Segmentation techniques were deployed to predict the likelihood of an IT Ticket getting reassigned in an online IT service management application. A robust segmentation model was built, despite the predictor variables being largely categorical in nature. Custom logic was developed to circumvent the absence of an in-built R function to implement the CHAID algorithm. Distinct segments of IT incidents were identified that were prone to reassignment to various degrees, providing the client a handle to exercise greater care in initial assignment of those IT incidents that were more prone to reassignment.
The client is a US-based provider of online IT service management products & solutions. The client’s product suite addresses its end-users’ IT service management needs through efficient routing of IT incidents (service requests) to appropriately-skilled service agents. For each service request received at the end-user’s site, an associated IT Ticket is assigned to a service agent.
The performance of an IT service management system hinges critically on the robustness of the assignment of IT incidents (through IT Tickets) to service agents who attend to such IT incidents and resolve them. IT incidents can either be pre-recorded in internal systems (as is the case with scheduled software installation, upgrades, etc) or arrive at the IT service desk in a dynamic fashion (such as requests for bug fixes, unscheduled outages, etc). IT Incident Queue Management is very subjective due to the inherent nature of a typical IT service desk. Organizations have a number of popular queue-system designs to choose from, such as, single-queue single-desk, single-queue multiple-desk, multiple-queue single-desk, and multiple-queue multiple-desk. Across the world, there are many organizations that have extracted desired throughput levels from their service desks via the single-queue multiple-desk approach, as there are many others that prefer to deploy the multiple-queue multiple-desk configuration.
What works best for a particular organization depends substantially on its queue-system design and the mechanism used to assign IT incidents to service agents, apart from the depth & breadth of the skills & competencies of its service desk personnel. The best throughput is achieved when an IT incident is assigned to an agent that can resolve the incident in the least time. Hence, assigning an IT incident (i.e., the associated IT Ticket) to the ‘right’ service agent is a critical success factor for an effective & efficient IT service management system. Failure to assign the IT incident to the appropriate service agent necessitates subsequent reassignment of the IT Ticket (sometimes multiple reassignments), leading to delays in problem resolution and erosion of customer satisfaction levels.
Accordingly, it is imperative to minimize the need to reassign an IT Ticket and, to this end, predict the likelihood of an IT incident getting reassigned.
To predict the likelihood of an IT incident (or Ticket) getting reassigned, exploratory analysis was conducted on data sourced from the back-end database. The reassignment likelihood could depend on a number of factors, such as priority of the Ticket, severity of the problem, impact of the problem, location of the problem, reporting mode of the incident, location of the problem, type of the problem, etc. Data was available for over 150,000 IT incidents, and there were around 75 variables to describe the particulars of each incident.
While predicting the likelihood (i.e., the probability) of an IT incident getting reassigned is of direct interest to the client, there is additional value in understanding if certain characteristics of an IT incident increase/ decrease the likelihood of the said incident being prone to reassignment. Accordingly, whereas a straight-forward analytical approach involved Predictive Modelling, classification techniques were deployed as well (in particular, Segmentation) to uncover any underlying clusters among the IT incidents.
Standard predictive modelling techniques require the predictors to be numeric in nature. Of the 75 variables available in the data, only 10 were numeric, the rest being categorical in nature. This severely limited the applicability of standard techniques like logistic regression. While categorical data can be transformed via variable binning and dummy variable creation approaches, the efficiency of predictive model greatly reduces if there are too many dummy variables. Accordingly, it was preferred to fall back on classification and segmentation techniques for our problem at hand.
Both CART and CHAID segmentation based solutions were explored. Given the client’s desire for a cost-effective solution, an open-source tool, viz, R, was envisaged to design the solution. The data at hand did not yield any useful segments through the CART algorithm, and hence the CHAID algorithm was explored. Given the absence of an in-built function for CHAID in R, a custom logic was designed to implement the CHAID algorithm in R.
The final solution was built through CHAID segmentation. In the absence of a proper CHAID function in R, a new approach was designed, developed and implemented to achieve the desired objective.
The data revealed 12 distinct segments of IT incidents, based on incident characteristics. With information at hand on the segment profiles, the client could customize the IT Ticket assignment process logic to ensure greater care in assigning service agents to incidents that are more prone to reassignment.