Estimating Contact Center Queue Sizes Using Back-of-the-Napkin Math
Want to know a quick way to gauge a potential queue in an asynchronous contact center? This approach is similar to how a real estate investor calculates the net operating income of commercial real estate and divides it by the cap rate to determine its valuation. Or in personal finance, where you take 72 and divide it by the annual rate of return of an investment to determine how long it will take to double. Essentially, it’s a fast method to estimate queue size. However, a more detailed analysis should be conducted to accurately determine the queue size, just as you wouldn’t invest in commercial real estate or buy an asset without deeper analysis.
Back-of-the-Napkin Math to Gauge Potential Queue:
- Calculate the number of transactions that will enter the queue every hour.
- Determine the average turnaround time of a ticket.
- Multiply point 1 by point 2 to get average queue size.
By the way, I didn’t invent this rule. It's actually known as Little’s Law that is part of queuing theory!
Here’s an example to clarify the law:
A contact center is projected to receive 500 tickets an hour, and the average turnaround time for the contact center is 2 hours. Then, the average queue size will be:
Of course, with any back-of-the-napkin or mathematical theorem, there are some caveats. For me, it's figuring out the average turnaround time of a ticket. This is where having an in-depth capacity model can help one calculate the average turnaround time of a ticket.
With that being said, a well-built capacity plan should have all the inputs required to calculate the turnaround time of a ticket. I will go into detail on how to calculate it in next week’s article, along with some pitfalls to watch out for when using Little’s Law to calculate the queue size in an asynchronous environment. Give it a try and see if you can figure out how calculate an average turnaround time of a ticket, and comment below!