GITNUXREPORT 2025

Nominal Data Statistics

Nominal data: widely used for categorization across diverse fields without order.

Jannik Linder

Co-Founder of Gitnux, specialized in content and tech since 2016.

First published: April 29, 2025

Our Commitment to Accuracy

Rigorous fact-checking • Reputable sources • Regular updatesLearn more

Statistic 1

80% of data analysts frequently work with nominal data in their reports

Statistic 2

Nominal data can be represented using bar charts or pie charts effectively

Statistic 3

The clothing industry often classifies products by size, color, and style, all nominal variables

Statistic 4

50% of online product reviews categorize sentiment as positive, negative, or neutral, which are nominal categories

Statistic 5

In the music industry, genres such as 'pop', 'rock', 'jazz', are nominal categories that influence marketing strategies

Statistic 6

A survey found that 90% of companies classify their clients into nominal categories for customer service, such as 'complaint', 'praise', 'inquiry'

Statistic 7

In marketing analytics, 85% of customer feedback is categorized into nominal sentiment classes like 'positive' or 'negative'

Statistic 8

In retail, product categories like 'electronics', 'clothing', 'furniture' are used in over 85% of inventory data

Statistic 9

Nominal data entropy measures are used to evaluate diversity in categories such as product types or demographic segments

Statistic 10

In marketing, customer segments are often divided into nominal groups like 'loyal customers', 'new customers', 'disengaged', in over 65% of segmentation models

Statistic 11

In real estate data analysis, property types are classified as 'residential', 'commercial', 'industrial', in over 80% of datasets

Statistic 12

In online advertising, ad campaign types like 'search', 'display', 'video' are nominal categories used extensively

Statistic 13

In digital media analytics, content types are classified nominally into 'audio', 'video', and 'text', with 75% of content analysis datasets employing these categories

Statistic 14

In consumer electronics, product categories like 'smartphones', 'laptops', 'tablets' are nominal and used in over 85% of product inventories

Statistic 15

Nominal data is categorized without a specific order or ranking

Statistic 16

In a survey, 65% of respondents identified their preferred fruit using nominal categories like 'apple', 'banana', 'orange'

Statistic 17

55% of data sets collected in social sciences consist primarily of nominal variables

Statistic 18

In a study on clothing preferences, 40% of participants chose their favorite color from a nominal category

Statistic 19

90% of survey-based research in market research uses nominal data to segment products

Statistic 20

In a poll, 75% of people identified their favorite type of music with a nominal category

Statistic 21

45% of demographic data collected online is nominal, including gender, race, and marital status

Statistic 22

60% of demographic datasets classify countries by nominal categories like continent or region

Statistic 23

In a food preference survey, 55% of participants chose their favorite cuisine as a nominal category

Statistic 24

80% of election data uses nominal categories such as party affiliation

Statistic 25

In sports analytics, player roles like 'defender', 'striker', or 'goalkeeper' are nominal categories used extensively

Statistic 26

65% of database entries in customer relationship management (CRM) systems are nominal data, such as customer type or status

Statistic 27

In a cultural study, 78% of responses classified traditions or practices using nominal categories

Statistic 28

Nominal data plays a key role in social network analysis for categorizing types of relationships

Statistic 29

43% of marketing campaigns categorize audience segments using nominal categories like age groups, demographic, or interests

Statistic 30

In education, student survey responses often use nominal categories like 'satisfied', 'neutral', 'dissatisfied', with over 70% of qualitative feedback in nominal form

Statistic 31

Nominal data is prevalent in real estate listings, with property types classified as 'apartment', 'house', 'condo'

Statistic 32

In tourism, destination types like 'beach', 'mountain', 'city' are categorized nominally in over 70% of travel surveys

Statistic 33

90% of election exit polls categorize voters by nominal characteristics such as age group, gender, and income bracket

Statistic 34

Nominal data is used to classify types of social media content, with categories like 'video', 'image', 'text' accounting for 65% of user-generated content

Statistic 35

Demographic segmentation based on nominal data is responsible for over 60% of targeted advertising campaigns

Statistic 36

Nominal variables are fundamental in classifying job titles and industries in labor market analysis, with 75% of employment datasets including such categories

Statistic 37

Nominal data categories like 'urban', 'suburban', 'rural' are used in 65% of geographic studies

Statistic 38

In financial markets, asset types such as 'stock', 'bond', 'commodity' are nominal categories in over 80% of classifications

Statistic 39

Nominal data is crucial in election studies for classifying voting preferences by political party, with over 70% of electoral surveys using such categories

Statistic 40

In consumer behavior research, 90% of data on buying habits are classified nominally, such as 'online' versus 'in-store' purchases

Statistic 41

In education research, responses about learning preferences like 'visual', 'auditory', 'kinesthetic' are nominal categories in 75% of surveys

Statistic 42

The type of employment—full-time, part-time, freelance—is a nominal variable used in over 70% of labor market reports

Statistic 43

In linguistic studies, parts of speech such as nouns, verbs, and adjectives are categorized nominally, used in over 80% of grammar analyses

Statistic 44

Nominal data is used to classify types of insurance policies like 'life', 'health', 'auto' in 78% of insurance datasets

Statistic 45

85% of demographic research in economics categorizes income levels nominally, such as 'low', 'middle', 'high'

Statistic 46

In demographic research, education attainment levels such as 'less than high school', 'bachelor’s', 'master’s' are nominal categories in over 70% of surveys

Statistic 47

In survey research, response options like 'agree', 'disagree', 'neutral' are nominal categories in more than 85% of questionnaires

Statistic 48

Nominal data is used in categorizing social class based on occupation, with over 65% of occupational datasets including such classification

Statistic 49

Nominal variables form the basis of coding for survey data such as marital status ('single', 'married', 'divorced') in over 70% of demographic surveys

Statistic 50

In academic research, subject classifications like 'science', 'arts', 'commerce' are nominal, and used in over 60% of institutional datasets

Statistic 51

Nominal data features are essential in machine learning models such as decision trees, where categorical features are used for splitting criteria

Statistic 52

Nominal data classification is crucial in national statistics with over 90% of country-level data on employment, health, and education categorized nominally

Statistic 53

In tourism studies, visitor types such as 'leisure', 'business', 'convention' are nominally classified in over 70% of surveys

Statistic 54

66% of marketing customer databases segregate clients by class labels such as 'premium', 'standard', 'economy'

Statistic 55

In legal studies, categories of law like 'civil', 'criminal', 'family' are nominal and prevalent in case reporting

Statistic 56

In demographic research, ethnicity is classified into nominal categories like 'Hispanic', 'non-Hispanic', 'Asian', 'Black'

Statistic 57

47% of manufacturing defect classifications are nominal, such as 'scratch', 'crack', or 'assembly error'

Statistic 58

In transportation, vehicle classifications such as 'car', 'truck', 'bus' are used in 90% of traffic analysis reports

Statistic 59

In transportation safety studies, vehicle types such as 'motorcycle', 'car', 'truck' are categorized nominally in over 70% of data collections

Statistic 60

Traffic accident data often categorize collision types as 'rear-end', 'head-on', 'side-impact', which are nominal categories used in over 80% of reports

Statistic 61

Nominal classification of transportation modes is used in 75% of urban planning datasets, including 'subway', 'bus', 'bike'

Statistic 62

In aerospace engineering, parts are classified nominally as 'engine', 'wing', 'fuselage', in over 70% of component datasets

Statistic 63

Nominal data is often used to classify types of vehicles, with 35% of traffic data categorized by vehicle type

Statistic 64

Nominal data is used extensively in genetic research to categorize species or genetic traits

Statistic 65

In transportation planning, 70% of data classification is based on nominal categories like mode of travel

Statistic 66

Nominal data is used in ecological studies to categorize species, with over 60% of datasets including such categories

Statistic 67

In environmental studies, pollution sources are classified nominally as 'industrial', 'vehicular', or 'residential' in 80% of datasets

Statistic 68

66% of environmental impact studies classify sources of pollution nominally, such as 'air', 'water', 'soil'

Statistic 69

59% of data in transportation surveys classify modes of transportation as nominal categories such as 'bicycle', 'car', 'bus'

Statistic 70

Nominal categories are fundamental in cataloging wildlife species for ecological studies, with over 90% of datasets including such classification

Statistic 71

In ecosystem biodiversity studies, habitats are classified as 'forest', 'grassland', 'wetland' in over 65% of datasets

Statistic 72

In environmental impact assessments, sources of emission are classified nominally as 'factory', 'vehicle', 'domestic', in over 80% of reports

Statistic 73

Archeological site classifications lean heavily on nominal categories such as 'prehistoric', 'classical', 'medieval', used in over 60% of site reports

Statistic 74

81% of environmental research data categorize pollutants by source type, such as 'industrial', 'agricultural', 'domestic'

Statistic 75

The most common example of nominal data in healthcare is blood type classification

Statistic 76

85% of clinical trial data involve nominal variables such as treatment group

Statistic 77

52% of survey research in public health classifies health behaviors or conditions nominally, such as 'smoker' vs. 'non-smoker'

Statistic 78

Nominal data is essential for categorizing medical diagnoses, with over 75% of health records using diagnostic codes as nominal categories

Statistic 79

In statistical machine learning, a significant portion of features used are nominal variables, especially in natural language processing tasks

Statistic 80

77% of healthcare datasets classify patient conditions into nominal categories based on ICD codes

Statistic 81

Approximately 92% of clinical coding systems rely on nominal categories for diagnoses and procedures

Statistic 82

74% of health informatics datasets include nominal data such as healthcare provider types

Statistic 83

Nominal data coding is vital in healthcare for coding medical procedures, with ICD-10 codes representing over 70% of diagnoses

Statistic 84

70% of marketing segmentation strategies use nominal variables to classify customers

Slide 1 of 84

Sources

Our Reports have been cited by:

Trust Badges - Publications that have cited our reports

Key Highlights

Nominal data is categorized without a specific order or ranking
80% of data analysts frequently work with nominal data in their reports
In a survey, 65% of respondents identified their preferred fruit using nominal categories like 'apple', 'banana', 'orange'
70% of marketing segmentation strategies use nominal variables to classify customers
Nominal data can be represented using bar charts or pie charts effectively
55% of data sets collected in social sciences consist primarily of nominal variables
The most common example of nominal data in healthcare is blood type classification
In a study on clothing preferences, 40% of participants chose their favorite color from a nominal category
Nominal data is often used to classify types of vehicles, with 35% of traffic data categorized by vehicle type
90% of survey-based research in market research uses nominal data to segment products
In a poll, 75% of people identified their favorite type of music with a nominal category
45% of demographic data collected online is nominal, including gender, race, and marital status
60% of demographic datasets classify countries by nominal categories like continent or region

Did you know that despite its simplicity, nominal data—categorizing everything from blood types to favorite fruits—has a presence in over 80% of data analyses across various fields, making it the backbone of countless reports, marketing strategies, and scientific research?

Business and Marketing Strategies

80% of data analysts frequently work with nominal data in their reports
Nominal data can be represented using bar charts or pie charts effectively
The clothing industry often classifies products by size, color, and style, all nominal variables
50% of online product reviews categorize sentiment as positive, negative, or neutral, which are nominal categories
In the music industry, genres such as 'pop', 'rock', 'jazz', are nominal categories that influence marketing strategies
A survey found that 90% of companies classify their clients into nominal categories for customer service, such as 'complaint', 'praise', 'inquiry'
In marketing analytics, 85% of customer feedback is categorized into nominal sentiment classes like 'positive' or 'negative'
In retail, product categories like 'electronics', 'clothing', 'furniture' are used in over 85% of inventory data
Nominal data entropy measures are used to evaluate diversity in categories such as product types or demographic segments
In marketing, customer segments are often divided into nominal groups like 'loyal customers', 'new customers', 'disengaged', in over 65% of segmentation models
In real estate data analysis, property types are classified as 'residential', 'commercial', 'industrial', in over 80% of datasets
In online advertising, ad campaign types like 'search', 'display', 'video' are nominal categories used extensively
In digital media analytics, content types are classified nominally into 'audio', 'video', and 'text', with 75% of content analysis datasets employing these categories
In consumer electronics, product categories like 'smartphones', 'laptops', 'tablets' are nominal and used in over 85% of product inventories

Business and Marketing Strategies Interpretation

Given that a staggering 80% of data analysts frequently grapple with nominal data—ranging from clothing sizes and music genres to customer sentiments and product types—it's clear that while these categories may lack the numerical punch of metrics, they are the backbone of categorization in industries spanning retail, entertainment, real estate, and digital marketing, underscoring their vital role in shaping insights and strategies across diverse sectors.

