Qualitative data analysis is done with the help of analytical tools to extract meaning from large volumes of non-numeric data, including images, reports, audio, and video. It’s used in researches and other areas of the business where the client behaviour needs to be understood at a deeper level to meet their expectations.
Businesses mostly like to work on the qualitative research platform as it is frequently thought as more human-friendly than quantitative data, which enables them to identify potential for product expansion and improve customer acquisition and retention techniques. The techniques used in qualitative data research can help researchers draw insights from the data, identify trends, and better understand the dynamics of the data. These insights help the businesses make an informed decision that is purely client-centric.
Despite some of the stark differences between qualitative and quantitative analysis, a lot of people get confused between the two. However, the two are used for research purposes, but the difference lies in data collection and reporting.
Quantitative Data Analysis vs. Qualitative Data Analysis
As the name suggests, quantitative data works on the quantity and analysing numerical data (present in the form of graphs, tables, or charts) to find patterns and trends is known as quantitative data analysis. In order to systematically measure variables and report the collected data in the numerical form, quantitative research uses numbers and statistics.
On the other hand, qualitative data analysis is the process of analysing and extracting insights based on non-numerical or textual data. In contrary to quantitative data which is easy and straightforward to interpret, qualitative data is generally more “open-ended” and might be challenging to interpret and present.
Quantitative data cannot offer insights that qualitative data can. For instance, qualitative data can help you understand why the client wants a particular product and what drives their buying decision.
Now that you have understood the difference between qualitative and quantitative data analysis, let’s discuss the ways of qualitative content analysis. There are two ways of qualitative data analysis, manually and automatic. Both yield the same result but have different approaches.
|Qualitative Analysis||Quantitative analysis|
|Analyses the ‘why’ of a certain phenomenon||Analyses the ‘how much’ of a certain phenomenon|
|More about the non-statistical data||More about the quantifiable data|
|Data is about the non measurable things like color, gender, nationality, region and more.||Data is about the measurable quantities like length, depth, number and more|
|The results are not presented in numbers as they are not measurable||The results are depicted through graphs, tables, charts and more.|
|Interprets and understands social interactions based on studies||Test hypothesis and predicts the future|
|Research methodology is exploratory||Research methodology is conclusive|
Let’s first learn about the methods of qualitative data analysis
Methods of Qualitative Data Analysis
There are 5 main methods of qualitative data analysis
1. Qualitative Content Analysis
A systematic study of a content to derive particular characteristics or trends is known as content analysis. This might from an interviw over the phone, survey reports, or feedbacks from clients. Since content analysis is a faster and simpler, anyone with a solid comprehension of the data can do it.
The insights taken from the content analysis are also easier to understand and comprehend.
2. Narrative Analysis
Analysing and interpreting the customer’s or research participant observation when narrated by them, are all part of using narrative analysis to analyse qualitative data. Customer interviews or testimonies can be used to gather the data.
By using narrative analysis, product managers may better understand how customers feel about their products, find gaps in the supply and demand, look for recurring patterns in their behaviour, and change or better the in-app experiences they receive.
3. Grounded Theory Analysis
It is well-knonw methodology employed in various research studies. Qualitative researchers analyse and construct the data based on the real world theories. It is based on the particular process that seeks to comprehend how customers engage with their goods. Additionally, it can be used to come up with predictions for future consumer behaviour.
4. Thematic analysis
A popular technique for analysing qualitative data that reveals patterns and themes in the data is thematic analysis. It is a method for analyzing qualitative data obtained through study of a data set to discover, analyze, and report repeated patterns. In order to do a thematic analysis, the data must be coded, or given themes or categories. Hence the term, thematic analysis.
5. Discourse Analysis
It is about understanding the language more than just a sentence and how people speak with one another to understand how it functions in a social context. It can be used to analyse both spoken and written language. Discourse analysis is a great tool to understand how customers discuss their products online.
