When did you last ask your users to understand their needs and wants?
If you're like most companies, it's probably been a while. You've got lots of data, but getting anyone excited about it takes a lot of work.
That's where qualitative data analysis comes in. It's a research method that helps you better understand your users' experiences, thoughts, and feelings by analyzing non-numerical data such as interview transcripts, surveys, and observations.
Qualitative data analysis is valuable for improving products and services and can help you make better decisions by providing a more comprehensive view of your users. However, it can be overwhelming for beginners to know where to start and how to analyze the data effectively.
In this blog, we'll learn the basics of qualitative data analysis, including the different types of data you can use and how to analyze your data effectively.
Let's get started.
Table of Content
What Is Qualitative Data?
Qualitative data refers to the information that describes or explains a person’s experiences, feelings, attitudes, and behaviors.
Examples of this qualitative data include,
- Responses from surveys, interviews, and observations,
- Written responses from users.
Qualitative data is subjective, meaning it can’t be measured or quantified. It consists of words and sentences that describe a person’s thoughts and feelings.
Qualitative data is usually collected through interviews, focus groups, surveys, and other forms of human-subject research.
Difference Between Quantitative And Qualitative Data
As the name indicates, qualitative data is more detailed and focuses on the "WHY".
In contrast, quantitative data is more into the numbers and focuses on the what.
It means that you can use qualitative data to understand the motivations behind customer behaviors, while quantitative research is better for predicting how many customers will make confident buying decisions.
Quantitative and qualitative data differ in what they reveal about a situation, how they are gathered, and the techniques used to analyze them.
Here are the key differences between these two data types:
But remember, it's not "quantitative data or qualitative data." It's "quantitative and qualitative data," as both data types are essential to marketers.
Quantitative data helps you predict how many people will buy your product, while qualitative data lets you understand why they're buying it. So when collecting and analyzing data, ensure you're using both!
What Is Qualitative Data Analysis?
Qualitative data analysis is the process of interpreting, analyzing, and understanding people’s responses to your survey questions. It helps you discover what people think and feel about the topic(s) you’re researching. Qualitative data analysis can be used to understand your customer base better, refine marketing campaigns, or even create new ones!
Now you might have confused between qualitative data and qualitative data analysis.
Simply put, analysis is the process of interpreting and making sense of qualitative data that you have collected using surveys and interviews. It involves identifying the data's patterns, themes, and categories and making inferences based on those findings.
For example, a company wants to conduct market research to understand why customers prefer a competitor's product over theirs. They might use qualitative research methods like focus groups or interviews to collect data on customers' experiences and attitudes towards both products. These are now qualitative data that are collected and stored.
Analyzing this data to identify common themes and patterns that emerge from the responses is qualitative analysis.
Why Is Qualitative Data Analysis Important?
For any marketer, understanding their customers' needs, wants, and desires are critical for successful marketing campaigns. Here is where the qualitative data analysis comes in. It can provide deeper insight into the problem than quantitative methods alone.
But that's not the only reason; here are some fundamental reasons why qualitative data analysis is essential.
1. Gain Deeper Insights About Your Audience.
You will only get straightforward yes or no answers with a poll or review. And as a marketer, more is needed to know whether your customer loves or hates your product or service. You want to understand why they feel the way they do. Qualitative data analysis allows you to dig deeper into the reasons behind their answers and gives you a better understanding of what they really want.
These authentic answers are goldmines for understanding your audience and how you can improve your business.
2. Capture Diverse Perspectives
Qualitative data analysis can capture a range of perspectives and experiences that may be overlooked in quantitative studies. As a marketer, you want to know your customers' thoughts and feelings about your brand.
You want to understand their motivations and challenges to serve them better.
“Qualitative analysis is important for a business because it gives me a better sense of what's happening behind the scenes and why things are happening the way they are. With it, you can make better decisions about how your company should change to improve its performance.”
Matt Benton, CEO at Noding
And we do agree!
Qualitative data analysis allows you to capture diverse perspectives by including users whose experiences may not have been included in the original study design or who may be overlooked in quantitative studies.
3. Improve Your Decision-Making Process
Whenever you have to make a decision, you should always look at both sides of the coin. Qualitative data analysis allows you to collect data that is not gathered easily with other methods. It can give you an in-depth understanding of what motivates people and how they feel about a product or service—something hard to get from simply asking them questions.
At the end of the day, all you need is a solid product that people love to use, isn't it?
How To Perform Qualitative Data Analysis?
At this point, you know that qualitative data analysis is integral to any marketing research project. But how do you actually perform qualitative data analysis?
The answer is there are multiple ways to do it, which you can categorize under these five types,
- Content Analysis,
- Thematic Analysis,
- Narrative Analysis,
- Grounded Theory Analysis,
- Discourse Analysis.
Now let's look at how you can perform qualitative analysis based on these five types.
1. Content Analysis
It is precisely what it sounds like. The data is sorted and summarized according to two standards: verbal (descriptive) and behavioral (prescriptive).
After the data have been collected, it is tabulated. The analysis of this content depends entirely on what has been gathered from the interviews—relationships and patterns are usually extracted to reach conclusions.
How to do the content analysis?
- Gather data: Collect all the data you need, such as interview transcripts, social media posts, or survey responses. To collect and organize data from scattered sources, you can use tools like Olvy, as it helps you gather your users' opinions from all over the internet—and label them appropriately.
