Busier roads, increasing competition, higher customer expectations, and rising costs are all converging in a big bang moment that’s 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” service calls, where technicians cannot validate the reported problem. These calls lead to the most significant and costly action—an unneeded truck roll—where businesses dispatch technicians and equipment unnecessarily.

Why No Fault Found Service Calls Are Bad for Business

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 service call, he is not available for actual revenue-generating calls. It also affects customer experience and could be the difference between retaining or losing a customer. Even service organizations that track asset movement in near real-time often need help pinpointing the causes of equipment failures and making sense of the best next steps.

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?

Move Away from Guess Work

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 centers, which means organizations 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 guesswork, trying to understand which customers need which parts in advance and then equip their respective technicians accordingly. To work out how to maximize engineer journeys and repair jobs while keeping customers happy means, in short, that no one can rely on human intuition any longer.

Interestingly, the recent State of Field Services: 2019 report from TSIA notes that assisted proactive support technologies can help service organizations reduce truck rolls by as much as 71%. 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.

Adopt AI-Driven Solutions That Provide a Holistic View

To avoid no fault found service calls and 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 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, decide if a truck roll is required, and have greater confidence in selecting the right technician 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, optimize 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 organizing the present—without a clairvoyant insight.

AI in Practice: How 3D Systems Uses Remote Triage

3D Systems, a leading additive manufacturing solutions company, has been delivering cutting-edge 3D printers, print materials, on-demand manufacturing services, and manufacturing software for over 30 years. As customers’ demands for consistent uptime continued to increase, 3D Systems knew they had to dramatically change their services organization to keep pace with customer need.

When looking for a way to prove better service, 3D Systems turned to ServiceMax and Aquant’s Remote Triage solution. Using Aquant’s service intelligence platform, Remote Triage mines and analyzes companies’ data to learn their service language and build an AI-driven decision framework around it that service technicians can rely on with confidence.

With Remote Triage, 3D Systems is able to

  • Equip even newbie service agents in the field with 20 years’ worth of knowledge in just a few seconds, allowing them to pinpoint and resolve issues quickly
  • Proactively predict service needs so that issues can be mitigated before the customer becomes aware of them
  • Find the most cost-effective solution for each failure incident, which often allows technicians to resolve the issue remotely, and if not, they have an accurate description of the problem and the parts they’ll need before arriving on site

“With artificial intelligence-driven insights, resolving customer challenges has become more efficient and accurate, ultimately driving improved service profitability,” says Mark Hessinger.

Read the Full Case Study here.

ABOUT Joe Kenny

Joe KennyJoe Kenny is the VP of global customer transformation & customer success at ServiceMax. His career spans over 30 years of leadership positions in Operations, Sales, Product Development, Product Marketing, and Field Service. Beginning his field service experience with the U.S. Naval Security Group Command (NSGC) as a mainframe computer technician, Joe subsequently lived and worked in Asia, the U.S., and Europe. Joe has focused on customer relationship management, using clearly defined and mutually agreed to measurements of success, and driving to continually exceed customer expectations, allowing for exponential business growth and client retention.

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