Why machine learning is the future of service
Although machine learning has been on the technology agenda for the past twenty years, it has only recently been possible for its potential benefits in terms of field service management to be better understood.
An integral part of artificial intelligence (AI), machine learning (ML) uses AI to analyze a company’s performance data and then makes decisions that can make it more efficient. Interest in AI has grown in recent years with technology leaders such as Elon Musk and Mark Zuckerberg, using machine learning to enhance existing technology.
The enthusiasm for machine learning (ML) in service organizations has increased profusely, although its powerful benefits are still not fully understood by the masses. Companies around the world begin to see ML as a “forecasting service” whereby all kinds of macro and micro environmental data – such as weather patterns and skills of a particular technician, for example – can be seamlessly connected and analyzed to provide accurate forecasts based on historical data.
And while all of this can be done without any effort of interpretation by professionals working in service organizations, the helpful insight offered can create a significant competitive advantage.
Machine learning : Is man-made rendering redundant?
On the contrary. In fact, combining machine learning predictions with operational research conducted by business leaders provides a deeper and more valuable level of business intelligence, enabling more strategic decision-making, and improving productivity and performance. So how exactly can machine learning leverage new opportunities for service organizations in the field? When it comes to delivering business value through machine learning (ML) , the key opportunities revolve around better planning and more accurate scheduling.
1. Traffic pattern predictions
The most innovative service organizations are already introducing the ability to route their technicians to predictive traffic patterns. Based on historical data, such as holiday traffic patterns, you can direct professionals to specific jobs when traffic is less congested in those locations. This already saves a great deal of time and expense, not to mention improvements in the customer experience by reducing delays in the technician’s arrival times and the need for long waiting windows. In addition, predictions derived from machine learning can also provide companies that provide home repair and installation services with more accurate indications around the estimated work duration, allowing scheduling and productivity to be optimized.
2. Weather forecast
Many meteorological institutes have already developed weather forecasting models that allow predicting weather patterns based on historical information and other seasonal factors. Likewise, field service companies are beginning to mirror these climate forecasting models by adding machine learning (ML) capabilities to their management systems. This streamlines the process of assessing and identifying when certain jobs – often those that need to be performed outdoors or at altitude – should be postponed because of the expectation of bad weather, health and safety concerns, as well as time considerations x cost.
3. Prevention of non-attendance of clients
One of the biggest financial losses for the companies that operate in the field is the non-attendance of clients, that is, the technician travels to a client’s house at the scheduled time only to find that there is no one on the property to serve him. In this scenario, machine learning can help predict whether or not the customer will be at home based on data from his or her background, the location of his home, and a host of other factors related to the weather and his work situation. This type of information eliminates time wasters of technicians and increases competitive advantage.
4. Sending the right person for the right job
Machine learning can also expedite service offerings by assigning certain professionals to specific jobs. For example, if a technician often installs smart meters in homes, you are already familiar with this type of work and will inevitably complete installations faster. Therefore, machine learning (ML) software can reallocate this professional to future intelligent metering facilities to speed up work processes. Rationalizing business decisions through machine learning can ensure that employees act on jobs they excel at, increasing customer satisfaction.
5. Predictive maintenance
By leveraging data generated by the Internet of Things (IoT), machine learning can anticipate when repairs will be needed and proactively program the service without requiring human intervention. Consequently, machine learning can monitor equipment status and predict problems, allowing technicians to service the equipment even before the problem is encountered. By opting for preventive service over reactive, companies can avoid costly failures and stop spontaneous outages that irritate customers and require engineers’ time.
How the machine is driving the experiences of customers and employees ?
From the point of view of the consumer in need of repair, the benefits of machine learning can include a considerable increase in “repairs on the first visit,” ensuring that certain parts and technicians are dispatched the first time. This improves overall customer satisfaction and experience levels – something that is becoming increasingly critical in an environment where customers demand service levels similar to apps like Uber, for example, and have more options and influence than ever. Similarly, for professionals working in field service companies, machine learning can also improve the overall employee experience and support staff retention levels.
What is the next step ?
Of course machine learning remains a new concept for many and the questions remain around the best way to apply it in a field service context. There is still work to be done to incorporate machine learning (ML) into existing workflow systems so that future predictions can be more easily integrated, understood, and applied. Companies that master this process before the masses, however, can certainly improve their compliance with the Service Level Agreement (SLA) and get better business rewards.
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