It has been observed that sometimes a client that is not seen very often will have a rush of tickets submitted in a brief period of time. I was looking through aggregate ticket data the other day, and the thought occurred to me that there might be a chance I could predict request surges that are sometimes seen with certain clients. I went through our ticket system and found a client that might serve as a good example case for this and started analyzing the data.
The figure does a good job of showing what issues surges are, and it shows how the request volume from this example client is increasing over time. While it’s interesting to note that the request volume is going up, it doesn’t really help us predict issue surges.
This is exactly what I was looking for. We see a few islands of higher than normal request submission. It turns out that for this client, these dates happen to be near repeating deadlines.
Using this data is very helpful. Combining this information with other tools, we know where certain ticket surge hot spots might be, and can plan accordingly to have extra personnel to handle it.