When you’re asked to come up with a Key Performance Indicator (KPI) to measure your strategic goals, do you consider the pros and cons of the various ways to quantify your measure? It’s easy to default to the first quantification method that comes to mind when we’re designing a measure. But the default isn’t always the best choice. I’ve realized, when working with my clients, that helping teams understand their options has helped them produce much better measures.
Here are some considerations to help you make more deliberate decisions about whether a measure should be a count, a percentage, or an average.
Consideration 1: existence versus rate of occurrence
For some goals, we’re interested in straight-out counts because it’s the existence of something that is important to improve, rather than its rate of occurrence. We simply want more of the stuff, or less of it.
- Number of lost-time injuries. This measure counts the existence of injuries, and not the rate of occurrence of injuries in a population (like a workforce). It does this because we’re interested in ending lost-time injuries altogether, irrespective of how the injury rate might be changing relative to the opportunity for injury to occur (this might also be measured, but it measures a different result). Here, we simply want less and less injuries to exist. We don’t like the idea that growing our workforce consequently means more people will be injured.
- Revenue. This measure is a simple count of dollars from sales, and it is meaningful just on its own. We’d measure this when we’re interested in seeing the overall revenue grow, irrespective of rate at which revenue might be generated from the number of sales or number of customers (these might also be measured, but they measure different results). Here, we simply want more and more revenue to exist.
Consideration 2: normalization for accurate detection of change
For some goals, it’s more useful to know the rate of occurrence to really understand if change is happening. A simple count would mislead us to the wrong conclusion about trends, so we need to normalize the measure.
- Percentage of work hours spent doing rework. If getting things done right the first time is our goal, then this measure helps us quantify how often that doesn’t happen. We need, though, to compare the rework hours to the total work hours and not just track rework as a simple count. We don’t want to be duped into thinking that we’re better at getting things done right the first time, just because total rework hours are reducing – it could be simply that we’re working less hours.
- Actual budget spend as a percentage of planned budget spend. We’d measure this if we wanted to gauge how consistently we spend our budget, and reduce the peaks and troughs. How much budget we are allocated each year will vary, so tracking just the dollars that are over- or under-spent is misleading – a bigger budget naturally means bigger “overs and unders”. We need to normalize the amount we spend relative to the amount we have to make apples-with-apples comparisons over time.
Consideration 3: degree of sensitivity to change
Then there are goals that are about the degree of change, rather than the rate of occurrence of change. A percentage would tell us only whether or not a result was achieved, rather that the degree to which that result was achieved.
- Average delivery cycle time. If we measure just the percentage of deliveries on time, we have no clue how late the late deliveries are. The average cycle time of deliveries helps us see if, as time goes by, we are managing to deliver faster.
- Average customer rating of overall satisfaction. This is superior to the percentage of customers that are satisfied, because it’s a more sensitive measure of change over time. Imagine if customers are starting to rate 6 or 7 out of 10 instead of 8 or 9 out of 10. The percentage would still class them as satisfied, but the average tells us more: satisfaction is starting to decline.
Next time you are designing a measure for one of your strategic goals, I hope these considerations help you improve your measure design.
Louise Watson is a strategist, planning facilitator and Canada’s Licensed Consultant for PuMP® Performance Measurement Blueprint. She provides PuMP® consulting and workshops through a strategic partnership with Stacey Barr, the Performance Measure Specialist.
