Date Published - Last Updated 7 Years, 213 Days, 6 Hours, 43 Minutes ago
Most IT professionals are familiar with the operational metrics of technical service and support: cost per ticket, first contact resolution rate, mean time to resolve—all are well understood and almost universally applied. Yet even support organizations that have mastered these metrics and achieved a degree operational success often struggle to gain visibility and credibility within their own enterprises. The all-too-common result is that support operates at a subsistence level, lacking the necessary resources to deliver effective levels of support.
The business world offers a potential solution to this dilemma. When a business is underperforming, investments dry up because there’s no reasonable expectation of earning a profit. By contrast, businesses that are profitable receive adequate funding because they’re able to attract investors seeking a positive return. So, what would happen if support organizations began operating more like businesses, and were able to attract funding and other resources based upon their profitability?
In this article, I outline an approach for managing technical service and support as a business. Additionally, I propose a handful of business metrics that can be used to quantify and communicate the profitability of technical service and support. Finally, I discuss the need to adopt a paradigm shift for any support organization that aspires to realize the benefits of operating as a true business.
Return on Investment
Return on investment (ROI) is one of the most common and important measures of financial performance in the business world. It’s the ultimate measure of success for any business. Most companies, business units, and departments track ROI on an ongoing basis, using this metric not only to make intelligent investment decisions but also to justify their very existence. Yet fewer than ten percent of all technical support organizations utilize this critical metric.
Support groups that understand and can quantify their ROI gain a number of important advantages; chief among them is the ability to obtain funding and other resources based upon the ROI of support. Unfortunately, ROI remains an abstraction to most organizations in the industry. So, how do we calculate ROI and quantify the value of technical service and support to the enterprise?
Technical service and support creates economic value in at least four ways:
- Reducing ticket volumes through technology and root cause analysis (RCA)
- Improving end-user productivity by reducing ticket resolution times
- Minimizing total cost of ownership (TCO) by maximizing level 1 resolution rates
- Mitigating the effects of downtime through planning, prevention, and aggressive remediation
Perhaps the best way to demonstrate how to calculate the ROI of support is with a case study. Consider a mid-size service desk at an insurance company that has an operating expense of $4.8M per year. The service desk supports 8,190 end users and handles 21,300 tickets per month. Through aggressive RCA, over the course of a year this service desk was able to reduce ticket volumes from 2.6 tickets per end user per month to just 2.2 tickets per end user per month. The table below summarizes the results of their RCA initiative.
At a savings of $94 per end user per year, the total savings attributable to RCA is estimated to be $813,100 ($94 annual savings per user × 8,650 end users supported).
Technology can also reduce incoming contacts, and hence the cost of support. Password management tools are a perfect example. In North America last year, password resets comprised nearly 25 percent of all contacts to the service desk. By adopting a password management tool, a typical service desk can eliminate 50 percent or more of the resets that would otherwise be completed by a live agent. This amounts to real savings for any support organization!
Now, let’s examine how technical service and support can make end users more productive. The majority of today’s workforce is knowledge workers, all of whom rely upon one or more computing devices to do their jobs. In fact, the average end user now has nearly three devices: laptop, desktops, tablets, smartphones, printers, servers, etc. When these devices break down or don’t function properly, employee productivity suffers. By preventing these incidents from occurring, and by quickly resolving issues when they do occur, a support organization can return productive hours to the workforce.
A study conducted by MetricNet and summarized in the figure below concluded that knowledge workers lose an average of thirty-three hours of productive time per year due to various IT outages, breakdowns, and hardware and software failures. For support groups performing in the top quartile of the industry, the lost productivity per worker is just seventeen hours per year, about half the industry average. By contrast, employees who receive support from bottom-quartile support groups lose an average of forty-seven productive hours per year.
The difference between the top- and bottom-quartile performers is a staggering thirty hours per employee per year! Put another way, support organizations in the top quartile are able to return nearly four extra days of productivity annually for every knowledge worker in the enterprise. When multiplied by thousands or even tens of thousands of employees in a company, the productivity gains and ROI delivered by a top performing support organization can be enormous!
Let’s apply these numbers to the insurance company in our example. We know from benchmarks that this company is a top-quartile performer in technical service and support. We also know from the study referenced above that the difference in lost productivity between a top-quartile and an average performer is about sixteen hours per end user per year (33 hours of lost productivity for an average company – 17 hours of lost productivity for a top-quartile performer). When multiplied by 8,650 end users, we can estimate a total labor savings of 138,400 hours per year (16 hours per year saved × 8,650 users). The average work year has about 1,700 productive hours in it, so this labor savings is the equivalent of 81 FTEs (138,400 hours per year saved ÷ 1,700 work hours per FTE per year). Finally, we know that the average cost per employee in the insurance company is $79,300, including salary and benefits. The economic value of being a top-quartile support group is therefore about $6.4M annually (81 FTEs × $79,300).
