5 qualitative data analysis methods

Before you choose a qualitative data analysis method for your team, you need to consider the available techniques and explore their use cases to understand how each process might help your team better understand your users. 

This guide covers five qualitative analysis methods to choose from, and will help you pick the right one(s) based on your goals. 

What is qualitative data analysis? 

Qualitative data analysis (QDA) is the process of organizing, analyzing, and interpreting qualitative data—non-numeric, conceptual information and user feedback—to capture themes and patterns, answer research questions, and identify actions to take to improve your product or website.

5 qualitative data analysis methods explained

Here are five methods of qualitative data analysis to help you make sense of the data you've collected through customer interviews, surveys, and feedback:

1. Content analysis

2. Thematic analysis

3. Narrative analysis

4. Grounded theory analysis

5. Discourse analysis

Let’s look at each method one by one, using real examples of qualitative data analysis.

1. Content analysis

Content analysis is a research method that examines and quantifies the presence of certain words, subjects, and concepts in text, image, video, or audio messages. The method transforms qualitative input into quantitative data to help you make reliable conclusions about what customers think of your brand, and how you can improve their experience and opinion.

You can conduct content analysis manually or by using tools like Lexalytics to reveal patterns in communications, uncover differences in individual or group communication trends, and make connections between concepts.

How content analysis can help your team

Content analysis is often used by marketers and customer service specialists, helping them understand customer behavior and measure brand reputation.

For example, you may run a customer survey with open-ended questions to discover users’ concerns—in their own words—about their experience with your product. Instead of having to process hundreds of answers manually, a content analysis tool helps you analyze and group results based on the emotion expressed in texts.

Some other examples of content analysis include:

• Analyzing brand mentions on social media to understand your brand's reputation

• Reviewing customer feedback to evaluate (and then improve) the customer and user experience (UX)

• Researching competitors’ website pages to identify their competitive advantages and value propositions

• Interpreting customer interviews and survey results to determine user preferences, and setting the direction for new product or feature developments

Content analysis benefits and challenges

Content analysis has some significant advantages for small teams:

• You don’t need to directly interact with participants to collect data

• The process is easily replicable once standardized

• You can automate the process or perform it manually

• It doesn’t require high investments or sophisticated solutions

On the downside, content analysis has certain limitations:

• When conducted manually, it can be incredibly time-consuming

• The results are usually affected by subjective interpretation

• Manual content analysis can be subject to human error

• The process isn’t effective for complex textual analysis

2. Thematic analysis

Thematic analysis helps to identify, analyze, and interpret patterns in qualitative data, and can be done with tools like Dovetail and Thematic.

While content analysis and thematic analysis seem similar, they're different in concept: 

• Content analysis can be applied to both qualitative and quantitative data, and focuses on identifying frequencies and recurring words and subjects.

• Thematic analysis can only be applied to qualitative data, and focuses on identifying patterns and ‘themes’.

How thematic analysis can help your team

Thematic analysis can be used by pretty much anyone: from product marketers, to customer relationship managers, to UX researchers.

For example, product teams can use thematic analysis to better understand user behaviors and needs, and to improve UX. By analyzing customer feedback, you can identify themes (e.g. ‘poor navigation’ or ‘buggy mobile interface’) highlighted by users, and get actionable insight into what users really expect from the product. 

Thematic analysis benefits and challenges

Some benefits of thematic analysis: 

• It’s one of the most accessible analysis forms, meaning you don’t have to train your teams on it

• Teams can easily draw important information from raw data

• It’s an effective way to process large amounts of data into digestible summaries

And some drawbacks of thematic analysis:

• In a complex narrative, thematic analysis can't capture the true meaning of a text

• Thematic analysis doesn’t consider the context of the data being analyzed

• Similar to content analysis, the method is subjective and might drive results that don't necessarily align with reality

3. Narrative analysis

Narrative analysis is a method used to interpret research participants’ stories—things like testimonials, case studies, interviews, and other text or visual data—with tools like Delve and AI-powered ATLAS.ti.

