What Is Ordinal Information? Definition, Examples, and Enterprise Makes use of

Monitoring and decoding buyer knowledge begins with understanding the best way to categorize it utilizing ordinal knowledge and looking for suggestions. Ordinal knowledge helps companies rank variables like satisfaction or curiosity, uncover patterns via surveys, and align product choices with real-time suggestions.

Whether or not you are measuring sentiment, prioritizing options, or refining your go-to-market technique, ordinal knowledge bridges uncooked enter with actionable perception. And with the proper survey software program or knowledge analytics instruments, you may accumulate, analyze, and apply this knowledge at scale.

For instance, survey responses like “very dissatisfied” to “very glad”, or schooling ranges like “highschool, bachelor’s, grasp’s”, mirror rising ranges of one thing, however we will not measure the precise hole between them.

This construction makes ordinal knowledge ideally suited for capturing human notion—how folks really feel, favor, or prioritize—with no need precise measurements.

How does ordinal knowledge examine to nominal, interval, and ratio knowledge?

As reverse to ordinal knowledge, nominal knowledge is used to categorize with out order (like person sorts or industries), whereas interval knowledge measures variables like temperature or take a look at scores with equal spacing however no true zero. Ratio knowledge, alternatively, contains each equal intervals and a significant zero, ideally suited for issues like income or age.

The desk under breaks down the variations between these 4 knowledge sorts that will help you select the proper one for your enterprise use case.

Characteristic	Nominal (Labels)	Ordinal (Ranks)	Interval (Numeric, no true zero)	Ratio (Numeric, with true zero)
Order of knowledge	❌ Not relevant	✅ Current	✅ Current	✅ Current
Constant spacing	❌ Not relevant	❌ Not relevant	❌ Not relevant	✅ Current
True zero level	❌ Not relevant	❌ Not relevant	❌ Not relevant	✅ Current
Statistical evaluation	Mode solely	Mode, median	Median, customary deviation, correlation	Full vary of statistical ops
Enterprise examples	Kind of employment, with outcomes like freelance, full-time, or hybrid work	Survey responses from “strongly comply with strongly disagree”, satisfaction ranges, and revenue ranges	Temperature, standardized exams, measuring time intervals, distinction between two readings	Gross sales income, age, return on funding (ROI), evaluation of monetary info

When do you have to use ordinal knowledge in enterprise?

Ordinal knowledge needs to be used when analyzing buyer satisfaction, agreeability, intent, or loyalty by cross-validating their financial standing with their survey responses.

Beneath are some widespread, high-impact eventualities the place ordinal knowledge is the go-to format. 

• Voice of buyer (VOC) packages: Ordinal knowledge captures sentiment shifts over time via ranked questions like “How glad are you together with your onboarding expertise?” These insights enable companies to measure emotional progress between time intervals and align services or products enhancements with buyer notion.
• A/B take a look at expertise suggestions: Past uncooked click on knowledge, working A/B exams with responses like “Which model helped you full your activity extra effectively?” offers qualitative perception into person expertise. This suggestions helps consider perceived usefulness, refining product iterations primarily based on what customers really feel works greatest.
• Model sentiment monitoring: Ordinal scales (e.g., “Not acquainted → Impartial → Like → Love”) assist section model notion throughout buyer sorts. These responses reveal emotional positioning, permitting entrepreneurs to differentiate model loyalists from informal customers and tailor campaigns accordingly.
• Product-market match valuation: Questions like “How disenchanted would you be in the event you may not use this product?” type customers into evangelists, casuals, or drop-offs. This ordinal strategy identifies person loyalty tiers, guiding product roadmaps, messaging, and funding priorities.

In abstract, ordinal knowledge is usually used while you care about human emotion, satisfaction, choice, and notion, and wish a format that’s structured and ranked however versatile sufficient to seize nuance.

Actual world enterprise use instances for ordinal knowledge 

Ordinal knowledge powers key choices throughout advertising and marketing, product, HR, and CX by serving to groups rank sentiment, section customers, and observe efficiency traits.

