How AI is triaging service calls

Joe Kenny, vice president global customer success for field service management company, ServiceMax, explains why artificial intelligence is better at service calls than humans are.

Busier roads, increasing competition, higher customer expectations, and rising costs are today all converging in a big bang moment demanding change. For service teams, this moment will either destroy a business or force change, where leaner, more efficient models to help customers thrive in an increasingly digital world will evolve. Adapt or die, as they say, but this is easier said than done. To accomplish this, businesses will have to confront ‘no fault found’ where technicians cannot validate the reported problem, which leads to the most significant and costly action – an un-needed ‘truck roll’, where businesses dispatch technicians and equipment unnecessarily – a wasted trip for the ‘white van man’.

Understanding the impact truck rolling can have on costs is important. Every time a service technician is needlessly dispatched to a work site or fails to complete a job during the first visit due to an unexpected problem, it cuts into a company’s bottom line. While that technician is en route to the no fault found call, he is not available for actual revenue generating calls.  It also effects customer experience and could be the difference between retaining or losing a customer. Even those service organisations that track asset movement in near real time often need help pinpointing the causes of equipment failures and make sense of the best next steps.

The problem is that for service teams to really identify efficiencies (and inefficiencies), they practically need to be telepathic with their current systems.  At the time of writing this, there aren’t may clairvoyants kicking around dispatch centres, which means organisations need to be able to proactively manage customers, with limited or zero visibility, making it practically impossible. For many service businesses today, they are still relying on calculated guess work, trying to understand which customers need which parts in advance and then equip their respective technicians accordingly. To work out how to maximise engineer journeys and repair jobs while keeping customers happy means, in short, that no one can rely on human intuition any longer.

Truck rolls can cost anything from $150 to $1,500 per visit for a typical service business. As visits can get into the hundreds of thousands a year, it’s easy to see how easily and quickly costs can escalate. So how do businesses manage this better? And how can they reduce the burden of unnecessary truck rolls and start delivering accurate intelligence that can drive technician dispatch more efficiently and effectively?

Interestingly, the recent Technology Services Industry Association’s The State of Field Services: 2019 report notes that assisted proactive support technologies can help service organisations reduce truck rolls by as much as 71 per cent. That’s massive. The issue most businesses have though, is that intelligence on customers and customer equipment tends to be siloed. Data from a disparate range of devices, people and places can contain the right knowledge but is often left isolated or takes too long to decipher.

To really triage customers efficiently, businesses have to start looking towards AI-driven solutions that can pull together the necessary data to help formulate more efficient plans. AI can help businesses harness data from all sources to drive business-critical KPIs and predict customer problems before they happen.

The aim of course is to use the technology to automatically validate failures quickly and offer actionable solutions, instantly. For planners and dispatchers, this means problems can be triaged with greater accuracy. They can recommend a remote solution or whether a truck roll is required and have greater confidence in selecting the right technician skills for the job and dispatching them with the right parts and tools.

With this level of automated business insights, service teams can evaluate customer metrics like ticket volume and risk of churn, optimise field routing, automate compliance reporting, and track and assess individual and team KPIs. Ultimately, this kind of AI-driven approach can greatly reduce the truck roll problem. It helps businesses gain greater control over service team overheads by not just predicting the future, but by organising the present – without a clairvoyant in sight.

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