Ordinal Data: Definition, Examples, Collection, and Analysis

Companies are investing more in data tools to help their marketing teams make better decisions.

But marketing needs more than just data and analytics tools to implement an effective marketing strategy. They also need to understand the type of data they collect and how to analyze it to gain useful insights.

It involves going back to basics and understanding ordinal data, one of the main types of marketing data. This article explores ordinal data and how it informs data-driven marketing decisions.

What is ordinal data?

Ordinal data is quantitative data in which the variables are organized into ordered categories, such as a ranking from 1 to 10. However, the variables do not have a clear interval between them and the values ​​of the ordinal data do not not always have an equal distribution.

The customer satisfaction level is an example of ordinal data. Its variables could be:

Very satisfied Satisfied Neutral Unhappy Very unsatisfied

Using ordinal data, you can calculate frequency, distribution, mode, median, and range of variables.

After defining ordinal data, you may wonder about other data types, such as nominal, interval, or ratio data. How are they different from ordinal data? Here are some quick definitions:

Nominal data is a classification of data whose variables have a finite set of values ​​and categories that are unordered. With nominal data, you measure variables such as job type, which has multiple outcomes, such as self-employed, full-time, or hybrid. Interval data is a data type where the interval between two values ​​is not constant. Interval data appears in several ways, for example when measuring time intervals or when the difference between two measurements varies. The most common way to represent interval data is to use a table with columns for the upper and lower bounds of each range. Ratio data is a type of data used for statistical analysis. Ratio data does not provide any information about the values ​​it represents. This information should be obtained from other sources referenced by the ratio data. It is often used in the analysis of financial information but can also be applied to other types of data. 5 examples of ordinal data

Ordinal data appears in different formats. Here are some examples of ordinal data and how to sync it with your business strategy to improve your data management efforts.

1. Level of interest

Whether you have already launched your product in the market or introduced new features to your existing product, you will need to conduct market research to ask questions to gauge the interest of your target audience.

Market research involves the analysis of qualitative and quantitative data to understand customer needs, their buying partners, and what motivates them to buy from you. This information can help you improve your marketing campaigns in the future.

For example, if you host regular conferences, polls can help you know how well you did and whether your attendees want to attend the conference again. Here is an example of interests data:

Source: SurveyMonkey

The questions you ask will reveal the level of interest potential customers have in your product or service. Interest levels range from not interested, not very interested, neutral to very interested.

2. Level of education

This type of ordinal data provides information about the skill level of your target audience.

Education level can ask you whether your target audience has acquired different levels of formal education, such as high school, college, and college. You can collect this data by assigning numbers to each level, such as 1 for no formal education, 2 for primary education, etc., up to 10 for a university doctorate.

Education-level data is useful when using analytics in your recruitment process to help you evaluate applications from potential candidates.