Levels of Quantitative Data in Customer Analytics
One way customer analytics data gets divided is by the four levels of measurement. They’re levels because they start with data that’s more limiting in the type of analysis you can perform to the least limiting:
Nominal: This includes discrete data such as the name of your company, type of car you drive, or name of a product. Nominal means essentially “in name only”; if you have a name, it belongs in this category. Nominal data is qualitative data.
Ordinal: This includes data that has a natural ordering. The ranking of customers by oldest to newest, the order of callers in a queue for a call center, the order of runners finishing a race, or more often, the choice on a rating scale, such as from 1 to 5.
With ordinal data, you cannot know with certainty whether the intervals between each value are equal. In measuring customers’ attitudes toward their experience with products and services, you have to rely heavily on questionnaire data that uses rating scales.
For example, on an 11-point rating scale, the difference between a 9 and a 10 is not necessarily the same as the difference between a 6 and a 7.
Interval: This is data that has equally split intervals between each value. The most common example is temperature in degrees Fahrenheit. The difference between 29 degrees and 30 degrees is the same magnitude as the difference between 78 degrees and 79 degrees.
Ratio: This is interval data with a natural zero point. For example, time to find a product on a website is ratio, because 0 time is meaningful. Degrees Kelvin has a 0 point (absolute zero). The steps in both these scales have the same degree of magnitude.
Whenever you can establish that data is ratio, you can make reasonable deductions, such as “customers are twice as satisfied using a new product version compared to an old version.”
Just because customers’ average rating on one product is a 4 and the rating on another product is a 2 doesn’t mean customers are twice as satisfied. The first rating is definitely twice as high, but unless the scale is both ratio, and calibrated so the numbers correspond to customer behavior, making such claims is risky. It’s best to simply say the rating was twice as high.
Many organizations, statisticians, and even software programs use this hierarchy so it’s important to understand the terms when you encounter them. Some analysts even restrict their analysis based on it.
The figure shows how the levels of measurement fit into the broader categorization of qualitative and quantitative data.