Expanding Service Department Metrics
In our work with dealers in multiple industries we see the same anxiety regarding the management, development and execution of the service department. Especially in periods of service market growth, the anxiety is heightened because of the diverse forces that play on service performance. These forces range from recruiting, selecting, onboarding and training cycles, to billing, expense management and measurement process. In today’s more sophisticated business environment we need to bring the analysis of service and service metrics to a deeper level.
Almost all dealers have relatively complete data processing systems that provide reasonable information on the performance of the service department. However, in today’s fast-paced data processing world, more than just standard reports are needed. All dealers should be engaged in data mining activities that allow us to dig deeper and analyze more specific operational statistics. Certainly, we have to start with a base set of service metrics – the traditional metrics – and supplement them with the new, deeper, richer analysis of service performance.
Traditionally, we have examined a number of key ratios. Service department gross profit benchmark is 65 percent, which is calculated by the following formula:
Overhead expenses in the service department are bench marked at 35 percent of labor revenue. These expenses include personnel expense at 20 percent, operating expense at 10 percent and occupancy expense at 5 percent.
Many dealers also measure technician productivity, which is calculated by:
More critically, an extension of technician productivity that we find more valuable is technician efficiency, which is measured by:
These are service department metrics that have been key measurements within the industry for a number of years. The expectation is that all reasonably run service departments have these metrics and are working with them continuously.
As we work with individual service departments, we have focused many of them on a group of supplemental data points to improve our management and, ultimately, to greatly improve bottom line results.
One of the most critical measures we see in managing service departments is revenue per technician per month. This measure correlates very strongly with service profit ability in all of the analyses we’ve done. It actually is the most strongly correlated – even more so than productivity or efficiency. So, our first recommendation is that you create a descending list by technician of revenue per technician per month. Further, accumulate this data over a period of time so that you can analyze the average revenue per tech per month and see if your trend is improving or not.
A competent technician should yield at least $12,000 per month. Better technicians can produce close to $15,000 per month. So, the first issue to be examined is, “How many technicians do we have greater than $15,000 per month, how many between $12,000 and $15,000 and how many under $12,000?” Frequently, we only see the average number. This tends to limit our expectations to the average number. If, in fact, one-third of the technicians are close to $15,000 per month, then that understanding will push us to drive other technicians to a number approaching $15,000 per month. In other words, let’s prove a benchmark that high performing technicians are achieving and use that benchmark to drive the performance of the rest of the team.
Another valuable measure, and one that is derivative of this analysis, is to take the revenue per technician per month and divide it by the number of billed hours in that month. That analysis produces a yield of revenue per hour for that technician. Let’s assume that your published service rate is $85 per hour. If this analysis shows that you have some technicians yielding $89 per hour, some yielding $82 per hour and some yielding $70 per hour, then the process is to analyze whether those differences are execution related, customer related, type of work related or related to some other factor.
One more critical measure we are using with dealers when consulting on service departments addresses the yield per hour per customer. Here, we want to begin with the descending customer list. We know in analyzing data in dealerships that the Pareto Principle applies. In fact, in most instances, even in service, 10 percent of the customers are 70 percent of the business. So, first complete a descending sales list, but cut off this analysis for the top 10 percent only, or the top 70 percent of revenue, whichever you prefer. Take this short list, divide labor revenue by billed hours and develop your yield per hour per customer. Again ask, how many are greater than $85 per hour, how many are at $85 per hour and how many are less than $85 per hour – more critically, why are they at this level?
So, as we examine service departments we want to reconfirm the traditional measures around which we drive service success. Plus, we want to introduce these three new measures to see what additional insight they give us into driving service performance.
Data is available. Be creative in seeking information that answers key questions. Don’t be surprised if this data also helps answer questions you haven’t even asked.
Matthew Hicks is with Currie Management Consultants, Inc., located in Worcester, Massachusetts, and on the web at www.curriemanagement.com.
posted at MHEDA Journal