In our ever faster changing world, there are manufacturing companies that master thousands of service calls a year. It goes without saying that in companies like these processes such as order taking, processing, disposition or invoicing are no longer carried out manually on paper or with the support of Excel spreadsheets – a digitized field service management is often the standard.

Due to the complexity of customer service challenges, however, there is a high demand to improve and automate operations in order to be able to keep up the pace. Unpredicted increases in service requests, different channels, long processes and high customer expectations are just a few examples. As a type of artificial intelligence (AI) that enables systems to automatically learn from experience without being explicitly programmed, machine learning (ML) can help since it enables the analysis of large amounts of data without huge programming effort. Here, you’ll read about some of the main advantages ML offers to improve customer service:

ML helps to predict downtimes

One of the main challenges manufacturers are faced with nowadays is to maintain operational stability and to ensure that production targets are met. In order to predict when a machine might fail, it is important to collect, study and analyze data generated by sensors that are installed on machine components. ML tools recognize data patterns, categorize and apply them to new data. One example: the vibrations of components subjected to abrasion wear are recorded by sensors and analyzed by algorithms afterwards.

These algorithms can now predict the probability of failure. The training for these models is based on exemplary data sets. For machine learning to enable predictive maintenance, it must be integrated into business processes and the analytical procedures have to be operationalized. With this in mind, appropriate models are just as obligatory as the linkage with business rules. Predictions can thus be linked with recommended actions that support decision making.

ML supports efficient time management

When machine learning is applied to a field service management solution, the system can learn from previous service orders: It analyzes these orders as well as the actual time required to perform a task. Therefore, ML can help to precisely predict how long similar or the same orders will take in the future. That way, manufacturers can coordinate their employees' schedules and routes better.

ML accelerates order processing

Intelligent content or speech recognition technology used in customer service help to understand the customer’s intention, e.g. in a chat or while talking to the automatic announcement of a hotline number. When those systems are trained to recognize the intentions of a specific customer and to learn from the manual assignment of contacts to individual employees with specific skills, they begin to understand the correspondence and what action is triggered by what content.

At a certain point in time, the machine will take over the assignment and will also learn from the employees’ feedback if an assignment was good or forwarded to another employee with better qualifications. This process is saved as a learning result in the system. Over time, the assignment will become more accurate and customer requests can be solved faster which leads to an increase in business productivity while also enhancing customer experience.

Manuel Grenacher is General Manager, Service Cloud at SAP.