Thematic analysis: an overview
When you have a lot of text feedback, like open-end survey responses, app review comments or twitter threads, how do you begin to make sense of it all? One of the best approaches is to uncover themes in the data using thematic analysis.
Thematic analysis is a systematic method of analyzing qualitative data. It enables you to find rich, useful insights quickly, and organizes your data so that you can easily see context.
Qualitative data is inherently unstructured, with people contributing ideas and feedback in the way that is natural to them. It’s a conversation, rather than a list of points. To get valuable insights, companies need to structure their feedback data and filter out the noise and filler data.
In this overview, we’ll break down the jargon, and explain how thematic analysis works. We'll briefly cover some manual approaches, and take a deeper look at thematic analysis software. We’ll also give some examples of how leading companies use thematic analysis to better understand their feedback data.
Qualitative data and themes
As soon as you start looking at feedback or text analysis, you'll see the term qualitative data. So what counts as qualitative data? Simply put, it's information collected from text, audio and images that can't be easily expressed using numbers.
If you have 100 one-star reviews of a product, you know something's going wrong. But ratings in isolation can't provide a fix. You need to look at the comments to understand context and identify the problem. The one-star rating is quantitive data, and the comments are qualitative data.
This is where thematic analysis comes in. Using this methodology, you can go through the comments and pull out the common themes to zero in on the answers you need.
What's a theme? In thematic analysis, a theme is assigned to a piece of data to summarize its meaning. When it comes to feedback, a theme is a collection of different ways people talk about the same thing.
The process of labeling and organizing your qualitative data to identify different themes and the relationships between them is called coding. Themes can be combinations of words, phrases or numbers.
What are the advantages of thematic analysis?
- Simple to learn and apply
Thematic analysis is straightforward and easy to learn. Everyone can use either manual or automated approaches to generate useful themes from their datasets.
- Goes beyond the surface
Thematic analysis allows you to dig deep, with themes referring to the thoughts, motivations and ideas behind your data.
Thematic analysis is a flexible approach which can be applied to a wide variety of sample sizes, data types, and study questions.
- Comprehensive bottom-up approach
With thematic analysis there is no need to set up categories in advance. Allowing themes to emerge from your data means you’ll capture unknown unknowns. This bottom-up approach allows you to jump right into your data without doing a lot of preparatory work.
What are the challenges of using thematic analysis?
- One-off cases
Topics that only occur once in your data might be overlooked, as thematic analysis focuses on identifying patterns. This could be significant if you have a smaller data set or an important issue that has only been mentioned once.
- Capturing the exact meaning
Since thematic analysis is phrase-based, sometimes the exact meaning isn’t captured correctly. You can tackle this issue by carefully evaluating and reviewing themes to make sure they match up to the meaning behind your data. Combining AI and a human eye is a powerful way to ensure your analysis results are accurate.
- Human error and bias
Manual approaches can introduce errors or bias into your results and create problems with consistency. It’s natural to highlight themes that support your existing beliefs. One fix is to choose an automated solution. These use NLP (natural language processing) to automatically identify themes. This can help increase consistency across themes and reduce bias.
Tools for thematic analysis
Let’s walk through some of the tools you can use for thematic analysis. These range from manual approaches to powerful AI solutions which automate the entire analytical process.
If you’re planning to do your thematic analysis manually, affinity mapping can help streamline the process. Affinity mapping groups data into clusters based on their similarities.
One effective way to create a thematic map manually is by using sticky memo notes. Once you’ve grouped together certain phrases or sentences, it will be easy to see the overarching themes in your data.
CAQDAS solutions for coding
CAQDAS stands for Computer Assisted Qualitative Data Analysis Software. It’s commonly used by researchers to help them with qualitative data analysis.
Instead of creating codes with pen and paper, you upload your data and create codes directly within the CAQDAS software. You can easily filter your data by code, which makes it easier to review your codes and evaluate your findings.
Examples of CAQDAS solutions that can be used for thematic analysis include ATLAS.ti, NVivo, MAXQDA, and QDA Miner. In the example below you can see the codes that have been selected in the right-hand panel.
AI-powered SaaS solutions
Automating thematic analysis with AI is your best option when working with feedback at scale.
You'll get results quickly and efficiently, improve consistency, and reduce the errors and bias that occur with manual analysis.
If you have large quantities of data to analyze, or you want to streamline the process, SaaS solutions are king. They save you significant time and effort without compromising on accuracy.
How does the software make sense of customer language?
