KPI#3: MEAN TIME BETWEEN FAILURES

 

What is it?

Mean time between failures (MTBF) measures the average time between customer issues. The goal for service organizations is to maximize this metric because a high time between failures represents high service quality and maximum uptime.

How it measures workforce performance

MTBF is less about resolving the single issue that prompted a failure, and more about a service professional’s ability to holistically maintain the health of the machine. This is a skill that is typically difficult to measure. Seasoned service heroes know how to take advantage of time in front of an asset to ensure that everything is working properly. They’ll work to proactively maintain or replace parts before a failure occurs, and understand how to keep failures from happening in the future.

How it measures customer experience

The less you notice this measurement creeping up, the better. Tracking MTBF is a great way to measure customer uptime and service reliability — ultimately resulting in happier customers. While service is about providing a great experience in each customer interaction, a better outcome is preventing the failing in the first place.

Challenges to measuring mean-between-failures

• Measuring mean time between failures requires long intervals of data because it’s a lagging indicator: MTBF is a big picture measurement. It requires a sustained ability to measure the time difference between failures. Quite often, organizations may only look at a quarter’s worth of data, and that’s not enough time to accurately measure MTBF because a good indicator of health means that failure may only happen every few quarters. 
• Attribution of mean time between failure to service professionals is up for debate: What is the definition of failure? Is it when different, unrelated issues arise in the same asset? Or can it also be a problem that (unknowingly) is not fixed correctly the first time, and subsequent service calls were needed to address the actual problem? Because multiple service technicians are usually contributing to service across one asset or customer, it’s difficult to attribute a long or short MTBF to a single individual.

How to measure mean-time-between-failure:

It’s straightforward with the exception of these scenarios. When dealing with long MTBF intervals (6 months or longer) or an extremely short average asset lifecycle, using the standard approach to MTBF might not be suitable. Instead, using MTBE (mean time between events) might be a better measurement. When using MTBE as a metric it’s less about whether a particular interaction with a customer constitutes a failure and more about measuring how many times you had to interact with that customer. Fewer interactions signal fewer service issues.

 

---

 

KPI#4: SERVICE COST PER WORK ORDER

 

What is it?

Service cost per work order (or per case) is an effective measurement of the total cost of each work order or case created. It’s also known as the average cost per truck roll.

How it measures workforce performance

The cost per work order provides a measurement of the duration and/or parts usage a technician uses, on average, to resolve an issue. Higher performing individuals innately know how to solve problems without shotgunning expensive parts.

How it Impacts Customer Experience

While customers aren’t concerned with how much it costs to resolve each problem, the bigger quality issues remain true here. A lower cost per work order generally highlights a higher quality of service -- and that’s what customers notice.

Challenges of measuring mean time to resolution

• Service cost per work order might be a misleading indicator of performance: Context is key, especially when a few experts are regularly assigned complex jobs. These are cases when top employees may have a higher average service cost per work order. If yours is an organization that saves the most difficult cases for workforce heroes, know that the more complex the cases, (typically) the more expensive the service costs. ○
• Service costs can have an inverse relationship to customer satisfaction: You likely get requests from service teams for additional help because more resources (and faster response times from those with lighter workloads) typically result in better service performance. So don’t look at this metric without regard to the customer experience component. ○
• Service cost per work order doesn’t differentiate poor experiences and repeat visits: After scouring data from hundreds of organizations, it’s clear that one of the most difficult challenges in measuring service costs hinges on the ability to identify when consecutive work orders are related to the same issue. Organizations that carefully track when cases that were previously closed, were closed in error -- because the root issue wasn’t resolved properly the first time -- are rare.

How to measure mean time to resolution:

One of the best ways to track cost per work order is to use the metric to measure service cost per success. By flipping to the positive outcome, your organization will have a better understanding of who within your workforce are the heroes of your organization (those who resolve issues with minimal parts and visits). The difficulty with measuring each success is understanding whether a case is isolated or related to another case. Depending on how you capture data, you can use different methods to measure successful events, including the method describing how to measure repeat visits previously.

