7 Examples of Nominal Data in Business

After conducting extensive surveys and running big-money marketing campaigns, you have a pile of customer data. Do you know how to apply this data to your business?

The important question here is: what kinds of data do you have and how can you put them to good use?

When working with data in any capacity, deal with four main data types—nominal, ordinal, interval and ratio.

In this article, we'll delve deeper into nominal data, associated examples, and analysis.

What is nominal data?

Nominal data is qualitative data assigned to multiple unique categories or groups with no common element and no position order.

Use it to name or label variables with no quantitative value. Think of it as naming variables that numbers can't measure.

Let's explain with an example—suppose a nominal data set contains information about the eye color of different people. The variable grouping here would be green, blue, brown… and other shades.

Notice how there's no numbered value assigned to the eye color. As such, you can't put them in position from lowest to highest and vice versa.

Here are three guidelines to identify nominal data:

• It can’t be quantified, meaning you can’t add, subtract or multiply the data
• It's categorized into groups that are purely descriptive with no numerical meaning
• It has no set order or hierarchy, meaning no category can be greater than the other

Nominal variables may also be represented as numbers and words together. For instance, 1 can represent green eye color, 2 for brown, 3 for blue and so on. The numbers are just for labeling and have no numerical meaning attached.

How is nominal data different from other types of data?

As mentioned, there are four data types on the measurement scale in research and statistics: nominal, ordinal, interval and ratio data. While they fall under the qualitative umbrella, there are a few nuanced differences.

Nominal data uses unordered, named variables, unlike the other data types that use quantitative or numerical values for analysis. Using our eye color example, it organizes the data set based on naming the eye color.

Other data, such as ordinal data, may rank the information according to eyesight power from strongest to weakest. Here, the variable is the level of eyesight that can be quantified and put into order, unlike nominal data, which simply describes the eye color.

7 examples of nominal data

In this section, we'll look at examples of nominal data and how you can use it to analyze and interpret marketing data.

1. Demographic survey

Data pertaining to gender, age and location are collected from demographic surveys. This data tells you who your customers are, so you can find the best way to approach them with your products and services.

Demographic survey data also breaks down a large group of people or customer base into specific segments. It's handy for customer segmentation in SaaS and marketing.

How to use in business:

This type of nominal data is used to make informed decisions relating to marketing and sales.

Suppose you own a unisex clothing brand and want to know if you have more male or female customers from a particular location. Your goal is to attract an equal number of male and female customers from that region. But after analyzing your data, you discover that you have a higher percentage of female-identifying customers, say 70%, than male-identifying customers, 30%.

Based on the insights from this data, you can either create ad campaigns tailored to male customers or produce more male-coded clothing to attract them.

2. Business assessment

Nominal data for business assessment helps you make better decisions to facilitate organizational growth. It provides valuable insights into market preferences, industry dynamics and other essential business variables necessary for developing growth strategies.

How to use in business:

Ask your customers the best way they'd like to receive marketing information on new products. Think emails, ads and website notifications. This will classify the percentage of customers who prefer emails to those who like seeing ads or web notifications.

Some other examples of gathering data for assessing your business include asking questions:

• Do you find our website helpful?
• How will you rate your experience shopping with us?
• Do you have any comments or suggestions to help us serve you better?

3. Service and hospitality feedback

Use this nominal data to understand how customers feel about your business and what they like or dislike about your offering. Not only will this promote customer satisfaction and business productivity, but it will also allow customers to voice their opinions about your products and services.

How to use in business:

“Were you satisfied with our services today?”

“Did you find what you needed?”

A simple Yes/No answer to these questions provide an idea of whether your customers' needs are met. Alternatively, use images or emojis (happy, sad, indifferent) to symbolize customer satisfaction and quickly gather customer feedback.

You can also ask multi-choice or open-ended questions to gain insights into your customer experience and create improvement strategies:

Which of our services was most beneficial to you today?

[Service A]

[Service B]

[Service C]

[Service D]

Describe how you feel about (service)

[Open text box]

We highly recommend A/B testing your surveys to gauge their effectiveness. Create a different version of your survey and send it to a segment of your customer base to find out which one generates more responses.

4. Voting behavior

Voting behavior in customer context throws more light on what your customers prefer from your product and service offerings. It involves understanding the factors and reasons which influence their buying pattern.

Collecting this nominal data helps you understand your customers’ preferred choices to create an effective marketing campaign and can strengthen your customer relationships in the long run.