• Product suggestions and roadmapping: Product groups use ordinal survey questions like “How helpful is the brand new dashboard?” to collect ranked insights. These responses spotlight what customers worth, flag ache factors early, and form roadmap priorities primarily based on person sentiments.
• Internet promoter rating (NPS) packages: NPS scores (0–10) are grouped into ordinal classes: detractors (0–6), passives (7–8), and promoters (9–10). Monitoring shifts between these teams helps manufacturers monitor loyalty, predict churn, and measure advocacy over time.
• Worker engagement and tradition surveys: HR groups use ordinal scales to evaluate worker sentiment on matters like management, advantages, and office tradition. These insights assist choices round retention, supervisor effectiveness, and inclusion initiatives.
• Buyer assist expertise scores: After assist interactions, prospects fee their expertise on scales like “Very unhappy” to “Very glad.” When aggregated, these scores reveal traits in agent efficiency and repair high quality.
• Model notion and market analysis: Advertising and marketing groups accumulate ranked suggestions on model choice, advert effectiveness, and perceived product worth. These ordinal insights inform positioning, messaging, and go-to-market plans. 

How are you going to accumulate and analyze high-quality ordinal knowledge?

Whereas analyzing ordinal knowledge is simple, the actual worth lies in how effectively you accumulate it. Flawed query design or poor execution can result in deceptive insights. Listed below are confirmed methods to make sure your knowledge is dependable and business-ready:

• Use balanced, impartial query framing: Keep away from loaded or emotionally biased questions like “How wonderful was your expertise?” As a substitute, ask neutrally: “How would you fee your expertise with our new product?” Impartial framing encourages sincere, unbiased responses and protects knowledge integrity.
• Restrict the variety of scale factors: Too many choices can confuse respondents. Use 4–7 scale factors (ideally odd-numbered) to permit for a impartial center. For instance, a 5-point Likert scale from “Strongly Disagree” to “Strongly Agree” balances simplicity with nuance.
• Be constant in scale path: Maintain scale path uniform—for instance, all the time inserting “Strongly agree” on the proper. Inconsistencies confuse respondents and will distort outcomes. Consistency additionally improves the survey expertise and belief within the knowledge.
• Contextualize your questions: Add temporary explanations for readability. As a substitute of “Fee your satisfaction with integration,” ask “Fee your satisfaction with how simply our device integrates together with your present CRM.” Specificity ensures constant interpretation and extra correct solutions.
• Use dependable survey instruments with logic branching: Select trusted instruments like Typeform, SurveyMonkey, or Qualtrics. These platforms assist clear logic flows, conditional questions, and structured knowledge seize. All the time take a look at the logic paths earlier than going reside to catch errors early.
• Pilot earlier than launch: Run the survey with a small group—each inside and exterior—earlier than full rollout. A pilot take a look at helps spot complicated wording, damaged logic, or scale misalignment. This step ensures your ordinal knowledge is correct, usable, and aligned with your enterprise targets. 

5 examples of ordinal knowledge in statistics 

Ordinal knowledge happens in numerous codecs. Listed below are a couple of examples of ordinal knowledge and the best way to synchronize it with your enterprise technique to enhance your knowledge administration efforts. 

1. Curiosity degree

Whether or not you have already launched your product into the market or are introducing new options to your present product, you’ll must conduct market analysis to ask inquiries to gauge your target market’s curiosity. 

Ordinal scales like “not ” to “very ” assist entrepreneurs gauge curiosity ranges throughout product testing, beta launches or function validation surveys.

After a product demo, use a suggestions device to ask, “how are you to make use of  

Market analysis entails analyzing each qualitative and quantitative knowledge to know buyer wants, their shopping for patterns, and what motivates them to purchase from you. These insights will help enhance your advertising and marketing campaigns sooner or later.

For instance, in the event you host conferences repeatedly, surveys will help you understand how effectively you probably did and whether or not your attendees wish to attend the convention once more. This is an instance of interest-level knowledge:

Supply: SurveyMonkey

The questions you ask will reveal potential prospects’ curiosity degree in your services or products. Curiosity ranges vary from not , barely , impartial, to very .  

2. Training degree

The sort of ordinal knowledge evaluation offers insights into your target market’s proficiency degree. 