Some formats narrative analysis doesn't work for are heavily-structured interviews and written surveys, which don’t give participants as much opportunity to tell their stories in their own words.

How narrative analysis can help your team

Narrative analysis provides product teams with valuable insight into the complexity of customers’ lives, feelings, and behaviors.

In a marketing research context, narrative analysis involves capturing and reviewing customer stories—on social media, for example—to get more insight into their lives, priorities, and challenges. 

This might look like analyzing daily content shared by your audiences’ favorite influencers on Instagram, or analyzing customer reviews on sites like G2 or Capterra to understand individual customers' experiences.

Narrative analysis benefits and challenges

Businesses turn to narrative analysis for a number of reasons:

• The method provides you with a deep understanding of your customers' actions—and the motivations behind them

• It allows you to personalize customer experiences

• It keeps customer profiles as wholes, instead of fragmenting them into components that can be interpreted differently

However, this data analysis method also has drawbacks:

• Narrative analysis cannot be automated

• It requires a lot of time and manual effort to make conclusions on an individual participant’s story

• It’s not scalable

4. Grounded theory analysis

Grounded theory analysis is a method of conducting qualitative research to develop theories by examining real-world data. The technique involves the creation of hypotheses and theories through the collection and evaluation of qualitative data, and can be performed with tools like MAXQDA and Delve.

Unlike other qualitative data analysis methods, this technique develops theories from data, not the other way round.

How grounded theory analysis can help your team

Grounded theory analysis is used by software engineers, product marketers, managers, and other specialists that deal with data to make informed business decisions. 

For example, product marketing teams may turn to customer surveys to understand the reasons behind high churn rates, then use grounded theory to analyze responses and develop hypotheses about why users churn, and how you can get them to stay. 

Grounded theory can also be helpful in the talent management process. For example, HR representatives may use it to develop theories about low employee engagement, and come up with solutions based on their findings.

Grounded theory analysis benefits and challenges

Here’s why teams turn to grounded theory analysis: 

• It explains events that can’t be explained with existing theories

• The findings are tightly connected to data

• The results are data-informed, and therefore represent the proven state of things

• It’s a useful method for researchers that know very little information on the topic

Some drawbacks of grounded theory are:

• The process requires a lot of objectivity, creativity, and critical thinking from researchers

• Because theories are developed based on data instead of the other way around, it's considered to be overly theoretical, and may not provide concise answers to qualitative research questions

5. Discourse analysis

Discourse analysis is the act of researching the underlying meaning of qualitative data. It involves the observation of texts, audio, and videos to study the relationships between the information and its context.

In contrast to content analysis, the method focuses on the contextual meaning of language: discourse analysis sheds light on what audiences think of a topic, and why they feel the way they do about it.

How discourse analysis can help your team

In a business context, the method is primarily used by marketing teams. Discourse analysis helps marketers understand the norms and ideas in their market, and reveals why they play such a significant role for their customers. 

Once the origins of trends are uncovered, it’s easier to develop a company mission, create a unique tone of voice, and craft effective marketing messages.

Discourse analysis benefits and challenges

Discourse analysis has the following benefits:

• It uncovers the motivation behind your customers’ or employees’ words, written or spoken

• It helps teams discover the meaning of customer data, competitors’ strategies, and employee feedback

But it also has drawbacks: 

• Similar to most qualitative data analysis methods, discourse analysis is subjective

• The process is time-consuming and labor-intensive

• It’s very broad in its approach

Which qualitative data analysis method should you choose?

While the five qualitative data analysis methods we list above are aimed at processing data and answering research questions, these techniques differ in their intent and the approaches applied. 

Choosing the right analysis method for your team isn't a matter of preference—selecting a method that fits is only possible when you define your research goals and have a clear intention. Once you know what you need (and why you need it), you can identify an analysis method that aligns with your objectives.