One of the most important functions in product life cycle management is to provide high-quality after-sales service to customers. This service most commonly comes in one of three forms:

  • The customer attempts to troubleshoot the problem with manuals or YouTube videos.
  • The customer calls a customer service help line in the hopes of troubleshooting and fixing the problem over the phone.
  • A technician is dispatched to the customer’s location.

In the first case, the customer must convert text, graphics or video into behaviors. The customer must constantly switch attention back and forth from the manual or video to the product while storing information in his or her working memory. This process is challenging and places a heavy load on the cognitive and representational systems in the brain. From a cognitive perspective, the working memory, executive attention and attention-switching loads are high. From a representational perspective, the customer must convert abstract information into an accurate mental representation. If the customer is lucky, he or she can fix the problem.

In the second case, customers often struggle to describe the problem that they are experiencing without knowing the appropriate terminology to communicate effectively. Customer service representatives struggle to translate the customer’s description into a meaningful dialogue to guide troubleshooting. In this scenario, both the customer and the customer service representative feel like they are speaking different languages. This problem, again, is with cognitive and representational processing in the brain. Translating the problems and solutions into words places a heavy load on working memory, attention and mental representation processes. Both people wish that the customer service representative could “see” the problem and “experience” the problem in real time.

In the last case, the technician comes to the customer’s location and troubleshoots the problem. Unfortunately, not all technicians are experts. They differ in their experience and skill level, and a novice technician may need to revisit the manual, call an expert for advice or even ask a more experienced technician to visit the site. This process costs the vendor time and money, and every second that the product is “down” represents an inconvenience, decreased satisfaction in the product and a potential loss in revenue.

These three scenarios are all lose-lose propositions. Imagine how companies could apply augmented reality (AR) to each scenario to increase efficiency, reduce downtime, save money and increase satisfaction.

Instead of using manuals and videos to troubleshoot a problem, suppose the customer could focus his or her phone or tablet camera on the device and receive step-by-step instructions on how to troubleshoot the problem. Built-in algorithms or artificial intelligence could instruct the customer on what to do and supplement instructions with arrows, highlights and other visual aids. In this case, the technology is guiding the customer on how to fix the problem with information that is overlaid onto his or her field of view. By providing step-by-step instructions and overlaid information, AR significantly reduces the cognitive load, because it removes the demands on working memory and attention-switching. It also reduces the representational load, because there is no need to translate abstract information into a mental representation. Rather, the visual representation is provided.

Next, consider the situation in which a customer calls a customer service representative to help troubleshoot the problem. Instead of trying to find the right words to describe the problem and trying to translate the customer service representative’s instructions into actions, suppose the customer focuses the phone or tablet camera on the device, which provides a first-person view for the customer service representative. The representative can then use writing, drawing and visualization tools that appear on the customer’s device to guide the customer through a set of steps for troubleshooting. This approach almost completely removes the need for verbal communication, thus reducing the cognitive load. It also reduces the representational load, because there is no need to translate abstract information into a mental representation.

Finally, consider the situation in which a novice field technician runs into trouble and needs guidance from an expert. In much the same way that the customer could use a phone or tablet to provide a first-person view for a customer service representation, the novice field technician can use his or hers to provide a first-person view for a more experience field technician.

In all of these cases, the likelihood of fixing the problem quickly and accurately is increased. AR reduces downtime and costs and improves customer satisfaction.

Although AR has great potential in field service, the tool in and of itself will not meet this challenge. AR provides experiences for human users, but it is critical to optimize the content and temporal aspects of the presentation for human consumption. Augmented information can reduce the cognitive load but can also overload the user. Optimization follows from good experimental testing and modification. Fortunately, the rich and broad set of real-time data that users can extract from AR technologies makes it a realistic prospect.