Training degree could inquire whether or not your target market has acquired totally different ranges of formal schooling, akin to highschool, faculty, and graduate faculty. You might accumulate this knowledge by assigning numbers to every degree, akin to 1 for no formal schooling, 2 for main education, and so forth, till 10 for a doctoral college diploma.

Training-level knowledge is useful when utilizing analytics in your recruitment course of that will help you consider the job purposes of potential candidates. Assigning values to instructional milestones (e.g 1 = highschool, 5 = doctorate) helps in recruitment analytics or viewers segmentation. 

Training degree ordinal knowledge can be utilized in applicant scoring fashions to evaluate coaching wants for buyer going through groups.

Academic-level knowledge will help you make highly effective predictions about who to rent sooner or later to assist firm development, the place to focus your recruiting efforts, and discover appropriate candidates for particular positions.

In the event you run a gross sales crew, assessing the schooling degree of your crew members allows you to know the best way to assist their profession growth targets. This manner, you may construct a high-performing gross sales crew and enhance retention.

3. Socio-economic standing

Understanding the socio-economic standing of your target market helps create and refine your buyer segments primarily based on their demographic and psychographic profiles. 

Ordinal knowledge set statistics like “low”, “center”, and “excessive” revenue groupings provide you with insights into buying energy and persona growth. Finding out the ordinal knowledge of revenue degree will enable you to perceive the socio-economic standing. A super use case is utilizing it to section B2C prospects by revenue to tailor advert creatives and e mail campaigns.

You may then depend on these segments when working customized advertising and marketing campaigns that meet their wants and needs. Ordinal knowledge on socioeconomic standing for a B2C target market contains gender, location, family revenue, marital standing, and age. 

Alternatively, knowledge for a B2B target market contains gross annual income, stage of enterprise development, variety of workers, market place, and sort of business. 

4. Satisfaction degree

The satisfaction degree displays how content material your prospects are with totally different model interactions. For instance, your buyer onboarding course of or how effectively you resolve totally different buyer points. 

Continuously used with web promoter scores (NPS) or CSAT surveys, ordinal satisfaction knowledge helps benchmark buyer expertise for corporations.

A standard use case might be making a submit assist survey that might ask, “how glad had been you together with your latest service?” And the solutions can vary from extraordinarily glad to extraordinarily dissatisfied for patrons.

Buyer satisfaction could also be expressed as extraordinarily glad, glad, unhappy, or extraordinarily dissatisfied. Satisfaction degree knowledge helps you gauge customer support and gross sales dealing with satisfaction to establish areas for enchancment. 

Right here’s an instance of satisfaction degree knowledge from a product-market match survey that Buffer performed:

Supply: Buffer

With this knowledge, the corporate may inform how helpful Buffer’s Energy scheduler is to their prospects, which means that the product was the proper match for his or her customers. 

5. Comparative suggestions 

This entails asking questions that reveal the similarities or variations between two or extra knowledge factors. When you establish the similarities or variations, you may study what traits are comparable, which of them are totally different, and the diploma to which they’re totally different or comparable.  

Ordinal knowledge helps directional comparisons like, “in comparison with final yr, how did our product meet your wants?” And the choices might be vary from considerably worse to considerably higher. This allows you to analyse traits in product sentiment yr over yr for product roadmap planning.

For instance, you might wish to examine income efficiency from 2021 to 2022. Your comparability will yield considerably much less, about the identical, extra, and considerably extra for every year’s income.

With this, you may gauge macroeconomic and business traits and modify your technique to suit your budgeting course of to management spending. You might even resolve to take this additional and examine business traits so that you could create studies and write thought management content material to drive model consciousness. 

What statistical exams work with ordinal knowledge?

