Corporations, large and small, amass a great deal of qualitative data. Customer feedback, interviews, support tickets – how do they analyze it all?
They use Qualitative Data Analysis Software.
Qualitative researchers used to perform their work manually. Conducting research today without technology is the equivalent of bringing a knife to a lightsaber fight.
Let’s get down to brass tacks. Qualitative Data Analysis tools enhance the UX workflow. They expedite cumbersome manual processes, allowing researchers and designers to focus more on analysis.
Want to learn more? Marvin’s here to help on your journey!
Here’s our no-frills guide to qualitative data analysis software. [We promise to try using fewer Star Wars references.]
TL;DR – Best Qualitative Data Analysis Software
There’s no shortage of programs for qualitative data analysis on the market. Companies spoiled for choice have a tough time whittling their options down. So how do they decide?
Let’s understand more about how to choose the right tools and what to look out for.
Highlighting their pros and cons, we’ll examine the following tools closely:
- Marvin
- ATLAS.ti
- Cauliflower
- Deedose
- Delve
- MAXQDA
- NVivo
- Qualtrics
Our recommendation? Glad you asked!
Marvin is fast gaining popularity in the UX community. With a simple and eye-catching UI, it’s easy to navigate through the application.
We’re constantly rolling out new features that delight users. We believe listening to our customers eventually enhances their user experience.
Read on to learn why researchers and designers call Marvin a ‘game changer.’
What is Qualitative Data Analysis Software?
We won’t wax lyrical about why Qualitative Research matters. Suffice to say that non-numerical, qualitative data is the lifeblood of good research and design.
Qualitative Data Analysis Software facilitates the gathering, structuring, and interpreting of qualitative data. Researchers identify trends or patterns in contextual data from various formats. They seek to understand users’ thought processes – the “why” behind their actions.
This software allows researchers to automate aspects of the research process, such as:
- Transcription. Tools capture user interactions from audio and video, automatically producing a verbatim transcript with multi-language support.
- Coding and Annotations. Tag your qualitative data and cluster textual data into themes. Query data and connect pieces of evidence across projects.
- Analysis. AI now plays a role in analysis. It can handle data far beyond what a human can. Use AI to conduct preliminary analysis – the output still needs human review. It has many applications across the analysis process, including (but not limited to):
- Statistical. Uncover trends, patterns, and correlations between variables.
- Sentiment. Interprets and classifies data to understand people’s attitudes.
- Textual. Text mining extracts high-quality information from your research.
- Media analytics. Analyze data from various media types or sources such as images, text, audio, and video. Link meaningful insights back to source data.
- Data reporting and visualization. Summarize and present findings. Spot trends and patterns and discover links across projects. Map and track the customer journey.
Use qualitative research data analysis software to unearth insights from unstructured data. Insights inform decision-making, allowing UX professionals to create experiences that delight users. Isn’t that the whole point, anyway?
8 Best Qualitative Data Analysis Software
All qualitative data platforms aren’t created equal.
As the software does, we’ve performed the heavy lifting for you. We scoured the web for the best software for analyzing qualitative data.
Put these 8 tools to the test and forget about the rest (just kidding):
1. Marvin
A centralized UX research repository that houses both qualitative and quantitative data. Marvin integrates with apps that designers and UX researchers love. Connect data from Zendesk, Qualtrics, Asana, Miro, and Notion (among others).
Connect to popular video conferencing tools – Marvin offers transcription support in over 40 languages. It supports seamless workflows. AskAI is a search engine within Marvin that allows you to ask questions about your data across the entire repository. Search for key insights effortlessly.
Supercharge your analysis. Marvin automatically surfaces key trends from data. It summarizes lengthy interviews and highlights key takeaways, so you don’t have to pore over transcripts. Examine your data within the powerful Analyze section with a global view of all tags.
Create stunning visualizations and reports. Stitch together highlight reels from audio and video files. Share files in various formats with stakeholders who’d benefit from insights. Consumers of research love hearing directly from users.
All your research in one place. That’s Marvin.
Pros
- Industry-leading AI features
- Multiple language support
- Wide-ranging integrations
- Highly intuitive UI
- User-friendly
- Collaborative – share insights with cross-functional teams & stakeholders
- Highly responsive customer support
Cons
- Limited quantitative statistical analysis
Best for: One-size-fits-all. Teams of 2 to 20,000 use Marvin! Set up your free demo with Marvin today.
2. ATLAS.ti
A powerful tool that assimilates customer and product data under one roof. Collect, structure, and evaluate customer feedback within the application. Atlas caters to a varying set of users who all collaborate on a single platform.
User surveys to collect feedback directly from users. Perform prototype testing and refine wireframes. Represent data with charts and visualizations. Atlas has incorporated AI features, including autocoding. Import data from Twitter or popular note-taking apps such as Evernote (among others).
