How to calculate an average with dimension items number?

KHRR1

17-07-2020

Hello there,

 

First, here is a little bit of context for you.

 

We have a product where we can see some tickets coming from the back-end system.

Each ticket contain an information that means for how long the ticket has been in the system in minutes.

When the ticket has been handled, we close it using a button (tracked with an event).

 

Now, i've been asked if it possible to know the average time it took to close a ticket in our Adobe Workspace. 

 

So, at first i was thinking that using a Calculated Metrics wich could be some thing like that :
= ((Dimension value = 1 * number of occurences of that value)+(Dimension value = 2 * number of occurences of that value)) / Total of occurences 

 

The thing is that dimension could return me an infinite (ish) numbers of value (as we can have ticket solved in 2min or in 2880min (2 days), and if we look at the calcul that i thought about, i would have to create 2880 items in my calculated metric (No thanks!).

 

Do you have any idea to do that and be able to display that information in Adobe Workspace without having to export the data and then import it in Adobe ?

 

Thanks a lot for your time and help !

 

 

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Answers (1)

Asheesh_P

MVP

19-07-2020

@KHRR1 I would classify it in meaningful time breakout through classification or segmentation. It will give you an idea about what percent of tickets are being closed in your preferred buckets thereafter you can explore the reason behind it. Avg. time calculation may be very high and misleading due to outlier tickets closing time. Ex. Let say out of 5 tickets 2 gets close in 2 hr, 1 in 5 hr and 1 in 12hr and 1 in 48hr. 

Classification Table:

less than 2 hr - 2 - 40% (row occurrence/total occurrence)

2-5 hr -1  - 20% 

5-24 hr - 1 - 20% 

more than 24 hr - 1 - 20%

 

Avg Time to Close ticket

While avg. time to close ticket will show it like 2*2 + 1*5 +1*12 + 1*48 = 13.8 hr. while it is 5.25 hr for 80% cases and 3hr for 60% cases.

 

-Asheesh