Average Severity as a Key Performance Metric
Should average severity be weighed as a key performance metric? While this question will likely elicit the immediate ‘of course’ from many readers, some insurance actuaries emphasize this metric too much at the expense of better measures. But this focus comes without fully understanding the underlying performance on parts, paint and repair decisions and most importantly the mix of repairs. These are the factors that drive average severity.
Many actuary managers use average repair severity as a heavily weighted metric to evaluate the performance of their Direct Repair Shops (DRP) and Staff appraisers without fully understanding the underlying behavior. A carrier actuary executive told me about a discussion he had with a manager who was about to take a long-term DRP off the carrier’s preferred auto shop list. The shop’s average severity was roughly $140 higher than the other shops in the area and had been that way for the last few quarters of the year. The manager told the executive “They are our worst performer because their severity is so much higher”. The executive’s response was thought provoking, stating, “They could be your best performer, and it would be a shame to lose them.”
The insurance actuary manager provided the executive with access to the underlying data for the shop in question as well as the other auto repair shops for comparison. The executive created a severity distribution chart on a bar chart with increments of $500. The chart revealed the natural bell curve of the severity and showed that the shop had a spike in severity from $5,500 to $7,000 during the time in question. The other shops in the comparison did not have similar spikes. The $1,500 severity spike drove the increase in average severity.
The executive continued digging deeper into the issue, asking the actuary manager, “How would you compare two shops that wrote an estimate on the same car?” The executive continued this line of questioning, “What if one shop has a $1,500 front hit and the other has a $3,000 rear end hit, how would you judge the shops if you only had those four estimates?” The manager replied that he would look at their selection of parts to make sure the shop chose cost-effective quality parts, then look at the repair decisions and the judgment time of the repair hours and see if the refinish operations were in line.
Exactly! Looking at each decision on the underlying factors that make up the average severity drives accuracy of repair and makes average severity only the first factor in the fact finding mission.
Insurance actuary managers face several common pitfalls when measuring performance of auto physical damage. The most common is relying solely on the percentage of dollars in parts performance. While it is a widely used metric, relying on that specific data set alone leads to ‘rewarding’ selection of more expensive alternate parts as it pushes the dollar percentages up. A similar caveat is true when looking at “repair vs. replace” decisions when the labor rates are significantly different.
For example, California has significantly different repair vs. replace ratios driven by vastly different labor rates in auto repair shops in San Francisco vs. Los Angeles. San Francisco’s hourly labor rate is roughly double what it is in Los Angeles, yet many insurers have not changed their Key Performance Indicator (KPI) to better match the impact this would have on repair vs replace decisions. While this is an extreme example, many states have average hourly labor rates that vary by 20% within different areas of the state. This magnitude would penalize an auto repair shop that had a higher hourly labor rate when compared to another shop in state with a the lower hourly labor rate.
Should average repair severity be a key performance indicator? No! Average paid severity is a better metric to guide you on your quest to understand the underlying performance metrics. Average repair severity is the product of the metrics that should be weighted and measured in every insurance actuary’s scorecard. Those metrics, with goals that consider the regional variances such as average labor rate and the impact of vehicle mix, will guide your company’s performance to the most accurate outcome.
Think of it as a modern version of the old adage “watch the pennies and the dollars will take care of themselves”.
