We’ve ran into this issue as well and have struggled with obtaining an understanding of our reopen rate and reopen reasons. We would like to be able to use data to see if our tickets are being reopened due to thank you repsonses, new issues, or the same issue. If it’s the same issue then that tells us our agents are closing the tickets too soon. The idea I am noodling around (without knowing how the customer portals’ reopen function works) is to create a fluid reopen process. Where if there is a response to a Closed ticket it will move the ticket to the New status (same ticket owner or trigger the round robin). The ticket maintains the original create date. A reopen property (#) marks the number of reopens (so first time = 1). The agent sees that is a reopened ticket and reviews if it’s one of the 3 responses above. If it’s a thank you response they can move it back to Closed, marks the original closed date, and marks the solved reason as a thank you response. If it’s a new issue the agent will create a new ticket, associate the email, move the reopened ticket to closed with the original closed date, and mark the solved reason as new issue. If it’s the same issue the agent will work the ticket to Solved as normal. Once in solved the agent must choose the solved reason (solved). This way you can pull reporting that shows tickets tagged as reopen broken down by solved reason to see if your agents are not solving the issue the first time around or if they are and customers are not using the proper channels for new requests/feedback.
Not sure if this is the best route to take but a simple solution that provides helpful insight is preferrable (without having to create a lot of workflows that can break, causing more time in troubleshooting and a messy ticket pipeline).