers believe that you need to fully immerse yourself in the data for real inspiration and insights. Manual research analysis is perfect for that. By reading and organizing all the data segments yourself, you understand the contours of the research better, as well as your users.

2. It’s easy to learn and execute

You don’t have to spend time figuring out a fancy new tool. All the labeling and notes can be conquered with Post-its, paper or a simple whiteboard tool.

3. You can make it collaborative

On a whiteboard or Miro board, you can bring in teammates or clients to analyze data for you—discussing each data point and labeling or grouping it based on consensus. This allows you to get buy-in from stakeholders and take their perspective into account when discovering insights.

Cons of Manual Thematic Analysis

While Thematic Analysis is really powerful, as we know—it has 4 major drawbacks:

1. It’s very time-consuming

If your research has a deadline in place, manual coding is a lot to take on. For academic projects with longer timelines, there should be room for it. But if you’re working at a company or agency—you have a few days or weeks to turn insights around. 

To do qualitative data analysis properly, researchers have to meticulously sift through raw data, identifying patterns and assign codes. This process can be painstaking, especially when dealing with large datasets.

It’s even worse with pen and paper. Post-it notes can get lost. Handwriting can be misread.

2. It’s insanely difficult to scale

You know that feeling when you've had 10 hour-long interviews you need to go back though? Or over 100 pages of transcripts? It’s painfully exhausting to review and re-review all this data.

For extensive datasets, manual qualitative tagging becomes laborious and inefficient. As the volume of data increases, so does the time required for coding.

It can also get difficult to maintain consistency and accuracy when handling numerous transcripts or interview recordings.

3. It requires work and re-work

Just imagine reading all that data and transcripts and manually remembering to code and categorize everything. 

You begin with one set of codes— let’s say different issues based on customer feedback. Halfway through the reading, you realize that a new feedback point has recurred enough to be labeled separately. You’ll now have to revisit all your data again and make changes to your coding.

And this can happen over and over, depending on what your data throws up.

Even if you outsource the tougher parts, hiring and training human coders can be expensive. 

4. So. much. data.

Since qualitative data is unstructured, you can very easy get overwhelmed and be drowning in recordings, transcripts, notes before you know it.

Think about it—you’re trying to figure out common patterns from 500 open-ended survey responses, 200 pages of user interview transcripts, and the note-taker’s observations from watching users interact with the new feature. It’s a lot.

5. Bias

If it’s just you coding—bias can enter easily.

With manual qualitative analysis, the themes that appear will be based on your subjective interpretation. One way to avoid this is by having multiple people code the data. 

If you’re working on this as a team, manual coding can also mean endless back and forth, lack of continuity between segments and internal contradictions.

Also, you’re just human! It’s highly possible to miss or misremember things.

Alternatives to Manual Qualitative Coding

To solve these many challenges, researchers often have some short-cuts up their sleeves.

You can always get a live note-taker (or be one yourself)! This is not the easiest, of course. You need another person on your team’s time, availability,and they need to be good at taking notes. Tough trifecta. If you’re at an agency, this could affect budgets on projects as well.

The easier route? AI qualitative analysis software. There’s a new era of tech AI-powered tools out there, ready to help and do the grunt work you don’t want to. It’s like having a research assistant available 24/7.

This brings us to the next section— the magic of AI qualitative analysis, and how it speeds things up!

AI for Qualitative Data Analysis

With the advent of Artificial Intelligence (AI), particularly through Natural Language Processing (NLP) and Generative AI, research analysis is going through a massive shift.

Sure, it’s not smarter than a person (yet). It cannot reason, and won’t be taking over any researcher jobs anytime soon.

But it can incredibly speed up research, and help you skip the tedious parts.

Can ChatGPT Analyse qualitative data? It can, but would take a lot of work, context providing and training. However, AI-powered tools specifically meant for qualitative analysis are another story.

AI qualitative analysis tools can generate highly accurate transcripts in minutes, take notes for your calls, organize data, add automatic tags and build affinity maps. 

Researchers can skip the busywork, and spend time paying attention to users and thinking through insights.

Can AI be used in qualitative research?

What is AI really good at?

It can process large amounts of data at inhuman speed.

It can summarize pages and pages information accurately.

It can scan large documents and find information for you in seconds.

This means that it can help researchers do their job 5x faster, by automating the manual, time-consuming, tedious parts.

Researchers already swear by tools like Looppanel for making it 10x easier to go from data to research insights. Here are some of the ways these AI tools speed up qualitative coding and analysis.

1. Generate super accurate transcripts in minutes.

Quality of AI transcription across accents has improved substantially. The days of manually listening to and transcribing calls are over (at least in English!)

Stop wasting time correcting transcripts or paying for expensive human transcription—hop on the AI-assisted transcription bandwagon to see what’s possible.

While the quality of transcripts is superior in English, tools like Looppanel offer transcription in multiple languages like Hindi, German, French, Spanish, Portuguese, Dutch, and Italian.

A customer testimonial about Looppanel's transcription with AI
Researchers at Pandadoc LOVE Looppanel's transcription with AI!

2. They can replace your note-taker (I’m not kidding)

Finding a good note-taker for every call is tough.

It’s expensive, time-consuming, often just not even available. Luckily, note-taking is the kind of task AI is actually really good at.

