Go ahead and call me cynical, but when it comes to service success, I’ve only ever cared about the numbers. Organizations can trot out whatever new, flashy technology they want, but if you can’t point to some sort of tangible improvement derived from its adoption (Preferably one that can be directly tied to money saved or money earned), I really can’t be bothered to care.

Service organizations are no stranger to performance metrics, obviously. In a field where the product is your service, it’s impossible to measure success without them. Business drivers like revenue earned, customer satisfaction, and jobs completed have always been the measure of service success, but organizations that have moved towards more digital processes now have significantly more visibility into their organization’s operations, and with that comes the ability to both measure new success metrics, and make significant improvements to your own performance. Are you measuring the right metrics? How does your performance stack up? Let’s take a look at some of the things that service organizations are measuring today, how they measure it, and what their performance looks like.

First-time Fix Rate

We will define First-time fix rate as the total number of service tickets divided by the number of service tickets resolved upon first visit.

First-visit resolution has become the standard-bearer of metrics for field service organizations in recent years, and for good reason. It simultaneously gives a picture of operational efficiency in terms of knowledge management, inventory management, and understanding of the serviceable asset while indirectly measuring customer satisfaction at the same time. Unsurprisingly, then, this metric is measured by two thirds of service organizations, according to research from Service Strategies. My own research indicates that among all firms, the average first-time fix performance is around 60%.

There are obviously some caveats to these broad averages. Different industries servicing assets of varying complexity levels will have dramatically different performance. When asked what the most likely reason for a second visit is, for instance, my research has shown that the leading reason was the need to use the first visit for diagnostics. While a diagnostic visit may be the standard today, connected devices are beginning to eliminate that need altogether. In fact, if you benchmark organizations with IoT-enabled serviceable assets versus those without, the fist-time fix rates improve dramatically.

Mean time to repair

We can define this as the total time from ticket to invoice, though some firms look at this exclusively as time on the job site. I’d argue that time from ticket to invoice is more valuable for a variety of reasons that help measure scheduling and routing efficiency as well as workforce availability and fleet utilization.

Compared to first-time fix rate, this metric is studied by far fewer organizations. According to Service Strategies, it’s only 36% of firms that measure this at all. I was admittedly perplexed by this because I feel like this metric could be the standard-bearer for field service excellence. When I have a service need, whether it be installation of new equipment, repair of existing equipment, or scheduled maintenance, to say nothing of things like pest control or roof repairs, I want that service completed as quickly as possible. This, of course, differs dramatically from industry to industry, so in my research, I prefer to look at this in terms of annual improvement, which sits at an average of 12%. As organizations improve, this number naturally plateaus, but improvement should consistently be the goal.

Remote resolution rate

We can define this as the total number of service tickets divided by the number of service tickets that did not require dispatch. Firms currently measuring this metric are saying that on average, 1/3 of service calls can be resolved remotely.

Benchmarking remote resolution requires a reasonable understanding of how service is executed at your organization, from the call center, through dispatch, to the repair. Can call center technicians walk end users through repairs on the phone? Do they? Do they have a plan for when to escalate to a service visit? Are there augmented reality-powered guides that can show customers how to swap out parts and recalibrate certain pieces themselves? IoT can help take this a step further. Can a call center representative provide a power cycle, firmware update, or system reset remotely? Can they use internal sensors to identify which part might have failed, and direct users how to repair? As you can see, there are a great deal of ways that organizations can begin to maximize and benchmark remote resolution, and it doesn’t ostensibly require any additional technology spend. It does, however, benefit from utilization of some new tech.

Asset uptime

This is defined as total asset operating schedule divided by actual asset utilization, or, conversely, total asset operating schedule hours divided by asset downtime hours minus 1. According to Service Strategies, only 40% of firms with applicable assets are measuring this today, so this is a great metric to use, where appropriate, to improve your serviceable asset performance. The benefits of doing so are obvious; You’re proving the efficacy of your serviceable asset, managing your technician workforce, and ensuring that the customer’s assets are working as-intended at all times. Among those firms measuring this, the average performance is 85%. Improve upon that, and you’re in the elite.

With any measure of performance, there are of course two avenues of improvement: Improving what you’re doing, and improving what you’re measuring. New technology spend, training opportunities, and efficiency gains will help you improve both.

Tom Paquin
Author

Contributor, Future of Field Service