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One of the ways that people like to classify their KPIs and performance measures is by whether they are quantitative or qualitative. Should we do it? And if we do, are we doing it right?

The distinction between quantitative and qualitative measures is often misunderstood. Really, if we’re measuring anything, we’re gauging the amount to which it’s happening. And numbers are the essential building blocks of amounts. Even when we use rating scales to turn attitudes into numbers, we’re doing it to gauge an amount.

So, technically, every measure is quantitative or numeric. Even when we’re measuring a qualitative result, we still use numbers to quantify it. Let’s explore this some more…

Qualitative measures aren’t actually measures.

In the field of statistics, or data analytics, we distinguish variables as qualitative (or attribute) when those variables are not gauging an amount but rather are simply putting things into buckets. The buckets are classifications like gender or market segment or geographical region or product group.

Qualitative variables can’t really be performance measures. Rather, they are used to help us analyse our measures. For example:

• We can slice Customer Satisfaction Rating into product groups to explore which products have the lowest satisfaction and should be priorities for improvement.
• We can dice Employee Engagement Ratio by profession and location to explore where morale might need boosting.

Qualitative data can also be richer than just attributes or classifications, such as stories or explanations. And that kind of data can be analysed to find themes or causes or further contextual information to help us decide what kinds of actions to take when our quantitative measures tell us performance isn’t good enough.

Quantitative measures can take two forms.

In the field of statistics or data analytics, we distinguish two types of quantitative variables: continuous and discrete. Continuous variables can take any value (including decimals) over a range, and are measured in units like kilograms, hours and minutes and seconds, dollars and cents, metres. Can you think of any other types of continuous data used for KPIs in your organisation?

Discrete variables are generally counts of things like complaints, accidents, new customers – anything that takes an integer (whole number) value. This includes rating scales for measuring attitudes, such as satisfaction on a 10-point scale. Do any other KPIs based on discrete data come to mind?

Performance measures can be based on either continuous or discrete variables. Measures such as Average Delivery Cycle Time or Net Profit and Non-recyclable Mass Sent to Landfill and Average Kilometres Travelled are based on continuous variables. Measures such as Average Customer Satisfaction Rating and Number of Lost Time Injuries and Percentage of Projects Completed On-Time are based on discrete variables.

Both types of performance measures – continuous and discrete – are equally useful. Next time when the discussion comes up about quantitative versus qualitative KPIs, make sure to check what your colleagues mean by ‘qualitative’ and whether they really mean ‘discrete’. This might help everyone get past the perceived barrier that qualitative concepts can’t be quantified.