IT Helpdesk is one of the core pillars of any technical support organization.
For this usecase, let’s assume that our IT department’s help desk customer satisfaction rating has dipped for three straight quarters and I know we need to reverse this trend. I’ve been given data about hundred thousand closed tickets with information about customers who submitted the tickets, the IT analysts who were assigned the tickets, the type of issue, in what areas, the severity and the priority of the tickets, the days that it was open for and the satisfaction of the customer after it was resolved.
I’m going to leverage the powerful cognitive, predictive and guided visual analytics capabilities of Watson Analytics to investigate what really drives our ticket resolution times and ways to improve on them to bolster our customer satisfaction metrics.
I upload the dataset and after Watson Analytics has ingested and analyzed the underlying data, it is assigned a quality score which helps me understand if there are any irregularities within the data. I start my investigation by just clicking on the data asset. Even without my typing a question, I’m presented with intuitive starting points based on this dataset in the discovery mode. While I can start with any of these recommended and unbiased starting points that include predictive insights as well, I can also ask my own question.
With my intent to understand the drivers of Ticket Resolution times, I type my query in natural language – “what drives days open”. Watson Analytics immediately responds with a set of insights ranked in order of their relevance.
The spiral diagram shows me all the factors that typically drive the number of days that a ticket was open for or in other words the Ticket Resolution Time. The closer a factor is to the center of the spiral, the stronger is the prediction. I change the aggregation type to show me average times and then look at the drivers list. I immediately realize that the combination of the function to which the ticket was assigned to along with the type of the ticket is the most important driver of Ticket Resolution Time.
I quickly give this discovery a name and continue my analysis by looking at the detailed insight for the most important factor combination. Using the Drivers List, I can delve deeper into each of these insights to know more.
Here I look at the interplay between two of my more important drivers of Days Open – the function type the ticket was submitted against and the type of ticket. It’s evident that Hardware Requests take the longest time to resolve.
Moving on, while I can use the suggested insights or start with a visualization, I type my next query to understand more about the functions that the tickets are submitted against. I open the suggested Tree Map to understand how the volume of tickets is spread across my functions. I add Severity and then replace it with Priority as I review and analyze this spread. Systems function shows the highest volume of tickets across all Priority levels.
So far we found out what drove our Ticket Resolution Times and the spread of ticket volume across Functions and Priorities.
I continue my analysis with my next question to understand more about Requester Seniority as it was deemed as an important factor earlier in the spiral visualization. After sorting on Ticket volume, I drag and drop Ticket Type on top of the visualization and also format it.
I save my discovery set and at this point, I can also share it with my colleagues or even give them access to the actual asset for us to co-create going forward. After I explored my data, I can bring it all together in Display.
I create a new Display by choosing an appropriate template and start building my dashboard my dragging and dropping visualizations from my saved discovery sets. I can assemble, format and even edit my saved discoveries and visualizations to generate a better understanding of our IT helpdesk usecase. So I use the discovery set that I just created to populate my Display by just dragging and dropping the required discoveries. I can also choose from a variety of layout and formatting options to further customize my dashboard.
While I’m fine with all the other elements of the dashboard, I open the bar chart in edit mode and replace the existing measure which was ‘Number of Tickets’ with ‘Days Open’ and further change the aggregation to show average Days Open. This helps us to have a closer look into the Ticket Resolution Times across Requester Seniority and Ticket Types.
Finally, I add the filter elements to my dashboard and adjust them to show only those tickets that were ‘Critical’ in Severity and resulted in ‘Unsatisfied’ Customer Satisfaction.
It is pivotal that we minimize unsatisfactory resolution of critical tickets especially those submitted by Management and Senior requesters. I already see areas of improvements in select functions like Access/Login and Systems with regards to ticket volume and Hardware with regards to resolution time.
So to recap, I started with a data about helpdesk tickets and within moments Watson Analytics was able to interpret it and presented me with a visualized list of interesting insights with plain language interpretations that I can easily understand. I was able to understand more about Ticket Resolution Times and that how that is impacted by the Type of Ticket, the function area Seniority of the requester and the experience of the employee it is assigned to. With these insights, I can better allocate my resources to critical and previously unsatisfactorily resolved tickets and provide additional training to those employees that take longer to resolve tickets.