How to effectively track changes in ticket volume?

Ticket volume refers to the number of incoming tickets (service or incident) submitted to your help desk. Studying ticket volume over a fixed time period such as hourly, daily, weekly, or monthly can help you trace patterns in your ticket volumes, understand ticket flow, and plan resource allocation accordingly. An easy way to get a quick overview of ticket volume in each quarter is to create the a pivot view using Analytics Plus (as shown below).

This pivot shows you the number of tickets submitted to the help desk in each quarter, split by category, and the last column gives you the total tickets submitted in each category. Click here to view the interactive version.

This is split by category to help you pin-point 'trouble' categories that consistently record high ticket volumes, and also point out categories whose ticket volumes have come down with time. For example, the above report shows that the ticket volume for the category 'Printer problems' has drastically reduced in Q1 2018 as compared to the previous quarters, while the ticket volume for categories such as 'Faulty hardware' and 'ISP link' has remained more or less the same throughout.

To recreate this report, drop "Created Time (Quarter & Year)", "Category (Actual)" and "RequestID (Count)" in the "Columns", "Rows" and "Data" boxes respectively.

To study the percentage variation in ticket volume for each quarter, simply change the value in the data box to Request ID > Count > Percentage difference from, and choose Columns: Request.Created Time (quarter and year) in the dialog box that pops up.

This will give you a report similar to the one given below

Click here to view the interactive version of this report.

From this report, you can see that there is a 35.17% increase in administrative requests in Q1 2017. But, the percentage increase is smaller in Q2 2017 and has been considerably lower compared to the previous quarter, starting from Q3 2017. The first column Q4 2016 is blank since it is the first reference column in the dataset used.

Click here to check out more interesting reports and dashboards that can help you manage your help desk.