---

 

KPI#5: NET PROMOTER SCORE

 

What is it?

Net promoter score (NPS) is one of the most widely used methods to identify customer satisfaction and loyalty. Simply put, it measures the number of promoters and detractors for the service you provide

How it measures workforce performance

There is no other metric that reinforces the statement “customer is king” more than NPS. Happy customers overwhelmingly signal strong workforce performance. Typically, NPS measures a qualitative experience (technical workforce skills), such as how quickly a problem is resolved, but sometimes, NPS reflects a customer’s opinion about how they feel they’ve been treated (soft workforce skills).

How it measures Customer Experience

This is one of the most straightforward measurements possible to gauge customer satisfaction.

Challenges of measuring mean time to resolution

• Net promoter score alone is not a true measure of workforce performance: While customer satisfaction is a critical component in delivering great customer service, NPS alone is an incomplete measure of the performance of your service workforce. In service organizations where NPS is the measurement used, service costs skyrocket. Customer service professionals provide endless perks and free services in order to create customer loyalty. Unless this is part of your organization’s objectives, be careful when using NPS alone. 
• Net promoter score is difficult to attribute to any one service factor: While NPS is a comprehensive measure of a customer’s experience, it’s difficult to use it to clearly identify any one specific factor or event that impacts score.

 

---

In the final feature in this series we will look at the Aquant Workforce Performance Index and how field service organisations like yours are leveraging this to improve their customer satisfaction ratings. Look out for this article in a weeks time!

Alternatively, subscribers to www.fieldservicenews.com can access the white paper on the button above and the rest of our premium content library! Join 30K of your field service management peers and subscribe @   www.fieldservicenews.com/subscribe

---

Further Reading:

• Read more about Leadership and Strategy @ www.fieldservicenews.com/blog/tag/leadership-and-strategy
• Read more about Managing the Mobile Workforce @ www.fieldservicenews.com/blog/tag/managing-the-mobile-workforce
• Read more news and articles featuring Aquant @ www.fieldservicenews.com/hs-search-results?term=aquant
• Connect with Edwin Pahk on LinkedIN @ https://www.linkedin.com/in/edwin-pahk-8a066515/
• Find out more about the solutions Aquant offer to help field service companies @ www.aquant.io/
• Follow Aquant on Twitter @ twitter.com/Aquant_io

 

KPI#1 FIRST TIME FIX RATE

 

What is it?

First time fix rate (FTF) is one of the most popular metrics for workforce measurement. It’s simply how often someone is able to fix the issue in question on the first try. This is typically referenced in both a contact center and field service scenario.

How it measures workforce performance

Generally, it’s assumed that the higher the first time fix rate, the more competent or skilled the technician is.

How it impacts customer experience

FTF makes a big impact on great customer experience. Customers want resolution quickly, and they don’t want to have to place a service ticket two days later because the issue was never properly resolved. Solving issues correctly the first time boosts a brand’s value and acts as a competitive differentiator, making it just as important as the quality of the initial product or service.

Challenges to measuring first time fix rates

• Service costs can increase: Service organizations often throw a lot of money at the problem to increase first time fix rates. The problem? First time fix rates alone do not represent the skill level of your workers. For example, a technician tends to swap costly parts for a new one every call instead of digging deeper into how to fix the root issue. Stats will show a high FTF, but doesn’t take into account that a cheaper option could likely have fixed the problem
• First time fix doesn’t reflect technician skill: Not all issues resolved on the second attempt reflect technician error. Often, failure to achieve FTF happens in tandem with an accurate diagnosis. If the dispatcher didn’t provide insight into the situation, a tech may arrive onsite, quickly diagnose, but need to come back later that day or days later with the right part. Understanding the context of what the metric represents is just as important as measuring first time fix rates.
• Properly defining and tracking first time fix is a challenge: This is especially problematic depending on how an organization tracks the KPI. If each new ticket is measured in a vacuum, a first time fix rate may be high. But what if tech thinks they fix the issue on the first visit, only to be called back a week later because of a different problem with the same machine? If the tech makes another quick fix, you record that as another FTF. Go team!
  But wait a minute. What if a third ticket is issued a week later and a different tech arrives on-site to realize the first tech was simply putting a band-aid on a more complex root issue? A deeper analysis of these common miss measurements finds that service organizations really have more chronic repeat visit problem than they realize.