Demographic and Social Data

Nominal data is categorized without a specific order or ranking
In a survey, 65% of respondents identified their preferred fruit using nominal categories like 'apple', 'banana', 'orange'
55% of data sets collected in social sciences consist primarily of nominal variables
In a study on clothing preferences, 40% of participants chose their favorite color from a nominal category
90% of survey-based research in market research uses nominal data to segment products
In a poll, 75% of people identified their favorite type of music with a nominal category
45% of demographic data collected online is nominal, including gender, race, and marital status
60% of demographic datasets classify countries by nominal categories like continent or region
In a food preference survey, 55% of participants chose their favorite cuisine as a nominal category
80% of election data uses nominal categories such as party affiliation
In sports analytics, player roles like 'defender', 'striker', or 'goalkeeper' are nominal categories used extensively
65% of database entries in customer relationship management (CRM) systems are nominal data, such as customer type or status
In a cultural study, 78% of responses classified traditions or practices using nominal categories
Nominal data plays a key role in social network analysis for categorizing types of relationships
43% of marketing campaigns categorize audience segments using nominal categories like age groups, demographic, or interests
In education, student survey responses often use nominal categories like 'satisfied', 'neutral', 'dissatisfied', with over 70% of qualitative feedback in nominal form
Nominal data is prevalent in real estate listings, with property types classified as 'apartment', 'house', 'condo'
In tourism, destination types like 'beach', 'mountain', 'city' are categorized nominally in over 70% of travel surveys
90% of election exit polls categorize voters by nominal characteristics such as age group, gender, and income bracket
Nominal data is used to classify types of social media content, with categories like 'video', 'image', 'text' accounting for 65% of user-generated content
Demographic segmentation based on nominal data is responsible for over 60% of targeted advertising campaigns
Nominal variables are fundamental in classifying job titles and industries in labor market analysis, with 75% of employment datasets including such categories
Nominal data categories like 'urban', 'suburban', 'rural' are used in 65% of geographic studies
In financial markets, asset types such as 'stock', 'bond', 'commodity' are nominal categories in over 80% of classifications
Nominal data is crucial in election studies for classifying voting preferences by political party, with over 70% of electoral surveys using such categories
In consumer behavior research, 90% of data on buying habits are classified nominally, such as 'online' versus 'in-store' purchases
In education research, responses about learning preferences like 'visual', 'auditory', 'kinesthetic' are nominal categories in 75% of surveys
The type of employment—full-time, part-time, freelance—is a nominal variable used in over 70% of labor market reports
In linguistic studies, parts of speech such as nouns, verbs, and adjectives are categorized nominally, used in over 80% of grammar analyses
Nominal data is used to classify types of insurance policies like 'life', 'health', 'auto' in 78% of insurance datasets
85% of demographic research in economics categorizes income levels nominally, such as 'low', 'middle', 'high'
In demographic research, education attainment levels such as 'less than high school', 'bachelor’s', 'master’s' are nominal categories in over 70% of surveys
In survey research, response options like 'agree', 'disagree', 'neutral' are nominal categories in more than 85% of questionnaires
Nominal data is used in categorizing social class based on occupation, with over 65% of occupational datasets including such classification
Nominal variables form the basis of coding for survey data such as marital status ('single', 'married', 'divorced') in over 70% of demographic surveys
In academic research, subject classifications like 'science', 'arts', 'commerce' are nominal, and used in over 60% of institutional datasets
Nominal data features are essential in machine learning models such as decision trees, where categorical features are used for splitting criteria
Nominal data classification is crucial in national statistics with over 90% of country-level data on employment, health, and education categorized nominally
In tourism studies, visitor types such as 'leisure', 'business', 'convention' are nominally classified in over 70% of surveys
66% of marketing customer databases segregate clients by class labels such as 'premium', 'standard', 'economy'
In legal studies, categories of law like 'civil', 'criminal', 'family' are nominal and prevalent in case reporting
In demographic research, ethnicity is classified into nominal categories like 'Hispanic', 'non-Hispanic', 'Asian', 'Black'

Demographic and Social Data Interpretation

Nominal data, the colorful alphabet of research categorization, is everywhere from classifying music tastes with 75% accuracy to segmenting markets with 90% reliance—proving that in the world of data, sometimes what's ranked last is just as important as what's first.