AI-powered software solutions like Thematic use an algorithm to automate the thematic analysis process. This algorithm learns on its own and identifies themes you might have missed. Thematic uses Natural Language Processing (NLP) and deep learning to analyze unstructured data like text. All you need to do is connect your source data.
How does thematic analysis work in Thematic?
We’ve broken down the analysis process into five broad steps to give you an easy overview. These steps closely resemble the stages a researcher follows when performing analysis manually.
Step one: Find meaningful phrases by scanning the text
First up is finding meaningful phrases by scanning the text. There’s a lot of ‘noise’ in customer feedback, so our AI pre-cleans the data. It finds comments or phrases that are useful to your company, and ignores what's not relevant, like greetings and non-feedback chat.
Let’s have a look at a piece of feedback like “Thank you for fixing chat in landscape mode, and the navigation is easy to use.”
The meaningful phrases here are “fixing chat”, “chat in landscape mode”, “landscape mode”, “navigation”, “navigation is easy”, and “easy to use”.
These phrases can be insightful when analyzing all of the feedback. But at this stage, we can’t know for sure, because a theme only emerges when multiple people mention it.
Step two: Turn phrases into themes
Next, our AI uncovers themes from the phrases used in your dataset. Natural language processing is key here: Thematic can quickly see when people are saying the same thing, even when they use different phrases.
For example, all three people below speak positively about fixing chat in landscape mode and the easy to use navigation:
- Thank you for fixing chat in landscape and the navigation is easy to use
- Glad horizontal chat is back and the new menu is user-friendly.
- Yay, chat in landscape mode works now. It’s easy to navigate around the app.
In a manual thematic analysis approach, you would repeat these first two steps several times to check all the phrases have been captured, and captured in the right way. Thematic does this automatically.
Step three: From themes to taxonomy
Our AI now groups the themes into a hierarchy, also known as a taxonomy or a code-frame. In Thematic, the AI scans the long list of themes, along with the context in which the theme was mentioned, and suggests a hierarchy which you can accept or refine.
How the themes are grouped and organized depends on other themes in the dataset. For example, the sub-theme “easy to use navigation” could be grouped under a base theme of “ease of use” or a base theme of “navigation”. If customers also talk about other features that are easy to use, it’s more likely to be grouped with ease of use. Or if they talk in-depth about navigation as well as ease of use, the broad theme is more likely to be navigation.
Step four: Fine-tuning the themes
Next, we double check each theme has enough data to support it. Each theme must be distinct, useful and actionable.
Thematic enables you to visually trace themes to phrases and raw feedback quotes, so that anyone can look at the feedback for context. This is important to build understanding and empathy with customers, as well as for quality checking.
The capability to easily refine and quality control themes is paramount in all thematic analysis, and no less important when powered by AI. Since the context isn’t always clear from the piece of feedback, the AI may occasionally miss the exact meaning of a phrase, requiring some theme editing.
Within Thematic, you can merge similar themes, and remove themes that lack enough data to back them up. This step tailors the analysis to meet your specific product, marketing or operations team use case. It also ensures trust, as you can check for accuracy and usefulness.
Step 5: Using the analysis to uncover insights
Now it’s time to identify actionable insights and discover what your customer feedback data tells you.
In Thematic, as part of thematic analysis, themes are quantified so you can see top mentioned themes. When you include scores, such as CSAT or app rating, in the data variables, Thematic analyzes and shows you which themes most affect satisfaction or ratings.
A review of the top themes, and the context in which they arise, will help you to form a narrative and identify key insights. With statistical analysis tools and filters, you can dig into the main themes impacting NPS or customer satisfaction, and understand key issues your customers are raising. You can choose vivid, relevant quotes from customers to back up your points.
There’s a strong element of interpretive analysis in forming a narrative and finding insights, so you can draw on the driver analysis, statistical changes and relationships between the themes. Dashboards pull all of this together, so you can share a summary with stakeholders and make an argument for your insights.
How companies use thematic analysis
Thematic analysis is widely used by researchers and companies to better understand their data. Insights found via the analysis show how you can improve your product, customer service, or employee experience.
Here’s how thematic analysis can be applied in the real world:
Drive product roadmaps
Thematic analysis is awesome when it comes to analyzing subjective feedback like product reviews. The analysis can focus on product related issues and opportunities, with themes that deepen insights for the product team.
Think new and existing products, improving or evaluating particular features, or taking direction from feedback for future iterations. Finding these types of insights enables you to validate your product and create data-driven product roadmaps.
