You have only recently redefined your service process. From ticket registration to closure and solution documentation, there are only a few process steps that the team should quickly go through thanks to optimal support from software tools. But in practice, your service centre does not reach service levels agreed with customers? High-priority tickets get stuck in the process for a long time and annoy your most important customers?
Then you should take another look at the service process with the help of process mining. This method reveals the differences between the actual process and the ideal model. This will help you understand why some tickets simply remain unprocessed, while others make it through the target cycle.
Process Mining: What is it?
The core process for customer service should describe as direct a path as possible from a fault report to the resolution of the problem. In between, there are escalation levels, for example if the remote service team cannot resolve the problem remotely. Then the onsite service takes over the ticket. Each such process triggers a transaction in the defined workflow. The service platform keeps an event log in which each of these steps is stored with a timestamp.
By definition, process mining tools use this data to track the path of each ticket through the service process. Since large amounts of data are involved, the method is also called process data mining. The result is a graph of the actual process. This shows loops that some tickets take in the process. This happens, for example, when several agents assign the ticket to each other. Process mining also determines the duration of the individual process steps. This shows you where the bottlenecks are in the process. Through targeted improvement measures, you can significantly shorten processing times.
The result of the process mining shows it: From the time the customer reports a problem by phone to the time the case is forwarded to an agent, an average of 30 minutes passes. That is valuable time, especially if it is a known problem. What happens in these 30 minutes? A team member takes the call and categorises the problem. But before forwarding the call, he or she has to manually enter data about the customer and the machine into the ticket. This costs valuable time and is prone to errors. Thus, process mining makes it clear that it is time to automate data entry.
Process mining tools uncover problems in the service
Workflow optimisations in the service process are an effective lever for increasing service quality. Fast and efficient problem handling reduces your costs and binds satisfied customers. Process mining thus creates the basis for optimisations:
- Holistic view: Process mining maps the complete process. However, many service employees are only involved in phases. With the graph, you can give your team a comprehensive picture of the consequences of their actions.
- Transparency: Where is the cause of delays in the process? You can recognise them by the dwell times of the ticket in the individual stages of the process. The graph also conveys which exceptions there are that are not considered in the target process.
- Objectivity: Process mining does not lie. The data-based approach gives you a distortion-free picture of the actual state. You do not have to rely on your impression or that of the team.
- Continuous improvement: Process mining shows where you and your team currently stand. It provides approaches for permanent optimisation.
Transparency for the path to becoming a service champion
With a process mining tool you create transparency in the service process. This is the best starting point for optimising process and workflow. We will show you how the Smart Service Solution from MEXS allows tickets to take the direct route through the system at a demo meeting.