Large enterprises that provide services to consumers may receive millions of customer complaint tickets every month. Handling these tickets on time is very critical, as this directly impacts the quality of service and network efficiency.

A ticket may be assigned to multiple teams before it gets resolved. Assigning a ticket to an appropriate group is usually done manually as the complaint information provided by the customer is not very specific and maybe inaccurate sometimes. This manual process incurs enormous labor costs and is very time inefficient as each ticket may end up in the queue for hours.

In this talk, we will present an approach to automate the process of ticket routing completely. We will start by discussing how we can use Markov Chains to model the flow of tickets across different teams. Next, we will discuss the feature engineering part and why Factorization Machine Models are essential for such a use case. This will be followed by a discussion on the learning of decision rule sets in a supervised manner. These decision rules can be used to traverse tickets across multiple teams in an automated fashion. Thus, automating the complete process of ticket routing. We will also discuss that the proposed framework can be validated easily by SMEs, unlike other AI solutions, thus, resulting in its quick acceptability in an organization. Finally, we will go through the different settings in which this solution can fit, therefore, resulting in its broad applicability.

The framework can provide substantial cost savings to enterprises. It can also reduce Response time to tickets significantly by almost eliminating the queue time. Overall, it can help large enterprises in

1. Saving costs by reducing the workforce of ticket handling team

2. Increasing revenue by improving quality of customer experience