How to measure first time fix:

It’s not an exact science, but a much more accurate approach is to measure whether there was a visit for that same asset or issue that occurred within X number of days of the previous visit. While this isn’t a complete solution, it addresses the major fallacy of the first time fix rate.

---

 

KPI#2 MEAN TIME TO RESOLUTION:

 

What is it?

Mean time to resolution (MTTR) refers to the time it takes to resolve a customer issue. This is typically defined as the time between the case creation date and its 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 costs. Organizations with high MTTR often have techs who find themselves in escalation black holes.

How it measures workforce performance

MTTR has an inverse relationship to first time fix rate. As your FTF rate goes up, MTTR should go down. How so? That’s usually related to service heroes resolving issues (really resolving the root cause) on the first visit, with the right parts and tools to make quick work of the problem.

How it Impacts Customer Experience

Consumers and B2B clients want immediate service. Amazon Prime, overnight shipping, Netflix, and more all represent the demand for immediacy. MTTR is a critical part of that customer service experience, and if your customers don’t receive the attention and quick resolution they want, they’ll seek out a competitor.

Challenges of measuring mean time to resolution

• Poor data collection or lack of uniform data: This is the biggest issue related to measuring MTTR. While case creation/creation date is a fairly consistent data point across organizations, case resolution time or date is much less reliable due to poor data collection. The biggest issue here is a lack of compliance among users -- technicians often forget to close out cases until days after the problem is resolved.
• Dependence on first time fix measurement: MTTR is highly dependent on how FTF is measured. Failed visits have a profound impact on MTTR, and the stats are grim. Issues not resolved the first time require, on average, another 14 days to resolution. The reason is often attributed to the need to order additional parts, scheduling issues with customers. MTTR suffers from the same challenge as FTF if the root causes of failure aren’t addressed.
• Mean time to resolution is a measure of process and people: It’s tough to separate the two. MTTR can be used to measure workforce productivity, but it’s also a measure of capacity and process. Sometimes when MTTR slumps, that’s the result of lack of enough headcount versus workforce performance.

 

How to measure mean time to resolution:

There are several approaches to mitigate some of the challenges faced when measuring MTTR.

• Measure the difference in mean time to resolution rather than overall rate. Instead of looking at MTTR as a single unit, focus on the aspects of MTTR that reflect workforce performance. For example:
  • This can be identified in the difference in MTTR between individuals when a failed visit occurs. Service professionals who often require multiple customer visits will generally have greater MTTR rates versus your best experts who seldom make repeat visits.
• Use only open dates to measure mean time to resolution. Given the lack of quality in measuring accurate resolution dates, using open dates and visit dates to define the MTTR threshold is a way to identify MTTR. This eliminates the inconsistency in resolution or close dates, and will only work on customer issues with failed visits.

---

In the next feature in this series we will look at three more crucial KPIs, Mean Time between Failure, Cost Per Service Per Technician and Net Promoter scores. Alternatively, subscribed now for access to the white paper above and the rest of our premium content library @  www.fieldservicenews.com/subscribe

---

Further Reading:

• Read more about Leadership and Strategy @ www.fieldservicenews.com/blog/tag/leadership-and-strategy
• Read more about Managing the Mobile Workforce @ www.fieldservicenews.com/blog/tag/managing-the-mobile-workforce
• Read more news and articles featuring Aquant @ www.fieldservicenews.com/hs-search-results?term=aquant
• Connect with Edwin Pahk on LinkedIN @ https://www.linkedin.com/in/edwin-pahk-8a066515/
• Find out more about the solutions Aquant offer to help field service companies @ www.aquant.io/
• Follow Aquant on Twitter @ twitter.com/Aquant_io

The service landscape is facing a dramatic transformation that ranges from the need to skill up a new workforce, to the move away from a reactive break-fix work towards a predictive service model. This, coupled with the desire to limit expensive on-site visits and customer demands for enhanced SLAs, means every service moment matters.