- Read through the data: Read through the data to get a general idea of what it's about.
- Identify themes: Look for themes or patterns in the data. Themes are recurring ideas or concepts that emerge from the data.
- Code the data: Assign codes or labels to the data that relate to the identified themes.
- Categorize the data: Group the data based on the codes or labels.
- Analyze the data: Look for relationships, patterns, and trends in the data. Draw conclusions based on your analysis.
- Validate your findings: Check your findings by reviewing the data and looking for evidence that supports or contradicts your conclusions.
2. Thematic Analysis
This type of analysis looks for patterns in the data that reveal the underlying themes. It’s a qualitative approach, so you’re not using numbers or statistics to analyze your data. You can think of thematic analysis as an art form—you must use your creativity and imagination to see how people express themselves within their stories.
The best thing is that thematic analysis is a flexible and adaptable method used across various projects. It allows for a deep and detailed understanding of the data, making it an effective tool for uncovering complex issues and themes.
How to do the thematic analysis?
- Familiarize yourself with the data: Read and re-read it to understand it.
- Generate initial codes: Create codes or tags to label the data related to key concepts, ideas, or themes. With Olvy, you can categorize your data based on the sentiment, type, and product segment to closely monitor your user's data.
- Identify themes: Look for patterns or clusters of codes related to each other and represent a theme.
- Review themes: Examine them to ensure they are coherent and consistent with the data.
- Define and name themes: Provide a clear definition and name for each theme that accurately reflects the content of the data.
- Write the report: Summarize the findings, including quotes and examples that illustrate each theme, and draw conclusions based on the analysis.
3. Narrative Analysis
Narrative analysis is a research method that uses stories to make sense of the data. It's a practical approach in situations where quantitative methods may not fully capture the essence of what people are saying or feeling. By identifying common themes and patterns within your data, you can develop an understanding of how people feel about your brand.
How To Do The Narrative Analysis?
- Convert the narratives to transcripts: Transcribe the interviews or focus groups into written transcripts.
- Read: Read the transcripts several times to familiarize yourself with the data.
- Look for common themes: Identify key storylines or themes from the data.
- Segment them using codes: Code the data according to these themes using descriptive tags or labels.
- Create sub-themes: Organize the coded data into categories or subthemes.
- Identify the relationships: Look for patterns or relationships between the categories.
- Create the final report: Develop a narrative summary of the data, highlighting the key themes and patterns.
4. Grounded Theory Analysis
Grounded theory analysis is a qualitative research method that uses data to generate a theory. It focuses on generating theory from data rather than confirming existing theories. It does not explicitly seek to explain why people do what they do or how they do it; instead, it aims to describe what people do accurately.
You can use it to study a particular phenomenon or process and discover new theories based on collecting and analyzing real-world data.
How To Do Grounded Theory Analysis?
- Data collection: Gather data through interviews, observations, or documents.
- Open coding: Start with an open mind and read through the data to identify and label emerging concepts or themes. You should give each concept or theme a name or label.
- Axial coding: Look for relationships between the concepts or themes identified in the open coding stage. Categorize and organize these relationships into subcategories.
- Selective coding: Synthesize the subcategories into a theory. Identify a main category that links all the subcategories and provides an overarching explanation for the phenomenon under investigation.
- Verification: Test the theory against new data to see if it holds up. Refine the theory as necessary.
5. Discourse Analysis
It is a process for exploring how people talk about something. It’s an essential tool for understanding how consumers think about products and brands, their motivations, and what they want from them. This type of analysis examines how language is used in advertising, brand messaging, and consumer reviews to shape perceptions of a brand.
How To Do Discourse Analysis?
- Collect data: Gather a range of texts related to the brand or product, including advertising copy, social media posts, product descriptions, and customer reviews.
- Identify discursive themes: Analyze the texts to identify key themes or topics in the language used. These themes can include ideas related to product quality, customer service, or brand values.
- Examine language use: Look closely at the specific language used to construct these discursive themes. For example, analyze the use of metaphors, rhetorical devices, and persuasive language.
- Interpret meaning: Analyze the overall meaning constructed through the language used in the texts. Consider how the language is used to construct a particular reality around the brand or product and how consumers might perceive this reality.
- Conclude: Use the insights gained from the discourse analysis to draw conclusions about the effectiveness of the brand's messaging and identify opportunities for improving brand perception and consumer engagement.
Wrapping Up
Qualitative analysis is a fundamental approach to research that allows for a deeper understanding of the complexity and richness of human experiences, behaviors, and social phenomena. By analyzing data systematically and rigorously, you can conclude the effectiveness of your brand’s messaging and identify opportunities for improving consumer engagement.
The next step is to select the best method for doing qualitative analysis. As there is no one size fit for all approach, you must choose the suitable method based on your research objectives, data characteristics, and budget.
Whatever you do, ensure you use the proper method to get actionable insights relevant to your business objectives. With Olvy, you can collect and analyze customer feedback under one roof. With one click, you can push your user's insights/feedback to Olvy and share them with your team to analyze, prioritize, and take actionable decisions.
At the end of the day, what matters the most are your users. So ensure you conduct analysis with the right tools, get the right insights and take actionable decisions based on them.