Pros
- AI features – auto coding, summarization, and chatbots
- Clean UI
- Strong integrations
Cons
- Expensive for small teams
- Coding features aren’t intuitive
Best for: Large Research teams and Academic Institutions
3. Cauliflower
A no-code qualitative data analysis tool. Conduct and analyze open ended survey data with Cauliflower. Examine social media content to understand customer reviews. Cauliflower has ready-made visualizations to help identify themes.
Cauliflower’s AI-powered analytics help correlate pieces of customer feedback. Features include no-code text analysis using an integrated chatbot. Automate conversations with customers to generate leads and provide 24/7 support. Collect feedback without human intervention.
Pros
- AI-powered chatbots
- Automates data and lead collection
- High level of customization
Cons
- High quality training data required
- Quality of AI results vary widely
- Complex tool to learn
Best for: Companies who want to automate customer conversations
4. Deedose
This software can handle both quantitative and qualitative data but with limitations. Import data from various file formats including audio, visual, text and spreadsheets. Its robust tools help during data coding, charting, and visualization.
Learn when to use Qualitative Research vs. Quantitative Research.
A major downside is the lack of transcription. They make it easy to integrate with a transcription app of your choice though. Deedose’ cloud-based platform makes remote access easy. A team-oriented application, collaborate effortlessly with your peers.
Pros
- Easy collaboration through the cloud
- Affordable
- Mobile compatible
- 100% Web-based
Cons
- Limited multimedia analysis
- No AI features
- No transcription
Best for: Students, Academic Researchers (UCLA academics created Deedose)
5. Delve
Delve is a no-nonsense qualitative data analysis tool solely focused on data coding. It’s especially beneficial for analyzing long-form customer interviews. Create and group data into themes and use its AI Assistant to suggest new subcodes.
Slice and view data from different dimensions – such as demographics or codes. Delve specializes in thematic analysis but is helpful for all kinds of qualitative analysis. The company curates helpful material (and even an online course!) to get you started with qualitative coding.
Pros
- Easy-to-learn and use
- Flexible pricing
Cons
- Limited AI features
- Limited collaborative features
- Limited functionality – a purely thematic analysis tool
Best for: Students or Researchers beginning their journey
6. MAXQDA
A versatile mixed methods data analysis tool that’s been around since 1989. Export and analyze a wide range of data sources. These vary from interviews, reports, tables, surveys, focus groups, and handwritten text to tweets. Get deeper insights using both quantitative and qualitative data.
MAXQDA allows you to easily search and retrieve information. Structure surveys both open and close-ended. Organize and categorize unstructured data. Visualize data with word clouds and more. Create lit reviews and work with bibliographical information. It offers transcription as well.
Pros
- Easy-to-use
- Multimedia functionality
- Advanced Visualization
- Inexpensive
- Supports multiple file formats and languages
Cons
- Collaboration isn’t easy
- High IT resources required
- No real-time syncing
- Limited free version
Best for: Education, Nonprofits and Small Organizations
7. NVivo
A popular tool for qualitative and mixed method research. Organize and structure qualitative data before analysis. Import audio and video files, and NVivo will automatically transcribe them. Use this to transcribe sales calls, customer interviews, product demos, and social media content.
Analyze these files in-house to find recurring themes. NVivo offers autocoding (which does exactly what it says). A caveat – researchers must perform analysis after running autocoding. Powerful visualizations using word clouds and concept trees.
Pros
- Relationship coding
- Query data feature
- Clean UI
Cons
- Expensive
- Difficult for beginners
- No transcription in-house
- Limited language support
Best for: Tech-savvy Researchers or Academics, Product teams at mid to large companies.
8. Qualtrics
An all-in-one customer experience management platform. Qualtrics XM helps remove uncertainty from product development. Gather data from various customer touchpoints and analyze it in one place.
Qualtrics offers a great variety of analytics. Gauge user sentiment from textual data. Visual tools such as session replays create a comprehensive view of the user experience. XM uses powerful predictive analytics to ensure customer opinion is at the heart of decision-making.
Validate product ideas, identify market opportunities, and fix broken customer journeys. Real-time data collection and analysis facilitates agile decision-making. Bonus: Qualtrics integrates seamlessly with Marvin.
Pros
- Real-time data
- Predictive analytics
- One-stop shop for UX
- Numerous integrations
Cons
- Complexity
- Limited data coding capabilities
Best for: Large Enterprises and Research teams dealing with multiple data sources
How to Choose the Best Qualitative Data Analysis Software
Every company is different. Each has different resources, capabilities, and needs when choosing qualitative data analysis software. Be mindful of these as you evaluate tools and how they fit into your organization.
Follow these steps:
- Understand Requirements. What problem are you trying to solve? How will the application fit into the company’s workflow? What does a successful implementation look like? If you can’t measure it, you can’t improve it…
- Establish Criteria. What features are you looking for? What functionality is essential, and what’s ‘nice-to-have’? How helpful is their customer support when you get stuck?
- Identify Users. Which teams or employees will use the application? What are their needs? Are there different types of users – power or view-only?