How to use in business:

Let's say you own a retail store that sells various perfume brands. One way you can use voting behavior is by comparing product variables by asking questions like “Which perfume brand would you prefer to purchase?”

The results will come in the form of the number of people that prefer a particular brand. For example:

• Dolce & Gabbana - 4
• Calvin Klein - 2
• Gucci - 7
• Versace - 5

Analyzing the data helps you understand your target audience better. Then, you can increase the quantity of the preferred products to meet your customer demand.

5. Personality assessments

Consumers' feelings, emotions and individual differences directly affect their buying behavior. Assessing data on your customers' personality traits allows you to segment your target audience and create tailored campaigns for them.

Think of it like this: the more you learn about your customers’ personalities, the better you can adapt your marketing to fit them.

How to use in business:

Suppose an online fishing gear company is interested in learning more about its customers' lifestyles and personalities.

To get the required nominal data for its marketing research, it can run a psychographic data survey to find out what its target customers are like and if they would like to take risks and try something new.

Let's assume the survey results show the fishing gear company's average customers comprise introverts. In that case, it might create marketing campaigns using images of people fishing alone while enjoying peace and solitude.

6. Purchase information

Since nominal data is simply naming variables, all data regarding a customer's purchase information can be nominal data. Think data for shipping orders and other purchase-fulfillment activities.

How to use in business:

During checkout from your site, collect the customer's information for shipping order fulfillment after making payments. You don't need to rank or put these data in order such as name, age and address.

7. Product survey

Every customer's contact with your product goes a long way to determine their perception of your brand. Product surveys give access to information about how your customers feel about your product.

Collecting feedback on customer experiences will reveal your customers' concerns. You can then ensure your product meets their needs by addressing said concerns.

How to use in business:

Send out a survey before the launch of a new product to collect first-hand information on what the market wants. Then use the data to guide your product creation process to create something that fits market needs.

Here’s an example of product survey questions:

• How would you describe our products?
• What key features of our product do you find helpful?
• How can we make this product better?

Types of tests and collection techniques:

Nominal data is usually collected through surveys with open-ended questions, multiple-response choices, and close-ended questions. You can use open-ended questions if you have many labels to capture data. Multi-choice option is best for close-ended questions.

Open-ended questions

This technique collects non-restrictive feedback to questions. It’s inclusive, and it allows the respondents to express themselves freely. A text box to input answers usually follows the questions. One issue with this technique is data quality challenges, as researchers may have to deal with irrelevant data.

Close-ended questions

Close-ended questions give a limited set of answers where respondents can't explain but only choose from the options provided. They may also have the option of inputting their response if it's not on the list, but it has to follow the same format.

Example: Which European country do you reside in?

1. Germany
2. Spain
3. Switzerland
4. France
5. Other (please specify): __________

Some tests also provide a technique for collecting and analyzing nominal data. Examples include Cochran's Q, Fisher's Exact, McNemar and Chi-squared tests.

How to analyze nominal data

Once you’ve collected nominal data, your next step is to analyze it and draw useful insights for your business.

Step 1: Identify and categorize the variables

The first step is to identify the parts of your data you need to categorize and the variables within those categories. For example, how many customers live in the same city? Segment the customers according to location to divide your nominal data into categories.

Step 2: Observe data from surveys and forms

If you've collected your nominal data using open-ended questionnaires and surveys, you may not be able to categorize them until you have observed the data.

For example, you may receive open-ended survey answers from online customers about their opinion of a product. You'll have to read through them and separate the data into different categories of suggestions before making a decision.

Step 3: Decide your goals

After categorizing your data, decide what you want to achieve from analyzing it.

Understanding the purpose of the data makes it easier to determine how you want to measure and apply it in your business. It also guides you in creating future questionnaires, predicting outcomes or confirming a hypothesis.

Step 4: Compute and measure the data

Statistical methods such as mode, frequency distribution and percentages compute the collected data and infer results. Statistical measures find the number of times certain variables appear in your category.

Step 5: Present your results

After your data analysis, present your results in a pie chart or bar graph to visualize the patterns and distributions of your variables. A pie chart uses percentages or proportions to organize data, while a bar graph displays the variables numerically side by side.

Data visualization is an effective way to understand the different categories of your nominal data with higher or lower frequencies. It’s an excellent strategy to boost productivity in your business.

Wrapping up

Collecting nominal data is crucial for any business. It's the least complex way to gain vital feedback to move your business forward. But more than collecting the data, it's essential to know how to use it to avoid bad data management.

A good way is to create a data literacy program for your team so they'd learn how to engage with data to meet your business objectives.