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Calculate the average weekly or monthly time it takes your team to close a conversation initiated by a customer.

The process

Understand the performance of your customer support team and improve your customer satisfaction. You can have a breakdown by time cluster:

- % of conversations closed within 24h

- % of conversations closed within 48h

- etc.

1) Import in a sheet the "Conversations" from Intercom. Call that sheet "Intercom conversations"

2) We'll use the columns "Statistics last close at" and "created at" to calculate the number of days between the time when the conversation was created and the when it was closed.

Let's add a calculated column called "Delta from creation to close" with the following formula: `=#Statistics last close at - #Created at`

3) Now you can write in any cell the following formula `=AVERAGE(#Delta from creation to close` to have the average close time.

4) Let's add a new calculated column called "Close time bracket" with the formula `=IFS(#Delta from creation to close<1, "<1="" day",="" #delta="" from="" creation="" to="" close<="3," "1-3="" days",="" close="">3, "+3 days")</1,>

5) Now let's build a dashboard on a new sheet called "Dashboard"

Write in B2, B3, and B4 the following values "

Then on C2, write the formula `=COUNTIF(Dashboard!#Close time bracket, B2)` and copy and paste the formula in C3 and C4

You now have the distrubution of conversations per Close time bracket. This is usually much more interesting than the average.

Indeed, an average of 1 day could mean you close all your requests in exactly 1 day (which would be okay if your objective is to close conversations within a day) or that you close 50% of requests immediately and 50% after 2 days (which wouldn NOT be okay since you would underdeliver for half of the conversations)

Of course, you could easily add more dimensions to this analysis: Month, week or customer support agent.

Understand the performance of your customer support team and improve your customer satisfaction. You can have a breakdown by time cluster:

- % of conversations closed within 24h

- % of conversations closed within 48h

- etc.

1) Import in a sheet the "Conversations" from Intercom. Call that sheet "Intercom conversations"

2) We'll use the columns "Statistics last close at" and "created at" to calculate the number of days between the time when the conversation was created and the when it was closed.

Let's add a calculated column called "Delta from creation to close" with the following formula: `=#Statistics last close at - #Created at`

3) Now you can write in any cell the following formula `=AVERAGE(#Delta from creation to close` to have the average close time.

4) Let's add a new calculated column called "Close time bracket" with the formula `=IFS(#Delta from creation to close<1, "<1="" day",="" #delta="" from="" creation="" to="" close<="3," "1-3="" days",="" close="">3, "+3 days")</1,>

5) Now let's build a dashboard on a new sheet called "Dashboard"

Write in B2, B3, and B4 the following values "

Then on C2, write the formula `=COUNTIF(Dashboard!#Close time bracket, B2)` and copy and paste the formula in C3 and C4

You now have the distrubution of conversations per Close time bracket. This is usually much more interesting than the average.

Indeed, an average of 1 day could mean you close all your requests in exactly 1 day (which would be okay if your objective is to close conversations within a day) or that you close 50% of requests immediately and 50% after 2 days (which wouldn NOT be okay since you would underdeliver for half of the conversations)

Of course, you could easily add more dimensions to this analysis: Month, week or customer support agent.

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