Lack of transparency in the processing of service requests often leads to infringement of service level agreements (SLA), and therefore to customer dissatisfaction.

Process Mining provides detailed analysis of the actual process flows and structures for each service type, including the involvement of service teams with the corresponding holding periods.

This makes it possible to optimize the processing of service queries (e.g. improved processes, better team interaction, automation). The benefits include greater SLA compliance, coupled with reduced costs and a reduction in customer complaints.

In many cases, customer surveys on satisfaction cannot be assessed in the context of the actual business processes that have taken place.

In Process Mining, the results of customer surveys (e.g. using net promoter scores, or NPS) can be fed into the specific business transactions and correlated. This enables much more accurate analyses of customer satisfaction in the context of the specific customer experience in question.

Internal process improvements can also be assessed for effectiveness and sustainability by selecting customer test groups. Not every service optimization results in positive customer feedback, and in such cases, the cost-effectiveness of the measure at hand may be worth reconsidering.

Manual corrections, such as in application processing or claims processing, lead to high process costs, and frequently to customer dissatisfaction.

The root cause analysis conducted during Process Mining enables you to identify the precise causes of process loops and specific reworking. On this basis, you can implement measures to reduce reworking and measure sustainability.

Manual process activities are frequently time-consuming and may be suitable for automation.

Process Mining provides a basis and approaches for new automation strategies and technologies. You can thus determine which activities lead to bottlenecks and could be automated – for example, in entering service requests or performing checks (including the generation of queries). In addition to the obvious reduction in costs, automation typically leads to faster throughput times.

Long-running processes frequently generate high costs and are a source of irritation for policy holders.

Process Mining enables you to identify such processes and unusual process routes. Early warning functionality and AI-based root cause analyses help identify sources of disruption, which makes it possible to derive potential action recommendations and optimizations. For example, one possible finding might indicate that assessor assignments for specific types of damages are taking longer than expected.

For operational control of core processes involving applications, contracts, damages, and service requests, there is frequently no cross-system controlling tool.

Process Mining maps the actual business processes that have occurred by reconstructing them across the different systems in a heterogeneous system landscape. Those on the operational business side thus gain a controlling tool that offers measurement, reporting, and analysis of their business processes with regard to all the relevant key figures and process structures. If there are irregularities in process operations, not only is a warning generated, but counter-measures are also derived. This enables you to get proactive about preventing negative customer experiences.

Even though clear process guidelines exist, assessing the compliance of actual processes that have already run is often not possible.

Comparing the mapping of processes that have actually been executed against the target model makes it easy to identify deviations. Measures can then be taken to improve process conformity, and additional information can be made available for internal checks/audits.