To make this transformation a reality requires a workforce of high performers, but there are plenty of hurdles on the path to achieving this goal.

Assembling and nurturing a powerhouse service team is challenging

• Baby Boomers are in the midst of a powerful retirement wave
• There is a sizable skills gap between new recruits and those who are retiring
• High paying service jobs became less desirable over the last decade as enrolment in vocational schools declined in favor of university programs
• Millennials and Gen Z seek to work, collaborate, and develop professional skills using speedy tech tools versus long-term learning plans

And the service landscape looks different, too

• Machines are more complex and require a workforce with advanced technical skills
• Consumer demand for standardized service costs are driving more predictive service offerings

• A move towards remote diagnostics and self-service triage offerings empower the customer to handle simple issues that don’t require a technician to be dispatched

• Changing economic factors are driving renewed pressure to stabilize or cut service costs

When we peel away the layers, all of this reinforces that a high-performing workforce is a key competitive differentiator. But how do service leaders move forward in this new service

landscape? Tools that map out your workforce, providing a snapshot of your experts versus challengers, and provide guidance on how to upskill the whole team is the first step.

 

Why Measure Individual Workforce Performance?

Service companies need to zoom in and out when it comes to KPIs Organizations have dashboards and charts to measure the service KPIs of the entire team, but few are zooming in to look at individual performance. Big picture knowledge is essential, but without visibility into great performers (service heroes) versus those who would benefit from training or upskilling (challengers) it’s hard to create service plans that address the root cause of workforce issues.

You likely know the shortlist of service heroes.

They are saved to your favorite contacts. And you may have a training plan for a few of the team members you know are struggling. What about the other 98% of the team? How are they really performing, and what do they need to do to level up and develop the skills and confidence of your superstars?

There are some obvious signs that members of your service workforce could use additional training. For example, those who regularly require repeat visits to remedy less complex issues is an obvious sign. What about more subtle indicators?

Some on the team may appear to check all the right boxes but may be struggling in other ways, such as racking up high parts costs by swapping out parts until the job is fixed.

This is where workforce measurement helps make sense of employee data by analyzing KPIs in a way that really matters. It can spot inconsistencies in service quality among the team and help you pinpoint who needs training and who is in the best position to provide mentoring.

But, there’s a caveat! Before you can get here, you need to measure the right things the right way.

 

Do the KPIs You Measure Provide the Right Insights?

We see you shaking your head. Service organizations already spend quite a bit of time and effort tracking workforce KPIs, and most service leaders keep a constant eye on those numbers.

But right now those KPIs tend to be narrowly focused on single measurements such as productivity or first time fix, and they don’t provide a holistic snapshot.

Without zooming out for a 360-degree view, measuring in isolation often leads to systemic and costly service issues. Instead of focusing solely on “what” to measure, it’s more important to ask “how” you are measuring. Do you have access  to the right mix of information to measure what really matters? That should include understanding metrics at a macro level across the organization and on an individual level. Take a deep dive into your data.

Does one KPI improve, only to have another fall below target? Not analyzing the right data, or not doing so in the right way, can be just as harmful as not measuring at all.

In the next feature in this series, we will look at the five key KPIs that, when understood in relation to each other, can provide an accurate snapshot of what’s really going on with your service teams.

 

---

Further Reading:

• Read more about Leadership and Strategy @ www.fieldservicenews.com/blog/tag/leadership-and-strategy
• Read more about Managing the Mobile Workforce @ www.fieldservicenews.com/blog/tag/managing-the-mobile-workforce
• Read more news and articles featuring Aquant @ www.fieldservicenews.com/hs-search-results?term=aquant
• Connect with Edwin Pahk on LinkedIN @ https://www.linkedin.com/in/edwin-pahk-8a066515/
• Find out more about the solutions Aquant offer to help field service companies @ www.aquant.io/
• Follow Aquant on Twitter @ twitter.com/Aquant_io