Buying a T-shirt, in North America at least, means that you pick a shirt that has a label that that reads either Small, Medium, Large, Extra Large, or depending on the manufacturer, maybe Extra Small or Extra-Extra Large. This simplifies the choices rather than forcing an exact measurement.
It’s not about precision; it’s about being in the right ballpark. Estimating and approximating.
When it comes to numerical analysis, just about any metric – revenues, costs, receivables or payables balances, etc. – can be slotted into one of these familiar categories, and a useful and meaningful overview of the topic can be achieved.
Here’s how to implement this approach quickly and easily with Monarch.
We’ll assume that we simply want to analyze the distribution of revenue generated by our customers. First, create a new calculated Character field named Size, with an expression similar to this:
if(Revenue<10000,"2 Small",
if(Revenue<250000,"3 Medium",
if(Revenue<3000000,"4 Large",
if(Revenue<10000000,"5 X-Large","6 XX-Large")))))
By adding the 1 through 6 prefixes to our Size labels we can easily use the Size field as a key field in a Monarch summary which will be sorted in the familiar manner, small to large, rather than being based on the normal alpha sort.
The amounts are arbitrary of course, and you should change them to suit your needs. You could even go so far as to create runtime parameter fields for each category level. Users of Monarch Pro could extend that functionality easily into other models by simply importing the new parameters, and the Size calculated field, into other models, and enhance a number of models in no time flat.
The expression for the calculated field using runtime parameters (RP) 1 through 5 could become:
if(Revenue<RP2,"2 Small",
if(Revenue<RP3,"3 Medium",
if(Revenue<RP4,"4 Large",
if(Revenue<RP5,"5 X-Large","6 XX-Large")))))
RP6 wouldn’t be needed as values larger than RP5 would default to the last label.
The expression could even be revised to incorporate the threshold into the label:
if(Revenue<RP2,"2 Small: < " + trim(str(RP2/1000,9,0)) + "k",
if(Revenue<RP3,"3 Medium: < " + trim(str(RP3/1000,9,0)) + "k",
if(Revenue<RP4,"4 Large: < " + trim(str(RP4/1000,9,0)) + "k",
if(Revenue<RP6,"5 X-Large: < " + trim(str(RP5/1000,9,0)) + "k","6 XX-Large: < " + trim(str(RP6/1000,9,0)) + "k")))))
Next, build a new summary using the Size field as a Key field, and simply using the count as the only measure. This will show the distribution of the customers, and instead of having a gut feel for what the distribution would be, you’ll know exactly. To make the summary even more useful, add a second instance of the count field and set it to display a percent of the total. Now you’ve got a real handle on where things are: what needs attention, and what’s doing just fine.
Does the T-Shirt concept fit?
How will you take advantage of this technique to benefit your organization?
T-shirt size estimating can be done very easily with Monarch, thanks to the summary feature, and provides powerful and flexible insights with minimal effort. It’s a great way to XL (sorry, couldn’t help myself) with Monarch.