Analysing Qualitative Data Manually
There are five steps involved in manual qualitative data analysis:
1. Get Your Data Ready
Before beginning with the research, it is vital to gather the notes, documents, and other resources that can give you a headstart. Mark the source of the data, any data point you may have collected, or any other information that will help you examine your data.
Since it is a manual research, majority of businesses get their data from traditional data collection techniques such as discussion groups, focus groups, questionnaires, and interviews. Usually, databases,, CRMs, and papers are used to hold this data. Based on the breadth of your research, it’s important to consider which data is actually available and should be used.
2. Organise and Investigate the Data
To understand what is there in your data, you will need to read it to make notes about your ideas, questions, and opinions. This can be stored in excel spreadsheets that are typically shared by the research teams. Because each team gathers and arranges the data in a manner that works best for them, the feedback is frequently kept separately.
3. Produce the Codes
Use anything that will help you make a connection with your facts, such as highlighters, sticky notes, or comments in the margins. Here is how you can do it manually
- To gain a general understanding of what the data reveals, read it more than once. It’s time to begin putting your first set of codes on statements and text chunks.
- Ensure that everything once it has been coded and there are no discrepancies neither the data is missing.
- For your codes, make a code frame to organise all your codes.
- By collecting the data, you might observe the recurring patters in your feedback data based on the frequency of a specific code.
4. Review the Codes and Find Valuable Insights
Determine recurrent topics, viewpoints, and beliefs. It is better to have sub-codes for your principal codes at this point as massive coding can be confusing. This will enhance the calibre of your analysis even though it takes time.
Customers are segmented by several businesses into age, demography, behaviour, and more. Additionally, you might already have your own respondent groups that you can use in your qualitative analysis. Observing the frequency of codes within your segments is especially helpful. It can be helpful to focus elsewhere if one of your customer segments is undervalued by your company but accounts for the majority of customer care issues.
5. Deliver Reports In a Logical Order
In order to best convey the story of your statistics, take into account your audience, the goal of the study, and the appropriate content to present. Condense these findings into a series of graphs, tables, and other graphics to represent them better.
Analysing Qualitative data: Automatic
Unlike the manual data analysis, automatic data analysis through software is easy, faster and produces error-free results. Let’s take a look at qualitative data analysis using software:
1. Gather Qualitative Data
The majority of businesses have now eployed in Slack chats, emails, chatbots, support ticketing systems which give the businesses additional benefits to get customer feedback while also enabling the mass collection of unstructured feedback data. Social media platforms like Twitter and Facebook along with online communities, review sites, and forums also offer useful qualitative data.
2. Organise All your Data
Earlier, developers popularised CAQDAS software, which researchers quickly got used to with the coding and organisation of data. Qualitative data is loaded into CAQDAS software for coding.
Software for computer-assisted qualitative data analysis has the following advantages:
- Allows you to investigate several interpretations of your data analysis
- Helps with the organisation of your data
- Facilitates group cooperation
- Makes it easier to share your dataset
3. Coding the Data
Several software options can facilitate boost this procedure. Here are some exampes.
CAQDAS – This software includes built-in capability that enables text coding within the programme. Thee software’s UI makes managing codes simpler than using a spreadsheet.
Dovetail: Transcripts and other textual data can be tagged using Dovetail. It is easier to maintain the coding on a single platform because they are also repositories.
Ascribe – The software has a code management system called “Coder.” Managing your codes is simpler because of its user interface.
4. Analysing the Data
The automatic extraction of codes and subcodes from the data is made possible by automated text analytics tools. It is easy to figure out what’s causing either negative or positive results quicker and simpler with this tool. It can also help in identifying new trends and uncover insightful information in the data.
The built-in sentiment analysis feature of AI-driven text analytics software offers the emotional context for your feedback and other qualitative data, which is an additional advantage.
5. Reporting the data
Platforms for feedback analytics include visualisation capabilities that makes it easy-to-understand the graphs of important data and insights. Automatic construction of these graphs of charts frees up time to concentrate on developing an engaging narrative that highlights the insights for executive teams to examine.
Also Read: Ways to Conduct Primary Research
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