What do your KPIs not tell you?

Aquant, has recently published the 2022 Service Intelligence Benchmark Report, now available at Field Service News, which offers an in-depth analysis of field service performance and customer satisfaction in a year of talent shortage, COVID service pivots, and shifting customer demands…

 

In the Service Leader’s Guide to Workforce KPIs, we examined why workforce measurement is more critical than ever, and we defined best practices for measurement criteria.

 

For the 2022 Service Intelligence Benchmark Report, we went a step further. 

 

We analyzed service data to understand service performance beyond KPI measurement. We found that knowing your KPIs—by number or average performance—is not the same as understanding what those numbers reveal about customer satisfaction, employee skill level, or overall service performance. Averages don’t disclose the specific details you need to make critical service decisions.

 

Service KPIs by the numbers

 

Here’s the breakdown of how service organizations measure up against other organizations and the industry as a whole.

 

FTFR – First Time Fix Rate
 

First Time Fix rate is one of the most popular metrics for workforce measurement. It indicates how often someone is able to fix an issue on the first try. In this report, we are measuring the FTF rate of field visits, in a 30-day window.

 

 

FTF rates have hovered around 75% for more than a decade—and remained stagnant for all but a handful of top-performing companies. Additionally, FTF rates should never be measured in isolation. On average, a failed first visit leads to 2.5 additional visits and 20 days for MTTR.

 

CPS – Cost Per Success

 

The total amount required to successfully close a service ticket is known as the Cost Per Success (CPS). This is a bit different from other similar-sounding KPIs, such as Cost Per Truck Roll, since total CPS may include multiple visits, multiple truck rolls, a variety of parts, and other labor costs.

 

 

Some organizations may measure Cost Per Work Order, but that metric leaves out cases that include always assigning experts to the most complex (and expensive) jobs. Additionally, it does not account for cases where multiple work orders are related to the same core issue.

 

MTTR – Mean Time to Resolution

 

The Mean Time to Resolution KPI measures the time it takes to resolve a customer issue.

 

Typically, it’s the time between the case creation date and the closure date.  Similar to the pain of staying on hold when trying to resolve a personal issue, minimizing MTTR is a key factor in increasing positive customer experiences and reducing service costs. In the last year, we’ve seen a growing divide in this metric.

 

 

MTBF – Mean Time Between Failures

 

Mean Time Between Failures quantifies the average time between customer issues. Service organizations try to maximize this metric because a higher rate represents excellent service quality and maximum uptime.

 

 

Individual pieces of equipment may have differing life cycles, but service leaders need to understand underlying patterns—in both their machines and workforces. When visiting a job site, experienced service heroes know how to use their time wisely and ensure that assets are working properly before leaving. This can significantly extend the time between failures.

 

MTBV – Mean Time Between Visits

 

Need to calculate both uptime and service performance? Look no further than the Mean Time Between Visits metric. This measures every visit you have for an asset or customer, instead of only tracking time between failures.
 
 
 
Experienced techs will skew towards longer time intervals between visits. That’s great news, thanks to low repeat visit rates, as well as their ability to utilize every visit to maximize uptime.
 
Watch out for the next and final feature next week in which we will analyze why KPIS limit business and how to look beyond them. Field Service News subscribers can access the full report now.
 

 

Further Reading:

 

Want to know more? This feature is an excerpt from a white paper published by Aquant. Please either log-in or subscribe for access. 

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