What Is Ordinal Data? Definition, Examples, and Analysis

One of the fastest ways to increase the workforce productivity, efficiency, and competitiveness in a small-and-midsize-business is to adopt a data-driven culture. Companies investing in aggregating, analyzing, and integrating data are creating new ways to improve customer experience and generate more revenue.

One common data management system is ordinal data, a data classification system with a natural order or ranking. It's a qualitative data set that exists naturally in sequentially occurring ordered categories. In this article, we'll discuss how to collect and analyze this qualitative data.

Ordinal data is a type of qualitative data that organizes categories in a sequential way. Ordinal data examples could be satisfaction level, education, or socioeconomic status Ordinal data values follow a natural order, but the differences between the ranks might not be the same.

Ordinal data has a meaningful order that makes it easy to sort different categories within a data set without necessarily quantifying a specific distance. For example, customer satisfaction values like "Very satisfied, satisfied, dissatisfied, and very dissatisfied" simply need to be ranked for easy interpretation, but there is not necessarily a numerical difference between "very satisfied" and "satisfied."

Ordinal data is valuable for small to midsize businesses as it preserves anomaly, offers flexibility and practicality, and maintains essential information while making it easy to interpret the clear ranking of items. It offers a high level of precision but maintains flexibility, which is helpful for companies to understand customer preferences, generate employee feedback, understand market research, and even set goals.

Compared to other types of data, such as nominal, interval, and ratio data, ordinal data gives values like frequency, distribution, mode, and median. Ordinal data gives data a meaningful categorization without forcing organizations to assign a numerical value or create precise measurements between intervals. This is especially helpful when aggregating subjective data or preferences.

What's the difference between ordinal data and nominal data?

Nominal data distinctly organizes data that doesn't have an inherent order or ranking. Compared to ordinal data, nominal data has no inherent ranking.

For businesses, this could include types of products, colors, different customer segmentation, and more. For example, there's no numerical difference between different service offerings within your business. Nominal data is commonly used for information like demographics, marketing values, psychological attributes, and more.

Both nominal and ordinal data have value when embracing a data-driven culture, as each provides unique insight into patterns and relationships within customer segmentation and targeting, employee performance evaluations, market analysis, and more.

Ordinal data is used for data classification that includes an order or ranking without a clearly defined interval. It provides unique insight into customer preferences, segmentation, feelings, perceptions, and behaviors.

One of the best benefits of ordinal data is the ease of comparing different variables. Its simplicity makes it extremely quick to group and segment data. Even though it's a simple classification, it can be turned into a more complex statistical analysis. Ordinal data is also easy to collect, as responses are limited to only a handful of options. Ordinal data can also be turned into more analytic business insights, supporting data-driven decisions around annual budgets, customer retention forecasts, product sentiment, and more.

Collecting ordinal data starts with leveraging your data management software to create, store, and access company-wide data. Follow the steps below to start collecting ordinal data in your business.

1. Define variables and scales

Once you've decided on the different types of ordinal data you want to collect, create your questions, variables, and scales. For example, if you want to survey customers for feedback on a new product launch, define the questions you want to ask and establish your variables within each question. For example, to answer, "How much does this product solve your problem?" the answers could include "Very well, somewhat well, not well."

Determine exactly what information you want to collect, and create questions that allow respondents to rate themselves on a scale.

2. Use surveys or questionnaires

Sending out customer or market surveys or questionnaires is one of the best ways to collect ordinal data. Make sure your wording is clear and concise, and only include the most effective and productive questions. Many consumers might stop answering after a survey gets too lengthy, so focus your questions on the top priorities that gather the most valuable information.

3. Send out the survey or questionnaire

If you're sending a survey to customers or a questionnaire to the general market, begin distributing your questions at scale. Choose your distribution platform, such as email or social media, and provide clear instructions on engaging with your survey. You can also send out questionnaires through your own platform or website to learn details such as satisfaction after a customer completes a checkout page.

Once customers, potential buyers, or the general public have responded to your survey, it's time to analyze and aggregate your ordinal data. Your data management software platform helps streamline, automate, and understand data at scale to identify trends and patterns. Here are a few more tips for analyzing your ordinal data.

1. Do a visual sweep of data for inconsistencies and inaccuracies

While artificial intelligence and software capabilities can sort data according to your pre-established rules and tags, it's helpful to do a manual, visual sweep of your data before implementing new data management strategies. Check for inaccuracies or inconsistencies in naming conventions or sorting, just to manually check the automations you've established. For example, make sure education level and socioeconomic status are not interspersed, or do a spot check that the correct data matches the right customer.

2. Lean on your data management software

It's almost impossible to manually sort and analyze trends and patterns, especially for huge datasets. Data management software organizes and structures large sets of data, formats it into different visuals and graphs, and provides ways of reporting and pulling insights. Especially with more sophisticated platforms, there are tons of different ways to slice and dice ordinal data, creating helpful visuals like customer satisfaction over time, trends in buying behavior, and more.

If you're working with data on a smaller scale, you can analyze and sort your information manually. For example, if you're a new business with only 10 customers, you might be able to aggregate customer satisfaction feedback via free tools.

3. Implement learnings and takeaways

Ordinal data is extremely helpful, but it's only productive when actually implemented into your business. Most organizations collect and sort data but might not be implementing their findings on a regular basis. Your ordinal data offers valuable insights into your customers and the market, so as you learn takeaways about your business, implement changes or action items within your organization.

For example, you might discover that customers buying a specific product offering are more satisfied than a different set of customers buying another product. Perhaps there is something different in that onboarding or purchasing process that you can implement, or maybe that pricing structure is more valuable. Whatever learnings you discover, test and experiment with changes across your business to improve revenue and customer retention.

Ordinal data is one piece of your data management strategy, and as you implement a data-driven culture, understanding and leveraging different types of intelligence helps you learn more about your customers, competitors, and the market. Ordinal data differs from other types of data in that it provides unique insight into patterns and relationships between different values. 

Creating a data-driven culture starts with small changes, and to implement ordinal data, determine what type of information you want to find out. Lean on data management software to help make this collection and analysis possible, and vet two to three different platforms to see what fits your budget and needs.