This is part of a new Looppanel blog series on AI and UX Research. For more on AI Qualitative Research, read the rest of the series of articles on AI for UX Design & Research, and 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 AI for research, with another 38% planning to join them in the future. Key use cases include AI qualitative data analysis, AI-supported discussion guide generation, re-writing reports with AI, and ideation with AI.
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:
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).
What are the 5 methods to analyze qualitative data?
There are 5 common methods of analyzing qualitative data: content analysis, narrative analysis, discourse analysis, thematic theory analysis, and grounded theory analysis. In this article, we'll mainly focus on thematic analysis—how you can do this manually, or with an AI qualitative data analysis tool.
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
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.
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.
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.
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.
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. Having AI to partner with can give you a different perspective to consider.
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 data 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—how to use AI for qualitative data analysis, and how it speeds things up!
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 you use ChatGPT for qualitative data analysis? You can, but would take a lot of work, context providing and training (more on that below!). However, AI-powered tools specifically meant for qualitative analysis are another story.
AI qualitative data 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.
AI is really good at processing 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.
So how can we use AI for qualitative data analysis?
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.
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 data 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.
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.
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!
Not only do great AI qualitative data 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.
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.
Subjective bias is a big risk with qualitative analysis. Standardizing AI qualitative coding, and providing a different perspective can be helpful.
It’s like a super-smart, third party research assistant to cross-check all your interpretations, and offer a second opinion.
There's a range of tools to try out for AI qualitative data analysis—some are purpose-built for qualitative research, while others are general purpose tools you can use for analysis.
Here are top 3 picks with step-by-step guides on how you can leverage them for AI qualitative data analysis.
Looppanel is built like an AI-powered research assistant—it'll automate all the tedious, manual parts of the job you don't have time for. Key AI features include:
If you're looking for a product that's easy to use and packages all the benefits of AI within your research workflow, look no further. Just sign up, paste in your discussion guide and upload your calls—Looppanel will do the rest!
Free Trial: Yes
Pricing: Starting $30 / month
It is possible to use ChatGPT, Microsoft Co-pilot or the equivalent tools you may have access to to support your analysis process. There are some limitations though:
What's the best way to use ChatGPT for AI qualitative data analysis?
4 steps:
Free Trial: Yes
Pricing: GPT 3.5 is free! The quality is far below GPT 4 though, which costs $20 / month
If you're working in an academic setting and need tons of customization to analyze your data, Atlas.ti is worth exploring.
The product is built more for manual analysis, relying heavily on coding transcripts by hand but has recently forayed into adding AI features to their toolstack.
Key AI features:
Free Trial: Yes
Pricing: Commercial license from $50 / month
Here’s what you need to avoid, if you are using AI-powered tools like Looppanel and ChatGPT for user research analysis and AI qualitative coding. Aka how NOT to use AI for qualitative data analysis!
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.
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.
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.
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.
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.
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:
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 support@looppanel.com.
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.
Looppanel automatically records your calls, transcribes them, and centralizes all your research data in one place