The third source of quantifiable value derives from reducing support costs by maximizing level 1 resolution rates, sometimes referred to as “shift left” (i.e., shifting a level 2 ticket to the left by resolving it at level 1 or 0 [self-help], and shifting a level 1 ticket to the left by resolving it at level 0). Recent benchmarks show that the average level 1 resolution rate for North American service desks is about 82 percent. What this means is that 18 percent of all tickets that could and should have been resolved at level 1 are being transferred or escalated to another source of support for resolution. These unnecessary escalations represent defects in the support process, and they result in increased costs that often go unnoticed because they’re rarely tracked. (Please note that level 1 resolution is not the same thing as first contact resolution. Level 1 resolution is the number of tickets resolved by the service desk divided by all tickets that can potentially be resolved by the service desk, regardless of whether the ticket is resolved on first contact or not.)
As shown in the figure below, the cost of resolution increases with each successive ticket transfer to a higher level of support. The insurance company in our case study had an impressive first level resolution (FLR) rate of 93 percent. That is 11 percentage points higher than the industry average, and it equates to resolving an additional 25,212 tickets per year at level 1 versus the industry average ([93% FLR – 82% FLR] × [19,100 tickets/month × 12 months]). If we now multiply this by the difference in cost between tickets resolved at level 1 ($22) versus tickets resolved at level 2 ($62), we can estimate a cost savings of $1,008,480 per year (25,212 tickets per year × $40 per ticket).
Once again, technology can play a crucial role in reducing an organization’s support costs. Knowledge-Centered Support and remote support/diagnostic tools are among the most common technologies used to improve first level resolution rates, and hence reduce the TCO of support.
The fourth source of economic value in technical support—the mitigation of unplanned downtime—is difficult if not impossible to quantify. Nevertheless, it’s important to acknowledge this source of value, and to actively engage in strategies that reduce unplanned downtime. These include, but are not limited to, disaster recovery drills, proactive/outbound user notifications by email, text, and social media for major downtime events, and recorded IVR messages that inform inbound callers that support is aware of and working to resolve any major issues.
To conclude our case study, let’s calculate the ROI delivered by the service desk in our example. We have the following estimated cost savings:
$0.8M saved through root cause analysis + $6.4M in returned productivity to end users + $1M saved by maximizing first level resolution = $8.2M saved annually
With an estimated cost savings of $8.2M, and an annual operating expense of $4.2M, this support organization is indeed profitable, and has an ROI of 195 percent ($8.2M returned ÷ $4.2M invested). Moreover, if it continues to operate at this level or higher, this impressive ROI will be realized in future years as well!
The Business of IT Service and Support
Our insurance case study introduced several business metrics that can be adopted by any technical support organization. The most important metric, of course, is ROI. The underlying metrics that make it possible to calculate the ROI of support include FLR rate, tickets eliminated through RCA, and productive hours returned to end users.
While most support organizations track FLR, very few track the other two metrics that factor into our ROI calculation: tickets eliminated through RCA and productive hours returned to end users. Some are suspicious of these metrics because they lack precision and often involve some degree of estimation. This should not, however, dissuade you from calculating the ROI of support, as the benefits of doing so far outweigh the costs.
Demonstrating a positive ROI, by itself, may not be enough to resolve the funding, credibility, and visibility issues that hobble many support organizations and prevent them from achieving their full potential. A fundamental paradigm shift is also needed: you must start thinking and acting like a business. This requires that you effectively communicate your ROI to key stakeholders, particularly to management. Moreover, your message must be bold enough to get noticed, and it must be persuasive enough to overcome any internal resistance to change. While this may take some out of their comfort zones, the alternative—a tactical, subsistence-level technical support organization—should be motivation enough to overcome any reluctance you may have about aggressively communicating your ROI.
Rational organizations fund businesses that are profitable. Now that you have a methodology for calculating the ROI of support, you should use this tool to elevate your support organization to a new level within the enterprise. Think of yourself as the owner of a profitable business that deserves to be funded at a level commensurate with its return on investment. If you believe that additional headcount, technology, training, or other investments are needed to empower your support organization to deliver the best possible service, you should ask for it...no, you should demand it! But you should do so armed with the knowledge—and proof—that your support organization is producing a positive ROI for the enterprise.
Jeff Rumburg is a cofounder and managing partner at MetricNet, LLC, where he is responsible for global strategy, product development, and financial operations. As a leading expert in benchmarking and re-engineering, Jeff is the author of a best-selling book on benchmarking, and he has been retained as a benchmarking expert by several well-known companies, including American Express, EDS, and General Motors. Prior to founding MetricNet, Jeff was the president and founder of The Verity Group, and held senior leadership positions at META Group and Gartner, Inc. He received his MBA from the Harvard Business School and his MS in operations research from Stanford University.