Tracking and interpreting customer data starts with understanding how to categorize it using ordinal data and seeking feedback. Ordinal data helps businesses rank variables like satisfaction or interest, uncover patterns through surveys, and align product decisions with real-time feedback.
Whether you're measuring sentiment, prioritizing features, or refining your go-to-market strategy, ordinal data bridges raw input with actionable insight. And with the right survey software or data analytics tools, you can collect, analyze, and apply this data at scale.
Ordinal data is a form of categorical data where values are ranked in a specific order, but the spacing between them isn’t consistent. Businesses often use it to track customer satisfaction, preferences, or interest levels and make it key for interpreting consumer feedback and product guiding decisions.
For example, survey responses like “very dissatisfied” to “very satisfied”, or education levels like “high school, bachelor’s, master’s”, reflect increasing levels of something, but we can't measure the exact gap between them.
This structure makes ordinal data ideal for capturing human perception—how people feel, prefer, or prioritize—without needing exact measurements.
As opposite to ordinal data, nominal data is used to categorize without order (like user types or industries), while interval data measures variables like temperature or test scores with equal spacing but no true zero. Ratio data, on the other hand, includes both equal intervals and a meaningful zero, ideal for things like revenue or age.
The table below breaks down the differences between these four data types to help you choose the right one for your business use case.
| Feature | Nominal (Labels) | Ordinal (Ranks) | Interval (Numeric, no true zero) | Ratio (Numeric, with true zero) |
| Order of data | ❌ Not applicable | ✅ Present | ✅ Present | ✅ Present |
| Consistent spacing | ❌ Not applicable | ❌ Not applicable | ❌ Not applicable | ✅ Present |
| True zero point | ❌ Not applicable | ❌ Not applicable | ❌ Not applicable | ✅ Present |
| Statistical analysis | Mode only | Mode, median | Median, standard deviation, correlation | Full range of statistical ops |
| Business examples | Type of employment, with outcomes like freelance, full-time, or hybrid work | Survey responses from "strongly agree to strongly disagree", satisfaction levels, and income levels | Temperature, standardized tests, measuring time intervals, difference between two readings | Sales revenue, age, return on investment (ROI), analysis of financial information |
Ordinal data should be used when analyzing customer satisfaction, agreeability, intent, or loyalty by cross-validating their economic status with their survey responses.
Below are some common, high-impact scenarios where ordinal data is the go-to format.
In summary, ordinal data is mostly used when you care about human emotion, satisfaction, preference, and perception, and need a format that is structured and ranked but flexible enough to capture nuance.
Ordinal data powers key decisions across marketing, product, HR, and CX by helping teams rank sentiment, segment users, and track performance trends.
While analyzing ordinal data is straightforward, the real value lies in how well you collect it. Flawed question design or poor execution can lead to misleading insights. Here are proven strategies to ensure your data is reliable and business-ready:
Ordinal data occurs in different formats. Here are a few examples of ordinal data and how to synchronize it with your business strategy to improve your data management efforts.
Whether you've already launched your product into the market or are introducing new features to your existing product, you’ll need to conduct market research to ask questions to gauge your target audience's interest.
Ordinal scales like "not interested" to "very interested" help marketers gauge interest levels during product testing, beta launches or feature validation surveys.
After a product demo, use a feedback tool to ask, "how interested are you to use
Market research involves analyzing both qualitative and quantitative data to understand customer needs, their buying patterns, and what motivates them to buy from you. These insights can help improve your marketing campaigns in the future.
For example, if you host conferences regularly, surveys can help you know how well you did and whether your attendees want to attend the conference again. Here's an example of interest-level data:
Source: SurveyMonkey
The questions you ask will reveal potential customers’ interest level in your product or service. Interest levels range from not interested, slightly interested, neutral, to very interested.
This type of ordinal data analysis provides insights into your target audience's proficiency level.
Education level may inquire whether your target audience has acquired different levels of formal education, such as high school, college, and graduate school. You may collect this data by assigning numbers to each level, such as 1 for no formal education, 2 for primary schooling, and so on, until 10 for a doctoral university degree.
Education-level data comes in handy when using analytics in your recruitment process to help you evaluate the job applications of potential candidates. Assigning values to educational milestones (e.g 1 = high school, 5 = doctorate) helps in recruitment analytics or audience segmentation.
Education level ordinal data can be used in applicant scoring models to assess training needs for customer facing teams.
Educational-level data can help you make powerful predictions about who to hire in the future to support company growth, where to focus your recruiting efforts, and find suitable candidates for specific positions.
If you run a sales team, assessing the education level of your team members enables you to know how to support their career development goals. This way, you can build a high-performing sales team and improve retention.
Understanding the socio-economic status of your target audience helps create and refine your customer segments based on their demographic and psychographic profiles.
Ordinal data set statistics like "low", "middle", and "high" income groupings give you insights into purchasing power and persona development. Studying the ordinal data of income level will help you understand the socio-economic status. An ideal use case is using it to segment B2C customers by income to tailor ad creatives and email campaigns.
