Problem solve
Get help with specific problems with your technologies, process and projects.
Using weighted averages to calculate ASA and abandonment rate
Your ACD should report ASA and abandonment rate for defined time periods, considering the volume handled. However, if management wants you to combine time periods, using a weighted average provides a more realistic picture of a particular measurement versus a straight average
Upper management would like me to use weighted averages for determining
average speed of answer and abandonment rate. I believe they were referring to call volume, which is (10% - 20%) higher on certain days of the week. However, I do not see a direct correlation between call volume and average speed of answer and abandonment rate (due to staffing levels, types of calls, distribution of calls, etc.). So, I'm not sure if weighted averages would apply in this case and I'm not even sure how I would apply weighted averages. Your thoughts?
Your ACD should report ASA and abandonment rate for defined time periods, considering the volume handled. However, if management wants you to combine time periods, using a weighted average provides a more realistic picture of a particular measurement versus a straight average which skews data and weights all periods equally. The example below shows calculations for straight averages and weighted averages.
1000 (80+65+90)/3 = 78.3% (550x80)+(300x65)+(150x90)/ 1000 = 77%
Continue Reading This Article
Enjoy this article as well as all of our content, including E-Guides, news, tips and more.
Step 2 of 2:
Example: Calls handled SVL Monday 550 80% Tuesday 300 65% Wednesday 150 90%
The "right" way to do it depends on your definitions and targets, and how your system calculates. For example, if your target is to meet a certain abandon rate each day, then your system should provide the required data. If you need to meet a target each week, you'll need to look at whether your system provides that performance indicator on a weekly basis, and how it calculates it.
Dig Deeper on Customer loyalty and retention
-
![]()
How to build a neural network from the ground floor
By: Kassidy Kelley
-
![]()
Storage 101: Queue depth, NVMe and the array controller
By: Antony Adshead
-
![]()
Three global contact center benchmarking mistakes to avoid
By: Scott Sachs
-
![]()
Make the right choice between Hadoop clusters and a data warehouse
By: David Loshin
