Variables Made Simple: A Guide to Marketing Data Types
Learn about the types of variables that shape your data and marketing strategies.
Data is used in almost every aspect of marketing, from campaign effectiveness analytics to customer segmentation. Behind all of the numbers and charts are variables. Variables are the foundation of your data analysis. They help you determine what to track, how to interpret what you see, and how to connect different data points. Understanding variables is useful for anyone who wants to make informed, strategic marketing decisions, not only data scientists.
Let’s look at the many types of variables, how they connect to marketing, and why they’re important for your success.
What Are Variables?
A variable is any piece of data that can change. Think of it as the “what” you’re measuring or tracking. For marketers, these are the metrics and characteristics you track daily, such as:
- The amount of money you spend on an ad campaign.
- The number of visitors to your website from social media.
- The age of customers who are most likely to purchase your product.
These are all variables, key pieces of data that can help shape your marketing strategies. The next step is understanding how these variables fit into different categories, which allows you to analyse them in more depth.
The Two Main Categories of Variables
1. Quantitative Variables (The Numbers)
Quantitative variables are all about numbers. These are the kind of variables that deal with amounts, quantities, or measurements. They are very useful for measuring performance and detecting trends.
- The number of conversions from an email campaign.
- The total revenue generated from a product launch.
- The average amount spent by a customer on your website.
There are two types of quantitative variables:
Continuous Variables:
Continuous variables can take any value within a range. These values are not limited by set units, and they can include decimals or fractions. For example, the amount of time someone spends on your website is continuous because it can be measured down to fractions of a second.
Discrete Variables:
Discrete variables, on the other hand, represent counts. These are whole numbers, and there’s no possibility for fractions or decimals. They typically relate to things you can count or count up to. For instance, the number of products sold during a specific campaign (e.g., 200 units).
Discrete variables are useful for tracking events like the number of clicks on an ad, the number of products purchased, or the number of leads generated.
When you’re counting things like how many customers signed up for a service, the number is pretty straightforward—you can have 10 or 11, but not 10.5. The same goes for things like sales, clicks, or subscribers. But sometimes, numbers like these can be more flexible. For instance, if you’re calculating the average number of customers visiting your store per day over a week, you might get a number like 10.5.
That’s because certain variables can shift between being continuous or discrete, depending on how you’re measuring them.
2. Qualitative Variables (The Categories)
Qualitative variables are categories or classifications. Instead of numbers, these variables describe traits or qualities of things, people, or experiences. In marketing, these are used for segmenting your audience and identifying patterns that might not be as obvious in numerical data.
- The type of content that drives engagement (blog posts, videos, infographics).
- The payment methods your customers prefer (credit card, PayPal, bank transfer).
- The geographical location of your target audience.
Like quantitative variables, qualitative variables are broken down into subcategories:
Nominal Variables:
These are categories with no specific order. For example, if you’re looking at customer preferences between different colours of a product, the colours (say, red, blue, and green) are nominal variables.
Ordinal Variables:
Ordinal variables, in contrast, have a specific order or ranking. However, the differences between the categories are not necessarily uniform. For instance, if you ask customers to rate a service with “Very Satisfied,” “Satisfied,” and “Dissatisfied” shows a clear order, but the gap between each level is not necessarily the same.
Some qualitative variables can change based on how you use them, just like quantitative ones.
Take age, for example. If you’re recording the number of years a customer has lived, it’s a discrete quantitative variable. But if you’re calculating the average age of your customers, it becomes continuous. On the other hand, if you group customers into age ranges, age is an ordinal qualitative variable.
So, it’s important to consider how the data is collected and how it’s being used to determine the type of variable.
Independent vs. Dependent Variables: How They Work Together
Now that we’ve covered the different sorts of variables, let’s look at their relationships. Each variable can be classed as independent or dependent.
Independent Variables:
These are the things you control or change. In marketing, these might be things like the time of day you send an email, your advertising spend, or the types of content you create. Essentially, these are the variables that you experiment with to see how they impact your results.
Dependent Variables:
These are the outcomes that you measure. Dependent variables change based on the independent variables you test. For example, if you increase your ad spend (independent variable), the number of clicks on your ad (dependent variable) may increase as well.
Why Marketers Should Care About Variables
When variables are properly classified and interpreted, it becomes easier to optimise your marketing strategies.
- Choosing the Right Metrics
When you understand the types of variables you’re working with, you can choose the right metrics to track. For example, if you’re tracking customer satisfaction, an ordinal variable like “satisfaction rating” is ideal. If you’re measuring conversion rates, a continuous variable is more appropriate. Knowing which variables are at play ensures that you’re gathering the right data. - Understanding Relationships Between Variables
Variables often work together to influence outcomes. For instance, your marketing spend (independent variable) may affect your sales (dependent variable). By identifying which variables influence others, you can see patterns and relationships that help you adjust your strategies for better results. - Selecting the Right Visualisations
Knowing whether you are working with qualitative or quantitative variables helps you choose the right charts for the job. For example, if you’re analysing CTR based on the ad format, you may use bar charts. If you’re reviewing audience segments and CTR, pivot tables are more suitable. This also applies to predictive models like simple linear regression and clustering.
Download Your Marketing Variables Guide
Ready to get started with understanding and working with variables? Download this free guide, which includes:
- A quick-reference table of variable types and their marketing applications.
- A step-by-step checklist for identifying variables in your data.
- Recommendations for charts and graphs based on your variable types.
- How to run a test analysis to ensure you’re on the right track with categorising variables
- A complete example of variable analysis with Red Kite Ski Shop.