- Explore Integrations. Does the application fit in with your existing tool stack? What applications must integrate with the new software? Is this process easy or cumbersome?
- Test with Free Trials. After evaluating tools capabilities and business needs, it’s testing time. Pilot a small project and ask users to evaluate the software in action. What did they find easy or difficult?
- Review and Decide. Involve primary users and stakeholders in the decision process. What did users like about each software? Which one would they vote for? Come to a consensus.
Keep these questions in mind to pick the tool that suits your business needs best.
Best Practices to Organize and Code Data in Qualitative Data Analysis Tools
Coding or tagging data makes it easier to understand and categorize user feedback. Tagging makes data analyzable, discoverable, and shareable.
Each research project is unique – there isn’t one specific way to tackle coding data. Having said that, there are some tricks to add efficiency to the coding process.
Here we go:
- Start with Output. What questions do you want to answer? For some questions, you may have an idea of the output (close-ended responses). Create tags for these to save time.
- Draft a Discussion / Question Guide. An effective discussion guide includes questions that help you navigate an interview. Tag your questions! It provides a starting point on how to group your codes.
- Data Preparation. Organize and tag data before analysis. Create foundational labels for overarching topics. Create codes for structured questions in advance (Like scale/radio buttons). Code personas to distinguish between user groups.
- Go Granular. Mihaela Vorvoreanu, or ‘Mickey,’ is the Director of UX Research Education for Aether at Microsoft. She suggests always coding with more granularity. That way, it’s easier to collapse codes than to go back and recode data.
- Refine Tags. Use a global view of your codes to clean them up. Collapse codes and merge overlapping ones. Educate other researchers on how to tag data correctly.
- Visualize Data. Use graphs and charts to visualize data. Viewing data in this form helps crystallize your thinking and build narratives. Anyone can look at a graph and make conclusions about your study.
- Stay Up-to-Date. Companies roll out new coding features at a rate of knots. Stay learning!
For more information, head over to our guide on tagging user research insights.
Frequently Asked Questions (FAQs)
Here, we tackle some frequently asked questions about qualitative data analysis software:
Is HeyMarvin Suitable for Large Research Projects?
Marvin gobbles up research data from any source and helps churn out rich insights, no matter the data volume.
Don’t take it from us. We’ll let Marvin users demonstrate how it handles large datasets:
At Microsoft, Mickey and team (from above) created a Responsible AI Maturity Model. They interviewed 47 AI practitioners and amassed over 80 hours of interview and focus group footage.
To dissect the data, they used Marvin to create a constantly changing codebook. Aether synthesized over 2000 notes to create the framework.
“If you can do this kind of complex project and this kind of complex data analysis with Marvin, you could probably do it with a lot easier projects, things that are not quite as hairy,” Mihaela said.
Entertainment Partners handles all things finance in the entertainment industry. Head of Research Beth Seaman handles terabytes of data every day.
“What HeyMarvin does for me is it allows me to very quickly see patterns in large amounts of data and then communicate those out,” she said.
In the pre-Marvin era, she had to sift through individual surveys manually.
“I don’t think I ever knew how bad it was until I figured out what Marvin can do for me,” she said. “It has changed my existence.”
What is the Best Data Analysis Method for Qualitative Research?
We’ll go with a researcher’s favorite answer — it depends.
Qualitative research involves several methods of analyzing data. The most common methods are listed below:
- Content. Transforms verbal and behavioral data into quantitative output. Helps classify data to give reliable conclusions about users and their opinions.
- Thematic. Identifies, interprets, analyzes, and categorizes patterns in qualitative data.
- Narrative. Examines different user stories and reformulates them based on the context of each case. The revision of primary qualitative data by a researcher.
- Discourse. Studies relationships between information and the social context.
- Grounded theory. Formulates a theory from a single case. Further examination of real-world case studies is needed to see if they contribute.
Each has its own applications and merits. To determine the method, identify what questions you want answers to. Qualitative data analysis software doesn’t favor one particular data analysis method. Due to the overlapping nature of qualitative data, software will work for most research methodologies.
What AI Apps are Best for Large Qualitative Data Sets?
AI brings efficiency and scale to qualitative research. It frees up a researcher’s time to dive deeper into analysis. Learn how AI makes qualitative research powerful.
Some AI applications handle large volumes of qualitative data better than others.
To deal with large qualitative datasets, we recommend using the following:
- Marvin
- ATLAS.ti
- MAXQDA
- NVivo
- Qualtrics
Let AI handle the drudgery of research and spend more time on analysis with these apps.
Conclusion
You have learned well, padawan (we couldn’t resist!).
Qualitative data analysis software helps you make sense of mountains of data. Turn raw, unstructured data into actionable insights.
It’s essential to consider your business needs before settling on a tool. Test each one and ask researchers which tool they think works best. Since AI is the talk of the town, future-proof your choice of tool with AI features. Implement these best practices for coding data to expedite the process.
We’d love to show you Marvin’s capabilities. Sign up for a free demo today. Use Marvin to understand qualitative data with ease.
