A customer company reports a malfunction on a machine from your company via the service app. At the same time, your service platform generates a service ticket that your remote service team receives in real time for processing. Together with the ticket, the colleagues also receive important detailed information such as the serial number and maintenance history of the affected machine. The experts exchange information via the platform and start searching for causes and solutions to the problem. There can hardly be a more direct way to rectify the malfunction.
But what if the service platform thinks for itself and makes suggestions for troubleshooting? The data-supported analysis method Predictive Analytics makes this possible.
Definition of predictive analytics
If the scenario described at the beginning of this article applies to your company, then you have already set up your customer service efficiently. Service ticket, machine data and the communication of the service team are brought together on one platform. Thus, after the ticket is closed, this information is available in digital form in the system:
- Machine data
- Description of the disorder
- Strategies for problem solving
The predictive analytics method is based on precisely such data. The short definition: The method consists of applying statistical procedures and machine learning to historical data sets. On this basis, it is possible to look from the past into the future. To do this, the method recognises patterns, which it uses to make predictions for the future. In this way, the system analyses successfully processed incidents and derives suggestions for new cases.
Application areas of predictive analytics in service
Besides customer service, there are many other areas of application for predictive analytics in industry. The examples illustrate how the method reduces costs and increases revenue: Banks match information from a loan application with their database and decide whether to grant a loan based on the forecast of the probability of default. Marketing departments forecast which customers are most likely to respond to a campaign with a purchase. They target precisely these customers, thereby increasing the ROI of the campaign.
Of course, manufacturing companies can also benefit from applications in marketing. But in customer service, predictive analytics offers particularly great potential for optimising business. They lie above all in these two application possibilities.
- The service platform can analyse tickets that have already been solved to suggest the most efficient problem-solving strategy for a new service case. Your team of service experts does not have to start from scratch. Instead, they receive data-based solution suggestions from a chatbot in the service platform along with the ticket, for example. Like a knowledge database, this improves the problem-solving rate.
- If the machines at the customer's site permanently transmit operating data to your service platform, machine malfunctions can also be predicted. Then you can avoid an unplanned shutdown through preventive maintenance measures.
Thanks to predictive analytics, machine manufacturers can solve service requests faster and thus increase the satisfaction of industrial customers. They can also make new data-driven service offers, such as plant improvements or intelligent maintenance strategies.
Act now and create the data basis for Smart Service
Experts agree: Those who collect data and use it efficiently create opportunities through digital business models, especially in SMEs. But when it comes to implementation, what counts above all is good preparation. Decision-makers in companies should define the data requirements and suitable predictive analytics tools for the evaluation purpose in good time. An important step into the data-driven future: Bring together all data from customer service on the central platform of MEXS. The Smart Service Solution already has a self-learning function that speeds up the resolution of repeatedly occurring faults.