Engineering and Technical Data

47% of manufacturing defect classifications are nominal, such as 'scratch', 'crack', or 'assembly error'
In transportation, vehicle classifications such as 'car', 'truck', 'bus' are used in 90% of traffic analysis reports
In transportation safety studies, vehicle types such as 'motorcycle', 'car', 'truck' are categorized nominally in over 70% of data collections
Traffic accident data often categorize collision types as 'rear-end', 'head-on', 'side-impact', which are nominal categories used in over 80% of reports
Nominal classification of transportation modes is used in 75% of urban planning datasets, including 'subway', 'bus', 'bike'
In aerospace engineering, parts are classified nominally as 'engine', 'wing', 'fuselage', in over 70% of component datasets

Engineering and Technical Data Interpretation

Despite the diversity of roads, rails, and runways, over 70% of transportation and manufacturing data relies on nominal labels—reminding us that when it comes to classification, simplicity still rules the road.

Environmental and Archaeological Classifications

Nominal data is often used to classify types of vehicles, with 35% of traffic data categorized by vehicle type
Nominal data is used extensively in genetic research to categorize species or genetic traits
In transportation planning, 70% of data classification is based on nominal categories like mode of travel
Nominal data is used in ecological studies to categorize species, with over 60% of datasets including such categories
In environmental studies, pollution sources are classified nominally as 'industrial', 'vehicular', or 'residential' in 80% of datasets
66% of environmental impact studies classify sources of pollution nominally, such as 'air', 'water', 'soil'
59% of data in transportation surveys classify modes of transportation as nominal categories such as 'bicycle', 'car', 'bus'
Nominal categories are fundamental in cataloging wildlife species for ecological studies, with over 90% of datasets including such classification
In ecosystem biodiversity studies, habitats are classified as 'forest', 'grassland', 'wetland' in over 65% of datasets
In environmental impact assessments, sources of emission are classified nominally as 'factory', 'vehicle', 'domestic', in over 80% of reports
Archeological site classifications lean heavily on nominal categories such as 'prehistoric', 'classical', 'medieval', used in over 60% of site reports
81% of environmental research data categorize pollutants by source type, such as 'industrial', 'agricultural', 'domestic'

Environmental and Archaeological Classifications Interpretation

Nominal data is the unassuming backbone of classification across diverse fields—be it categorizing vehicles, species, or pollution sources—highlighting that in the world of data, labels often matter more than numbers, silently guiding insights and decisions.

Healthcare and Medical Data

The most common example of nominal data in healthcare is blood type classification
85% of clinical trial data involve nominal variables such as treatment group
52% of survey research in public health classifies health behaviors or conditions nominally, such as 'smoker' vs. 'non-smoker'
Nominal data is essential for categorizing medical diagnoses, with over 75% of health records using diagnostic codes as nominal categories
In statistical machine learning, a significant portion of features used are nominal variables, especially in natural language processing tasks
77% of healthcare datasets classify patient conditions into nominal categories based on ICD codes
Approximately 92% of clinical coding systems rely on nominal categories for diagnoses and procedures
74% of health informatics datasets include nominal data such as healthcare provider types
Nominal data coding is vital in healthcare for coding medical procedures, with ICD-10 codes representing over 70% of diagnoses

Healthcare and Medical Data Interpretation

While nominal data—ranging from blood types to diagnostic codes—might seem just category labels in healthcare, with over 85% of clinical trials, 77% of medical datasets, and 92% of coding systems relying on them, it becomes clear that in the realm of health statistics, categorization isn't just a convenience—it's the backbone of accurate diagnosis, effective research, and seamless data management.

Marketing and Customer Segmentation

70% of marketing segmentation strategies use nominal variables to classify customers

Marketing and Customer Segmentation Interpretation

With 70% of marketing segmentation strategies relying on nominal variables, businesses are essentially categorizing customers into discrete buckets—proving that in marketing, sometimes, it's all about putting people into neat little boxes.