Imagine you’re part of a camera company with thousands of product reviews. You need to quickly learn which features users do and don’t like. Using thematic analysis, you can see what you need to work on to increase customer satisfaction. These practical insights translate into targeted improvements for product design and delivery.
Data-driven conversations with stakeholders
Teams need easy ways to communicate directly from their data and get stakeholders onboard with future plans. When product managers use insights from thematic analysis in presentations and key meetings, they can explain exactly what’s informing product direction. Showing your transparent, data-driven decision processes makes it much easier to get buy-in from the rest of the company.
Identify areas for improvement in the customer experience
Thematic analysis can also be used to analyze experiences, thoughts and behaviors across a customer feedback data set.
Working with an on-demand food delivery service, Thematic looked at why users were leaving the platform, and why some markets weren’t growing as quickly as others. Thematic analysis helped pinpoint the problem areas, leading to a 5.4 point increase in NPS and faster growth.
Analyzing diverse unstructured datasets
Thematic analysis really shines when it comes to getting insights from unstructured data: customer conversations, social media posts, or open-ended survey responses. Many companies have a wide range of datasets that they need to analyze accurately and efficiently, and thematic analysis is a winning solution.
Atom bank was the first app-only bank in the UK and number 1 rated UK bank on Trustpilot. They wanted to learn how to improve customer experiences, and how to grow their customer base. The challenge: numerous types of customer feedback, including surveys, customer complaints and online reviews.
Using Thematic, Atom bank was able to apply thematic analysis across all their different datasets, leading to valuable insights. With a comprehensive understanding of their customers and their experiences, Atom bank made impactful improvements to their product roadmap and corporate strategy. They reduced calls relating to unaccepted mortgage requests by 69%, and calls relating to saving maturities by 43%: the two main reasons why customers contacted the bank.
Thematic analysis is a great tool to better understand what your customers care about. Data-driven insights help companies improve products and services in line with customer needs, and help marketing and sales teams develop more targeted strategies. Thematic analysis can also be used to track how new initiatives are impacting how your customers feel about your product or service.
Melodics is an app that teaches you to play MIDI keyboards, pad controllers and drums. They wanted to find out if customers were interested in music theory, and if they should include it in their lessons. Melodics used Thematic to analyze their customer survey responses. The thematic analysis revealed that music theory wasn’t frequently mentioned, so they knew not to prioritize it for their product roadmap.
Improve support team efficiency
Thematic analysis can help companies understand which issues their support teams are spending most of their time on. They can then prioritize these issues for improvement, so that their support teams don’t have to repeat the same scenarios over and over. It’s a win for users, who have a better experience, and a win for the support team, who become more efficient.
Blend qualitative and quantitative data
Thematic analysis of qualitative data can be used in conjunction with quantitative data for deeper insights. This might be your NPS (Net Promoter Score) or other key metrics like sales figures, customer demographics, or performance statistics.
Try combining specific themes with your NPS to better understand how to improve customer satisfaction. You can find out more about how to calculate your NPS here.
Another approach: analyze how much time your customer support team spends on particular topics. The insights will tell you which areas take up most of their time, so you’ll know how to improve user experience.
Understand employee feedback
Understanding employees is essential to retaining talent and maintaining good employee engagement levels. Thematic analysis can help you understand what drives engagement and contributes to both great and negative employee experiences. These insights can be delivered to departments or managers for company wide improvements.
DoorDash is the largest and fastest-growing on-demand prepared food delivery service in the US. The company used Thematic to analyze qualitative employee engagement feedback. Leveraging the insights they found boosted the company’s Employee NPS (eNPS) by 12 points. Over 150 users were able to get reports on team-level eNPS scores, making it possible for company-wide changes.
Thematic analysis is a great analytical method for getting rich insights. It can help you understand what really matters to customers, and give you a clear roadmap for improving your products and services.
Now you know the basics of thematic analysis, the next step is to decide which approach is best for you. For some individuals and companies, it might make sense to go manual. For companies with larger quantities of data from multiple sources, a good option is a SaaS tool like Thematic.
Thematic’s automated feedback analysis platform makes it easy to analyze all your customer data in one place. Keen to see how Thematic works on your data? Request a personalized guided demo right here.
You can find out more about analyzing customer feedback in these guides:
- How to code qualitative data
- How to code and analyze open-ended questions
- Guide to thematic analysis software
- How to analyze customer and product reviews
- How to analyze survey data
We also have some free feedback tools and resources that may help you: