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As a part of our customer health evaluation, we need to be able to measure the % of zendesk tickets


solved outside our SLA, in the last 90 days, and then feed this into our health


score with the score moving in line with the %.















The challenge is that:















-   In our ZD data, we know the number of minutes it


took to solve a ticket but do not know whether SLA was met or not (unless


manually calculated)















-    We have 4 ticket priorities with different SLAs


so there are 4 different scenarios determining whether a ticket’s SLA was met (e.g.


SLA of Urgent tickets is 540 mins)











I believe Gainsight can calculate the % of tickets solved/not


solved with the SLA but can it also support the 1st step of identifying


whether SLA was reached or not? and if so, how best to combine these steps


together and feed them into one healthsocre measure, updated daily?















Thanks in advance!















 Katerina



Hi Katerina, I'm assuming that for each of your 4 ticket priorities, the SLA time duration is the same regardless of customer segment or region? For example, the SLA for Urgent tickets is always 540 mins, the SLA for High tickets is always 1000 mins, etc





If so, one way to determine programatically if the SLA was met is to perform a subtraction action using a formula field in a Bionic rule. You'd subtract the SLA time from the actual resolution time and if the result was positive, the SLA was not met. 





For example - An urgent case took 600 mins to resolve. Subtracting the SLA time you get: 600-540=60. So the SLA was not met. 





Then using a pivot action in bionic rules you can group all positive numbers (failed SLA) and negative numbers (met SLA) and aggregate them by Account.





If you then wanted to determine what percent of tickets by Account met that SLA, just do one final formula field to compute (met SLA / total cases).





Does this help?
Hi Dan,





Many thanks for the suggestion - I'll give it a shot today and see if I can make it work.


Katerina 

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