Best in class organizations that have adopted analytics have seen their service profits increase by 18%, their customer retention rates have soared 42% and the performances of their service level agreements have shot up 44%.
That’s according to a recent study conducted by Aberdeen Group on the use of analytics in field service organizations.
Clearly, there are multiple ways in which predictive analytics can be used by and for field service organizations. One of the most obvious applications is to examine how field service processes and techniques can be further improved to drive operational effectiveness.
For instance, analytics can be used by field service supervisors for route optimization to reduce travel time, fuel consumption, and vehicle maintenance across field service fleets.
Information that’s gleaned from location technologies, GPS systems, and 3D mapping has generated double-digit gains among various types of field service companies. In fact, the use of GPS technology and data has produced 11% savings in labor costs, a 13% reduction in fuel consumption, and 13% savings in vehicle maintenance and repair, according to a study by NDP Consulting.
Field service managers can also use analytics to manage their workforces more effectively and to schedule technicians more efficiently based on worker availability, skill sets required for upcoming or anticipated service calls, etc.
The top 20% of field service companies experienced much higher workforce utilization rates (78% for best-in-class versus 64% for all others), while 75% of top-performing companies met their 2012 customer satisfaction goals compared to just 56% for all other organizations, notes a separate study by Aberdeen Group.
Another way that predictive analytics can be used by field service organizations is to better prepare for the equipment and parts that technicians will need for each of their scheduled service calls.
Although there’s always a certain level of uncertainty about the problem a field service technician will encounter at a customer location as well as the parts and tools he’ll need to carry out the repairs properly, analytics can help guide dispatchers in this regard.
For instance, a dispatcher for a regional plumbing and heating company who handles a call from a customer having a particular problem with the flow of water in her home, can deduce that the source of the problem is likely related to a pump that’s being used.
The dispatcher can then ensure that the technician who is assigned the call has the right replacement pump and other associated parts on board his truck before arriving at the customer’s house.
Doing so can help improve customer satisfaction and customer retention by elevating the percentage of customer issues that are resolved on a first-visit basis. It can also improve operational efficiency by dramatically reducing the amount of time technicians spend driving back to their warehouses or distribution centers to pick up the needed parts.