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Anyone who's taken a statistics class is probably already familiar with qualitative and quantitative data. One includes numerical values and the other describes qualities or traits. One can be counted, and the other cannot, plain and simple. But wait, are we forgetting another type of data?
Categorical data takes qualitative data and divides them into specific categories. It seems complicated at first glance, but as you read further, you'll find that it's the most simple form of data to collect and analyze.
In this guide, we'll go over the details of categorical data, how it differs from quantitative data, its applications in the business world, and much more.
Categorical data is defined as qualitative data divided into distinct groups or categories. This is different from numerical data, which consists of numbers that can be calculated. Categorical data focuses on classification instead of values that can be added, subtracted, or measured.
For example, if you had a collection of different fruits, categorical data would organize them according to specific characteristics, such as color or fruit kind.
Categorical data primarily branches into two distinct types: nominal and ordinal data. Each type has specific characteristics and applications, emphasizing the importance of understanding the differences to use the most appropriate analytical approaches and accurately interpret the results.
The two main types of categorical variables are:
Nominal data, also known as "label" or "named" data, is the most basic type of categorical data. The main characteristic of nominal data is that it distinguishes between things or subjects based on name or category without assuming any kind of order or ranking among the categories.
Essentially, nominal data labels and categorizes data, but the categories have no underlying numerical value or hierarchical position.
Ordinal data differs from nominal data as it not only categorizes and labels but also assigns a clear order or ranking to the categories. This form of categorical data is distinguished by the relative order of the categories, implying a hierarchy or sequence. However, the separations between these ranks or places are not always equal or quantifiable.
With its unique characteristics and analytic approaches, categorical data is essential to many fields, such as business intelligence, research, and statistics. Handling and interpreting this kind of data effectively involves an understanding of its features.
Here's a closer look at the key features of categorical data:
Categorical data is widely used in a variety of sectors and scenarios, showing its broad applicability and effectiveness. Here are some situations demonstrating how categorical data is used in real-world contexts:
Numerical data, often known as quantitative data, is information presented in numerical form and can be quantified or measured. This type of data is characterized by its ability to undergo mathematical operations, such as addition, subtraction, multiplication, and division, allowing a wide range of statistical analysis.
Numerical data is used in a variety of industries, including science, finance, and social research, as a basis for quantitative analysis, projections, and decision-making processes.
Quantitative data is divided into two types: discrete data and continuous data. Each type has distinct properties that make it suitable for different analytical methods.
Discrete data consists of countable numbers representing unique entities or categories that cannot be split. The main feature of discrete data is that it contains a finite or countably infinite number of values. This type of qualitative data is typically represented by whole numbers.
Some examples of discrete data include the number of students in a classroom, the number of cars in a parking lot, and the number of books on a shelf.
Continuous data can have any value within a specific range, including fractions and decimals. This form of data is measured rather than counted, and it often involves observations or measurements that fluctuate indefinitely along a continuum.
Examples of continuous numerical data include the height of individuals, weight produce in the grocery store, and the temperature of a room throughout the day.
Categorical data is qualitative in nature, meaning it describes attributes or properties that are not inherently numerical. On the other hand, numerical data (sometimes known as quantitative data) deals with numbers and values that can be measured or quantified.
You need to understand the differences between categorical and numerical data to be able to each type effectively. Let's take a closer look at how these two types of data differ:
Categorical data provides valuable information that businesses can use to guide decision-making, improve customer experiences, and optimize operations. Here are some of the most common applications of categorical data in business:
Categorical data helps organize goods into specific categories such as apparel, electronics, and home goods, which improves site navigation and search functionality. This categorization enables targeted marketing methods and simplifies the shopping experience for customers by making it easier to find the products they want.
Additionally, customer segmentation enables customization of marketing efforts and recommendations based on purchasing habits, preferences, or demographics, thereby increasing conversion rates and promoting client loyalty.
Categorizing transactions based on their type, such as online purchases or in-store pickups, also provides a deeper understanding of customer behavior. This benefit allows businesses to fine-tune their inventory management and logistics strategies.
Customer service benefits significantly from the strategic application of qualitative data. Businesses can speed up the resolution process by categorizing customer inquiries and complaints and routing them to the right departments. Plus, this categorization helps optimize service ticket prioritizing by ranking issues based on urgency or severity, assuring that the most crucial concerns are addressed first.
Customer surveys are a vital tool in measuring customer satisfaction and preferences. These surveys often ask customers to rate their level of satisfaction across multiple characteristics of the product or service, using categories like "very satisfied" to "dissatisfied," to help businesses identify operational strengths and weaknesses.
Plus, asking customers to select their preferences from a specified list of categories can provide meaningful insights into current consumer trends and preferences, which can prove extremely useful for adapting products and services to better suit market demand.
Categorical data collection is an essential part of generating insights that guide business decisions and improvement efforts. You can collect this type of data using different methods, such as:
Collecting qualitative data for your business often requires multiple approaches to cover a wider scope of people and categorical variables. Unfortunately, this can take up a lot of time, which you may not have enough of.
This is where web analytics tools like FullSession come in handy. FullSession automatically collects both qualitative and quantitative data from your website that can help you optimize multiple aspects of your business. Plus, it can translate categorical data into quantitative data for easier statistical analysis.
Don't let data collection intimidate you. With FullSession, you can gather different types of categorical data and gain deeper insights into what you can improve for a better conversion rate.
The FullSession platform offers a 14-day free trial. It provides two paid plans—Basic and Business. Here are more details on each plan.
It takes less than 5 minutes to set up your first website analytics tool with FullSession, and it's completely free!
Categorical data is crucial for businesses as it aids in market segmentation, customer profiling, and trend analysis, enabling targeted marketing strategies and informed decision-making.
Yes, businesses can use categorical data to classify customer inquiries and feedback, which helps in prioritizing and efficiently addressing customer needs.
Absolutely. By categorizing sales, customer feedback, and service quality, businesses can identify performance trends and areas for improvement, guiding strategic adjustments.