What is categorical data? Definitions, examples & differences.

What is categorical data?

Categorical data, also known as qualitative data, shows information that can be sorted into groups or categories, rather than measured numerically. It describes characteristics rather than quantities. For example, when used in marketing surveys, these categories could reflect countries and regions, genders, ages, and even brand and social media platform preferences.

These categories help marketers segment audiences, tailor campaigns, and analyse trends effectively. While categorical data can’t be averaged like numbers, it’s important when understanding customer behaviour and making targeted marketing decisions.

Types of categorical data.

There are many different types of categorical data – not all of them are the same or collected for the same purpose. Each data type has several different characteristics and uses.

For example, some data may be more fitting for survey responses, while other types work well for collecting customer data.

It’s not as simple as labelling all categorical data as simply qualitative or representative of a group – it’s much more diverse than that.

Nominal data.

Nominal data is a sub-category of categorical data that is used to name different variables. Derived from the Latin ‘Nomen’, which translates to ‘name’, it’s also sometimes called ‘labelled’ or ‘named’ data. It’s used to collate more specific information – like name, age and hair colour – as opposed to wider qualitative categories.

This type of data is mostly used in questionnaires and surveys, and allows people to input their own descriptive responses, rather than simply selecting a relevant answer or category. This means the information collected is often more accurate and specific, so is great for collecting customer insights and grouping data, but not so much for ranking.

Ordinal data.

As the name suggests, ordinal data has a clear, ordered ranking or sits on a scale. However, the differences between these ranks aren’t measurable or comparable.

For example, you might ask customers to rank their experience on different levels like ‘very unsatisfied’, ‘neutral’, ‘satisfied’, and ‘very satisfied’. Or you may need to know the highest level of education someone has completed – ‘GCSE’, ‘A-Level’, ‘Degree’.

The order of ordinal data helps you to interpret preferences, opinions and ratings in relation to experiences, your brand, and its products and services. However, the difference between these levels and preferences isn’t necessarily uniform due to their subjectivity.

Binary data.

Binary data only has two possible categories or values, often representing two extremes. For example, ‘yes’ or ‘no’, ‘true’ or ‘false’, ‘present’ or ‘absent’.

Within marketing, binary data is used and collected when assessing whether a customer has taken a certain action. Have they subscribed to your newsletter? Have they made a purchase?

It’s a simple way of segmenting your audience, tracking conversions and analysing consumer behaviours. It helps you make clear, data-driven decisions based on distinct and certain outcomes. For example, if you know someone hasn’t subscribed to your newsletter, you know whether to target them with certain paid social content or email prompts.

Categorical data uses.

Categorical data plays a vital role in many areas of marketing, business research and everyday decision-making. It helps you group, sort, and understand data in ways that inform targeted actions, and assists in building more personalised customer experiences.

The data and its sub-sets can be used across a wide range of different industries, and for many different purposes, including:

Retail.

In retail, categorical data can be used to personalise marketing campaigns, optimise product inventory and refine product recommendations. For example:

• Customer gender data can be used to tailor promotions and product suggestions.
• Clothing sizes (S, M, L) can be referred to when optimising stock levels for popular sizes.
• Product categories (electronics, fashion, home) can help to segment email campaigns by shopping interests.

Healthcare.

You can use categorical data to support and inform patient care plans and welfare journeys, build on public health research, and effectively allocate resources. For example:

• Patient blood type data is collected to build donor and recipient databases.
• Diagnosis categories (e.g. diabetes, asthma) can help to track treatment outcomes by illness.
• Smoking status (yes/no) is used to identify lifestyle-related risk patterns.

Advertising.

Categorical data has many uses in advertising, including understanding channel and campaign effectiveness, optimising ad and content delivery, and better targeting demographics. For example:

• Device type (mobile/desktop) data can help you adapt content and campaign formats for better engagement.
• Campaign source (email/social/organic) information allows you to collect and analyse performance and ROI data.
• Location and region data allows for geo-targeted ads and campaigns, which can increase local relevance.

Education.

You can use categorical data in education to improve both student and teacher experiences. It can be used in curriculum planning, student profiling, and even when allocating funding and resources. For example:

• Course type (online/in-person) categories can help track learning preferences and accessibility.
• Qualification levels (GCSE, A-Level, degree) allow you to tailor course offerings and marketing materials.
• Understanding student support needs (e.g. SEN status) can help you customise learning resources.

Finance and banking.

Although the finance and banking industries might usually be associated with quantitative data, categorical data can be used for a variety of purposes. Use it to improve fraud detection, build customer retention strategies, and model certain product offerings and services. For example:

• Account types (personal/business) enable you to provide relevant products and services.
• Risk categories (low, medium, high) can inform lending decisions.
• Customer segments (e.g. young professionals, retirees) help you target specific demographics with financial advice and products.

What is the difference between categorical and continuous data?

The difference between categorical data and continuous data lies in the type of values they represent and how they can be used.

For example, categorical data:

• Is qualitative
• Puts data into categories, groups or labels
• Represents qualities and characteristics, not measurable numbers. E.g.
• Gender – male/female/non-binary
• Region – North Yorkshire/West Midlands
• Payment method – credit card/PayPal
• Is used to classify or group information for segmentation and analysis.

Meanwhile, continuous data:

• Is quantitative
• Represents any numerical value within a data range
• Can be broken down into smaller parts (decimals, fractions). E.g.
• Age – 29.5 years
• Income – £32,500
• Height – 182.3 cm
• Is used for statistical analysis, trends, averages and precise measurements.

Categorical vs continuous data examples.

Many data categories are relevant across both categorical and continuous data. Here’s how they differ: