4-Step Recipe for Writing a Quantitative Measure

December 10, 2013 by Stacey Barr

When we write our measures as two- or three-word phrases, like Customer Loyalty, Employee Engagement, Turnaround Time and Sales Call Efficiency, we cause a lot of trouble for the poor souls who are tasked with reporting the measures.

The trouble with writing measures with just a few words is that it leaves too much ambiguity as to how exactly it is calculated.

You don’t have a real measure until you’ve articulated how its values will be calculated.

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

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

We can’t leave our measure calculation to chance like this. The solution is quite simple, and involves a 4-part recipe for writing a description to accompany vague measure names.

Part 1: The statistic.

Decide what the best form of summary statistic is to turn the raw data into the values of your measure. You have lots of choices: number (count), total (sum), average, median, percentage, maximum, minimum, range and so on.

Usually the statistic begins the description, as follows:

  • Order Turnaround Time: Average number of days from order request to order delivery, for completed deliveries, calculated weekly.

Part 2: 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 month.

The two performance attribute data items are:

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

Part 3: 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.

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

  • New Referred Customers: Total 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’, etc…).

Part 4: The temporal data item.

Contrary to popular practice, the frequency with which you measure something should not be chosen to align with reporting time frames. Just because you have a monthly report does not mean your measures’ values must all be calculated monthly.

The frequency of your measure should be chosen as frequent enough to detect signals as soon as possible, but not so frequent that your signals drown in noise. Measures with too low a frequency tend to 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 so:

  • New Website Visitors: Number of new website visitors by week.

Is it really that simple?

Well yes, and no. You need to follow this 4-part recipe as the framework of describing a measure. But your 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 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.

With very clear and specific descriptions based on this 4-part recipe, it’s easier to work out what data is needed and how to turn it into the values of the performance measure. And this means you’ll have a much higher chance of success in bringing your measures to life – the way you intended it to be.


Share your measure descriptions on the Measure Up blog, and explore with me whether this 4-part formula really does work… or not.


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