AI qualitative analysis tools can capture key parts of your call while keeping you in the driver’s seat. It’ll automatically highlight where questions were answered, summarize notes in a Q&A or theme based format.

Instead of spending days re-reading transcripts, you can review the notes, make sure they capture what you care about, and add additional context or tags as needed.

With a little help from AI-assisted note-taking, researchers can speed up the entire process, reduce dependence on team members and make sure they don’t miss anything from their interviews.

Here's what Looppanel's AI notes looks like

Looppanel’s AI-powered note-taker for example joins digital interviews  on Google Meet, Zoom or MS Teams seamlessly to record, transcribe and take notes, leaving you free to focus on the conversation at hand.

After your call ends, you can review notes by the question they answer, review the transcript section they’re created from, or create shareable  video clips to send to your team.

3. They can create codes and automatic tags

Instead of reading through all the pages of the transcript to identify key themes and codes, AI thematic analysis tools can do the first pass of grouping relevant data for you.

Of course, you want to still review the data, check that the themes tally with your intuition and context, but boy it’s so much easier dealing with small groups of data, versus 1000 random sticky notes (breaks into sweat at the thought 😅).

Many AI thematic analysis tools have started to offer AI-assisted tagging, which can cluster tags automatically for review. Although this technology is still evolving, it holds immense promise in streamlining data analysis processes.

Drop an email here if you want to be part of our AI thematic beta program!

4. Organize your data by question 

Not only do great AI qualitative analysis tools extract data for you, but they can group them by your interview questions for easy review.   

Tools like Looppanel auto-organize notes based on your discussion guide or interview questions. Just toggle the topic you’re interested in exploring, and voila! The notes and highlights related to it are in one place.

Since answers are automatically extracted for each interview, you can easily view answers across calls as well.

Looppanel's Analysis dashboard
Looppanel's AI research analysis and tags dashboard

5. You don’t miss important data

If you’re relying on scrappy notes, you’re probably hoping you haven’t missed anything important. 

With tight timelines, you may not have time to go over transcripts and recordings with a fine-toothed comb.

Luckily, AI is a thorough, reliable worker. It’ll not miss out on a single byte of information, and process everything with computer precision.

6. Gut check on bias

Subjective bias is a big risk with qualitative analysis. AI can help by standardizing coding, and checking bias.

It’s like a super-smart, third party research assistant to cross-check all your interpretations, and offer a second opinion.

While not yet perfect, AI can achieve impressive accuracy rates, significantly reducing the time and effort required for thematic analysis. 

Best Practices & Limitations for AI Qualitative Analysis

Here’s what you need to avoid, if you are using AI-powered tools like Looppanel and ChatGPT for user research analysis and qualitative coding.

1. AI is your assistant, it cannot replace you 

AI supplements human expertise; it doesn't replace it entirely.

AI is your research assistant. It can help you code faster, but you still hold the context to interpret notes and clusters accurately.

You should check any AI-generated data and expect it to be a great starting point—not blindly take it as the final answer.

2. AI lacks context

AI tools completely lack worldly context, and don’t have access to a lot of data about your work. They will miss out on obvious points that you, with years of experience  at your company, intuitively understand. 

3. AI needs some supervision

You cannot feed an entire project’s worth of data to AI and expect fully formed final insights. 

Well, you could—but you the odds that it could go sideways are high.

AI is not that smart (yet).

That’s why with Looppanel, we make sure AI output is always traceable and editable by you.

Because AI can make mistakes, you need to be able to check where this note came from, edit a cluster, or add your own takeaways manually.

4. Don’t expect AI to answer the whys

AI is good at spotting patterns in user behavior, like a detective sifting through clues. However, it struggles to grasp the "why" behind those behaviors. That's because human actions are influenced by a colorful mix of personal preferences, cultural norms, and situational factors. AI might tell you what users are doing but unraveling the why remains a puzzle it can't fully solve.

So, limit its role to just observing the user behavior and pointing out the patterns and themes within. You will have to do the analysis and making sense of the whys and how this affects your organization. 

4. Feed it all the necessary data, as accurately as possible

Imagine AI as the brightest student in class, who answers your questions. But it doesn’t have the ability to answer anything outside the syllabus. 

AI’s excellence depends on the data you give it. If your data is incomplete, inaccurate, or skewed, well, it's like giving it an incomplete set of notes for an exam. It might not provide the answers you're hoping for, simply because it doesn't have the full context.

5. Data security and compliance is a concern

When signing up to use AI qualitative data analysis tool, check the terms and conditions for their data security measures. Being brand new territory, legal and ethical regulations for AI tools are still catching up. 

Do a quick check for the data security measures implemented, GDPR compliance etc.

For example, at Looppanel here are some of the security measures we take:

  • 🔐 Data is encrypted and stored securely
  • ❌ We only use summarization models that do not use your data for training purposes
  • 🛡️ GDPR compliance for data privacy

If you have questions about Looppanel’s data security measures or what you should watch out for in terms of security, you can always reach out to us at

AI Qualitative Data Analysis Tool

Ready for a super-smart AI assistant to help with qualitative analysis?

We have the perfect one for you.

Looppanel is an AI-powered research repository tool that can:

You don’t have to believe me.

Try it out for FREE, and see how you feel!

Share this:

Get the best resources for UX Research, in your inbox

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Related Articles