Have you imported all the data using Exchange connector in Power BI Desktop? If you have all the data of different persons, you can create measures/columns to calculate the duration.
@ Lydia @v-yuezhe-msft
Thanks for the response Lydia.
Yes. We already have the data as it is in import mode.
However, the problem here is, I m unable to find a way to find related emails.
While I do understand, that there might be anamolies (like user changing subject line..etc) is there a way we can still determine related emails for most of the cases?
We could be seeing several emails from same person. So it would not be easy to infer only on the basis of sender and recepient.
For example consider the following steps:
1. User logs a support ticket by composing an email to support desk.
2. One of the support executives respond to the email asking for more details.
3. User replies back in a day or two
4. Support executive replies to this email and provides the solution and sends an email asking for cofirmation.
5. User confirms and closes the ticket. [ Total time = time since step 1 till closure of ticket]
The same user meanwhile, can log another support desk and the above steps would be followed.
We therefore need to track the related emails (conversation history) in order to determine the period.
I m keen to know how this is being achieved in MS outlook as this is exactly what we require. Trace the conversation history, in order to infer/ calculate total difference in time.
Is there a field that would help me identify/ group related emails?
I'm trying to achieve the exact same thing to determine whether teams are meeting their Service Level Argeements . Did you get an answer or find a solution?
I didn't explore the solution as the requirements changed.
However, I found a ConversationIndex and ConversationTopic on expanding the "Attributes" Object.
Hope this helps.
If this works let me know 🙂
I suspect you're right and there is a way to use the index to track recognition of an email/SLAs but I just need top find out how haha
Thanks again 🙂
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