Discrete and Continuous Quantitative Data Used in Customer Analytics
Quantitative data is information that’s broken down by concrete numbers — for example, how many products a customer places in the shopping cart (3) or how much revenue you earn from a specific customer ($2,000).
Quantitative data falls into two categories:
Discrete (countable items)
You encounter a lot of numbers when quantifying customer experience with products and services. Knowing whether the data is discrete or continuous dictates the method you use in your analysis and reporting.
Discrete data has finite values, or buckets. You can count them. For example, the number of questions correct would be discrete: There are a finite and countable number of questions.
Other examples of discrete data are
Number of products in your catalog
Number of employees you have
Number of customer reviews for a specific product
Continuous data technically has an infinite number of steps, which form a continuum. The time to find a product on a website is continuous because it could take 31.627543 seconds. Time forms an interval from 0 to infinity.
Other examples of continuous data are
Dimensions of a specific product
Miles to your retail store from a customer’s location
Time for a customer to find the information he or she is looking for on your website
Days until a product ships to a customer
You can usually tell the difference between discrete and continuous data because discrete data can’t be broken into smaller meaningful units. You can’t have half a customer, but you can have half a minute.