You may conduct a number of exams on ordinal knowledge to measure the distinction between two or extra teams. These exams embrace:

• The Kruskal-Wallis take a look at: A non-parametric take a look at used to check the medians of three or extra impartial teams. It is ideally suited when the info is just not usually distributed and group variances are unequal. It could possibly additionally examine two dependent teams, akin to before-and-after outcomes from a web site redesign.
• The Mann-Whitney U take a look at: A non-parametric take a look at used to check the medians of two impartial samples. It’s appropriate for ordinal knowledge like 1–5 score scales and works effectively when there are not any clearly outlined teams or when working with small pattern sizes.
• Wilcoxon rank-sum take a look at: A non-parametric various to the paired t-test for evaluating matched or paired samples. It is applicable when the info is not usually distributed and is usually used to guage adjustments over time, akin to buyer satisfaction earlier than and after a service replace.
• Temper’s Median take a look at: A non-parametric take a look at for figuring out whether or not group medians are equal. It’s generally utilized in opinion or sentiment evaluation, akin to measuring buyer reactions to services or products. The take a look at will help predict habits primarily based on temper, like buy intent or model loyalty.

What’s descriptive statistics for ordinal knowledge?

There are two methods to research ordinal knowledge: inferential and descriptive statistics.

Descriptive statistics

Descriptive statistics for ordinal knowledge assist summarize the general traits of a dataset and reveal underlying patterns.

Widespread descriptive measures embrace frequency distribution (how usually every response happens), measures of central tendency just like the median and mode, and the vary, which signifies the unfold or variability inside the knowledge. These insights present a foundational view earlier than making use of extra superior evaluation.

Inferential statistics

Inferential statistics for ordinal knowledge alternatively, predict what could occur sooner or later based on the info you’ve gotten.

You can use ordinal knowledge to collect insights, create hypotheses, and even draw conclusions with the 4 tests described above.The Kruskal-Wallis, Mann Whitney U, and Wilcoxon signed-rank sum tests all analyze ordinal knowledge. They’re all nonparametric tests, which means they do not depend on any assumptions about knowledge distribution.

Descriptive analytics

Descriptive analytics collects, analyzes, and studies knowledge about occasions which have already occurred. This differs from predictive analytics, which predicts future occasions primarily based on historic knowledge.

Descriptive analytics helps companies establish patterns up to now to enhance their future decision-making. In descriptive analytics, the aim is to seek out patterns n present knowledge, not predict the long run. It goals to seek out trigger and impact relationships between previous occasions and use these relationships to foretell future occasions.

Ordinal knowledge: Continuously requested questions (FAQs)

What’s ordinal knowledge and the way is it used?

Ordinal knowledge is ranked categorical knowledge the place the order of values issues, however the variations between them aren’t exactly measured. It is used to seize perceptions like satisfaction, curiosity, and settlement in surveys and suggestions types.

How do you accumulate ordinal knowledge?

Ordinal knowledge is usually collected via surveys, polls, or Likert scales that current a ranked vary of responses (e.g., “Very glad” to “Very dissatisfied”). Instruments like Google Kinds, SurveyMonkey, and Typeform are generally used.

What exams are used for ordinal knowledge?

Non-parametric exams such because the Kruskal-Wallis H take a look at, Mann-Whitney U take a look at, Wilcoxon signed-rank take a look at, and Temper’s Median take a look at are used to research ordinal knowledge when evaluating teams or testing hypotheses.   

What’s the distinction between ordinal and nominal knowledge?

Ordinal knowledge has a significant order or rating amongst values, whereas nominal knowledge represents classes with none inherent order. For instance, schooling degree is ordinal; hair shade is nominal.

How is ordinal knowledge utilized in enterprise determination making?

Companies use ordinal knowledge to evaluate buyer satisfaction, worker engagement, product suggestions, and model sentiment. It helps establish patterns, prioritize enhancements, and align methods with viewers preferences.

Technique to buyer satisfaction begins now

Familiarizing your self with customer-oriented ordinal knowledge units a course in your product roadmap, new function launches and buyer satisfaction. Dealing with suggestions surveys not solely informs the shopper that you just as a model are pondering of them however helps you deep dive into their preferences to tremendous tune your product effectivity.

Not solely that, it segments your viewers in a method the place you may analyze the monetary threshold earlier than working a go to market marketing campaign or a brand new commercial to enhance your probabilities of success. By breaking down ordinal knowledge and finding out the actual numbers, you may paint an image of the success or failure of your model.

Discover ways to section, handle and visualize your knowledge by testing greatest knowledge visualization instruments in 2025 to delve into real-world consumption metrics and pave a model roadmap.

This text was initially revealed in 2023 and has been up to date with new info.