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Writing a good performance measure requires four essential parts to make it both understandable and implementable.

When we write our measures or KPIs as two- or three-word phrases, like Customer Loyalty, Employee Engagement, Turnaround Time and Sales Call Efficiency, it makes it impossible to implement or report them properly.

The trouble with writing a measure or KPI with just a few words is that it leaves too much ambiguity about how exactly it is calculated. You don’t have a real measure until you’ve articulated how its values will be calculated. And that means we need to know how to clearly write quantitative KPIs and measures.

Take Customer Loyalty for example. We could argue that the values of this measure could be calculated in several different ways:

• median number of sales per customer
• total number of years that each customer has been purchasing from us
• percentage of the customer base who have given us repeat business
• average rating of likelihood that the customer will continue purchasing from us
• Net Promoter Score
• and no doubt there are many more…

And what if customer loyalty is really more than just one of these quantitative calculations? Each measure name should focus on quantifying a single attribute. We can’t leave our measure calculation to chance like this. It’s like baking a chocolate soufflé – if we don’t get the recipe right, it is guaranteed to flop.

For meaningful KPIs, a recipe can help us avoid flops, too. We can make our KPIs and measures easier to understand and implement accurately by using a 4-part recipe for how we write their quantitative description and give them a more deliberately chosen name.

Part 1 of a quantitative KPI: the statistic.

Decide what the best form of summary statistic is to turn the raw data into the values of your measure. There are five basic ways to quantify KPIs or measures: number (count), total (sum), percentage, average, or a ratio of two different measures. Of course there are other statistics you can use, but these are less common, such as: median, mode, maximum, minimum, and range.

Usually the statistic begins the description, as you can see in the following measures, with the statistics highlighted in bold:

• Order Turnaround Time: Average number of days from order request to order delivery, for completed deliveries, calculated weekly.
• Commercial Waste to Landfill: Percentage of tonnes of waste produced that is sent to landfill, for commercial organisations, by quarter.
• New Referred Customers: Number of new customers referred by an existing customer, by month.
• Operating Costs: Total expenditure on day-to-day business operations, by month.
• Sales Speed: Total revenue received divided by average sales lead time, by quarter. (This one is a ratio of two measures.)

If you don’t include this first part of the KPI recipe, then you aren’t really measuring anything, because you’re not making it clear how performance is being quantified. Every meaningful performance measure has a deliberately chosen quantification statistic.

Part 2 of a quantitative KPI: the performance attribute data item(s).

Clearly identify the data item, or data items, you are applying the statistic to. What exactly are you averaging? What exactly are you counting? What exactly are you taking a percentage of?

In taking a percentage, you really have two performance attribute data items. Take the example:

• Commercial Waste to Landfill: Percentage of tonnes of waste produced that is sent to landfill, for commercial organisations, by quarter.

The two performance attribute data items are:

• tonnes of waste produced
• tonnes of waste sent to landfill

But for this measure quantified using a count, ‘new customers’ is the only performance data item:

• New Referred Customers: Number of new customers referred by an existing customer, by month.

You might be wondering, what about ‘referred by existing customers’? That’s not a performance data item, but it is a different kind of data item, as you’ll see now…

Part 3 of a quantitative KPI: the scope data items.

Scope data items define the extent of the performance area that the measure should relate to. They define what to include in the measure, or what to exclude. They define the subset of the whole performance area that the measure should tell us something about.

In this measure description, the scope data item is ‘referred by an existing customer’:

• New Referred Customers: Number of new customers referred by an existing customer, by month.

You can imagine in a data set of all customers, there might be a field that flagged how the customer found out about the company. One of the values of this field could be ‘existing customer’ (other values might be ‘web search’, ‘TV advertisement’, ‘social media’, etc…).

And for a few more measures, the scope data items are in bold:

• Order Turnaround Time: Average number of days from order request to order delivery, for completed deliveries, calculated weekly.
• Commercial Waste to Landfill: Percentage of tonnes of waste produced that is sent to landfill, for commercial organisations, by quarter.

Not every measure has a scope data item, but it’s essential to give it some thought, just in case you discover the measure has more meaning if it’s a bit less diluted.

Part 4 of a quantitative KPI: the temporal data item.

Contrary to popular practice, the frequency with which you measure something should not be chosen to match exactly with reporting frequencies (they can be aligned, though). Just because you have a monthly report does not mean your measures’ values must all be calculated monthly.

The frequency of your measure’s calculation should be chosen as frequent enough to detect signals as soon as possible, but not so frequent that your signals drown in noise. Most measures have too low a frequency, and therefore give very dull or delayed signals.

Your chosen frequency of calculation for your measure becomes the temporal data item to include in the description, like you can notice in bold for these measures:

• Order Turnaround Time: Average number of days from order request to order delivery, for completed deliveries, calculated weekly.
• Commercial Waste to Landfill: Percentage of tonnes of waste produced that is sent to landfill, for commercial organisations, by quarter.
• New Referred Customers: Number of new customers referred by an existing customer, by month.
• Operating Costs: Total expenditure on day-to-day business operations, by month.
• Sales Speed: Total revenue received divided by average sales lead time, by quarter.

If you’re not yet sure how frequently to calculate a measure, err on the side of too frequent, and make your final decision when you see the measure values, calculated and displayed in a time series chart. For example, if you’re not sure a measure should be monthly or quarterly, start with monthly and only change to quarterly if it appears you don’t have enough data for monthly to make sense.

Is writing a KPI or measure really that simple?

Yes… and no. You need to follow this 4-part recipe as the framework of writing a quantitative performance measure or KPI. But your performance measure might have a more complex calculation that needs more than one statistic, or several performance attribute data items, or a few scope data items.

A good example is this performance measure:

• International Training Gross Profit Margin: Total sales revenue less total cost of sales, expressed as a percentage of total revenue, for all training workshops that are held internationally, by quarter.

But always, start simple and small when you’re trying something new. That way you’ll lay a solid foundation of know-how and be much better prepared to tackle the more complex KPIs.

Make your KPIs understandable and implementable.

The first tremendous advantage of writing our KPIs and performance measures quantitatively, with this 4-part recipe, is that it’s far easier for anyone and everyone to understand what we’re really trying to measure. Rarely do we have a measure that doesn’t need the buy-in of others and buy-in is impossible without understanding.

The second tremendous advantage is that it’s easier to work out what data is needed and how to turn that data into the values of the performance measure. And this means we’ll have a much higher chance of success in implementing each KPI – the way we intended it to be.

And this recipe isn’t just useful for new KPIs. Do you have any KPIs or performance measures that people don’t understand or cannot implement? They are perfect candidates to improve, by reworking them to follow this recipe.