Definition of qualitative data
Qualitative data is data that cannot be objectively measured or counted, or data that expresses the subjective and interpretive qualities of an item or process. While quantitative data is almost always expressed numerically, qualitative data can come in a variety of formats, including written words, audio clips, and video clips. Particularly when qualitative data is collected from human responses, the data set brings back highly assorted results with fewer straightforward methods of measurement and analysis than those found in quantitative analysis.
Some examples of qualitative data include:
- The training programs that most interest your employees.
- The social security numbers of the students in your classroom.
- The typical career background of a retailer’s top buyers.
- The types of cars that people drive in Los Angeles.
- The details in a celebrity’s video testimonial.
How is Quantitative Data Different?
Quantitative data is different from qualitative data because it is driven by numeric values that can be counted or measured, while qualitative data is exactly what it sounds like: data that speaks to the qualities of a surveyed population or data set.
Although there does not necessarily have to be a limit to the range of quantities that can be returned through quantitative data collection, there are limits and parameters in the sense that quantitative research must bring back a quantity rather than a word or open-ended response.
Some types of data can easily be confused with quantitative data. A good example of qualitative data that looks quantitative is categorical data. Categorical data is a type of data that has been collected qualitatively, and then thematic analysis has been applied to group the qualitative data into categories.
You can now count the categories and how many pieces of data are found in each group, but you still cannot classify the data as quantitative data. It remains qualitative because the groups are measured by a researcher’s interpretation of open-ended responses and the categories they should fall into. The data is still “words,” or qualities rather than numbers.
It’s also important to note that just because data contains a number or numbers, that does not necessarily qualify it as quantitative data. Consider a person’s phone number. While the data consists of numbers, it is still subjective. It relates to only one person and can fluctuate, or be interpreted differently by a different collector of this data.
Someone might identify Jason’s phone number by his cell phone number, his office number, his home phone number, or an outdated phone number that he no longer uses, meaning that Jason’s phone number can be interpreted differently depending on who you ask and when or how you ask them to obtain that data.
Another reason why this data should not be considered quantitative is that there is no real “value” to count, measure, or include in statistical analysis. If you were to analyze several phone numbers in a structured data set, finding the average or median of those numbers does not produce valuable insight into what those numbers mean.
How Can You Collect Qualitative Data?
Qualitative data can be collected in several ways, and may even be unintentionally collected in some cases. What many people don’t realize is that the responses in an interview, or even the conversational points from several people in a friend group, are pieces of qualitative data that can be interpreted and analyzed when the right questions are asked and parameters for analysis are set. Some of the most common ways to collect qualitative data include:
- Asking open-ended survey questions or questionnaires.
- Asking questions during an interview.
- Forming focus groups and discussion groups through Socratic seminars.
- Reviewing existing information, particularly about customers and prospects in a CRM database.
More on the Value of Customer Data: Modern Companies Need a Customer Data Platform: Build or Buy?
Why is Qualitative Data Important?
Qualitative data may be more difficult to identify and analyze than the data you obtain through quantitative research, but the insights are just as important. Qualitative analysis allows you to ask questions that cannot easily be quantified, which teaches you more about the “who,” “why,” and “how” behind the data you are collecting.
Qualitative data builds a strong foundation of knowledge for researchers who want to learn more about a population or data set, and asking those qualitative questions at the beginning often gives a better grasp on what quantitative data can be collected and will be most valuable in combination with the qualitative foreknowledge. This qualitative foundation provides high value, especially for companies that are attempting to understand their customers and how to prospect more customers effectively.
Identify Your Buyers and Their Buyer’s Journey with Qualitative Analysis.
You can learn a lot about your buyers purely through quantitative analysis, especially if you use a CRM that allows you to track engagement on marketing tools like email campaigns, social media posts, and digital ads, and web traffic on blogs and conversion landing pages. However, this quantitative knowledge tells you how marketing tools are doing overall, but doesn’t give you much insight into the “who” or “why” that is most valuable when prospecting customers and identifying their specific needs. Knowing qualitative features about your digital visitors helps you to answer several questions like:
- Is this person an actual prospect, or are they a current customer, current employee, competitor, or bot? CRMs collect several qualitative pieces of data, such as a person’s associated company and the types of inquiries they’ve previously made on your site. All of this data saves your marketing and sales teams time when determining who to call and what offers to make.
- Who has visited your conversion page recently and how many times have they visited? If you see that Sally Jo Hansen has visited the page 37 times in the past week, that quantitative data shows you that she’s interested, but something is stopping her from converting into a buyer. This quantitative data about Sally Jo is invaluable, however, it doesn’t mean much without the qualitative data: her name is Sally Jo Hansen and her email address is email@example.com. With both the quantitative and qualitative data in hand, your marketing and sales team now have the measurable background information and the contact information they need to do targeted outreach on this prospect.
Qualitative data is often overlooked, and many people don’t even realize that the words and expressions they read constitute a form of data. But with qualitative data, especially in combination with quantitative data, you can develop deeper insights into the meaning of the content in front of you.
Read Next: Data Governance Best Practices