You can then rely on these segments when running personalized marketing campaigns that meet their needs and wants. Ordinal data on socioeconomic status for a B2C target audience includes gender, location, household income, marital status, and age.
On the other hand, data for a B2B target audience includes gross annual revenue, stage of business growth, number of employees, market position, and type of industry.
The satisfaction level reflects how content your customers are with different brand interactions. For example, your customer onboarding process or how well you resolve different customer issues.
Frequently used with net promoter scores (NPS) or CSAT surveys, ordinal satisfaction data helps benchmark customer experience for companies.
A common use case can be creating a post support survey that could ask, "how satisfied were you with your recent service?" And the answers can range from extremely satisfied to extremely dissatisfied for customers.
Customer satisfaction may be expressed as extremely satisfied, satisfied, unsatisfied, or extremely dissatisfied. Satisfaction level data helps you gauge customer service and sales handling satisfaction to identify areas for improvement.
Here’s an example of satisfaction level data from a product-market fit survey that Buffer conducted:
Source: Buffer
With this data, the company could tell how useful Buffer’s Power scheduler is to their customers, meaning that the product was the right fit for their users.
This involves asking questions that reveal the similarities or differences between two or more data points. Once you identify the similarities or differences, you can learn what characteristics are similar, which ones are different, and the degree to which they’re different or similar.
Ordinal data supports directional comparisons like, "compared to last year, how did our product meet your needs?" And the options can be range from significantly worse to significantly better. This enables you to analyse trends in product sentiment year over year for product roadmap planning.
For example, you may want to compare revenue performance from 2021 to 2022. Your comparison will yield significantly less, about the same, more, and significantly more for each year's revenue.
With this, you can gauge macroeconomic and industry trends and adjust your strategy to fit your budgeting process to control spending. You may even decide to take this further and compare industry trends so that you can create reports and write thought leadership content to drive brand awareness.
You can conduct several tests on ordinal data to measure the difference between two or more groups. These tests include:
There are two ways to analyze ordinal data: inferential and descriptive statistics.
Descriptive statistics for ordinal data help summarize the overall characteristics of a dataset and reveal underlying patterns.
Common descriptive measures include frequency distribution (how often each response occurs), measures of central tendency like the median and mode, and the range, which indicates the spread or variability within the data. These insights provide a foundational view before applying more advanced analysis.
Inferential statistics for ordinal data on the other hand, predict what may happen in the future based on the data you have.
You can use ordinal data to collect insights, create hypotheses, or even draw conclusions with the four tests described above.The Kruskal-Wallis, Mann Whitney U, and Wilcoxon signed-rank sum tests all analyze ordinal data. They're all nonparametric tests, meaning they don't rely on any assumptions about data distribution.
Descriptive analytics collects, analyzes, and reports data about events that have already occurred. This differs from predictive analytics, which predicts future events based on historical data.
Descriptive analytics helps businesses identify patterns in the past to improve their future decision-making. In descriptive analytics, the goal is to find patterns n existing data, not predict the future. It aims to find cause and effect relationships between past events and use these relationships to predict future events.
Use graphs to simplify large or complex ordinal datasets. Choose your graph type based on purpose, data volume, and audience needs.
Ordinal data is ranked categorical data where the order of values matters, but the differences between them aren't precisely measured. It's used to capture perceptions like satisfaction, interest, and agreement in surveys and feedback forms.
Ordinal data is typically collected through surveys, polls, or Likert scales that present a ranked range of responses (e.g., "Very satisfied" to "Very dissatisfied"). Tools like Google Forms, SurveyMonkey, and Typeform are commonly used.
Non-parametric tests such as the Kruskal-Wallis H test, Mann-Whitney U test, Wilcoxon signed-rank test, and Mood’s Median test are used to analyze ordinal data when comparing groups or testing hypotheses.
Ordinal data has a meaningful order or ranking among values, while nominal data represents categories without any inherent order. For example, education level is ordinal; hair color is nominal.
Businesses use ordinal data to assess customer satisfaction, employee engagement, product feedback, and brand sentiment. It helps identify patterns, prioritize improvements, and align strategies with audience preferences.
Familiarizing yourself with customer-oriented ordinal data sets a course for your product roadmap, new feature launches and customer satisfaction. Handling feedback surveys not only informs the customer that you as a brand are thinking of them but helps you deep dive into their preferences to fine tune your product efficiency.
Not only that, it segments your audience in a way where you can analyze the financial threshold before running a go to market campaign or a new advertisement to improve your chances of success. By breaking down ordinal data and studying the real numbers, you can paint a picture of the success or failure of your brand.
Learn how to segment, manage and visualize your data by checking out best data visualization tools in 2025 to delve into real-world consumption metrics and pave a brand roadmap.
This article was originally published in 2023 and has been updated with new information.
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