A View into Appraiser Effectiveness: How Does your Staff Stack Up?

Bart Mazurek / Insurance, Perspectives /

simple_data

(This article was originally published in Property Casualty 360.)

A majority of my conversations with appraisal supervisors from auto insurance carriers include a discussion on appraiser effectiveness, which makes sense since these are the individuals that can have a significant impact on claim cost, cycle time and customer satisfaction. Most frequently, I hear that the obstacle to understanding the true effectiveness of appraisers is that the data can become too cumbersome – some insurers can review 20 measures or more to gain the insight needed to track and measure individual performance. Scaling this activity across their appraisers to get a peer comparison can be an extremely time-consuming exercise, which means it doesn’t always get done, resulting in organizational blind spots and missed opportunities.

Working for CCC, which contains a vast warehouse of claims data and provides software and services to more than 350 auto physical damage (APD) insurance carriers, my colleagues and I started to research how we could help appraisal supervisors increase their visibility into staff performance. Our goal was to help enable an actionable snapshot through the smart and simple application of key claims-related data based on important metrics identified by the insurance carrier. The result of this work is a data model that helps identify individual and peer-to-peer appraiser performance within an organization using a set of carrier-specific metrics.

What can a simple data model really tell you?

When we started thinking about this, we didn’t have a set number of measures in mind; our guiding principles were to offer something simple and actionable. We researched other similar models to see what lessons had already been learned in taking this type of approach. The NFL passer rating, which compares quarterbacks week-to-week, over the course of the season, and over their careers – caught our attention.

In place for more than 30 years, the NFL passer rating uses four variables: completion percentage, yards per attempt, touchdowns per attempt and interceptions per attempt. Each is weighted differently and an average is established and a rating is assigned. The QBs, coaches and general managers all know this system and can therefore communicate using a standard language and make informed decisions based upon individual objectives and strategies.

The simplicity of this formula has drawn some critics, but it has stood the test of time and has been referenced as being among the most indicative measures of wins and losses.1

Can a similar concept be applied to APD insurance carriers?

We think so.

Just as each NFL team has its own strategies and playbooks, we know each claims organization approaches claims differently. Through our work, however, my colleagues and I have found that the expectations for appraisers within an enterprise tend to be uniform. Sure, there may be regional differences, such as vehicle mix, but a management team usually has a universal philosophy on how the field staff should approach assigned claims.

Since these uniform expectations exist, we started analyzing the data in CCC’s warehouse, which includes more than 170M claims worth of data. My colleagues and I focused on data that could help supervisors quickly gauge the performance of individual appraisers on a standard scale within an organization for the purpose of driving more effectiveness across their teams. Staying with our simplicity theme, we observed that just a few data elements can provide meaningful insight into performance. While these characteristics could be applied to a broad set of data, we chose four broad measures that a hypothetical insurance carrier could use to get an effective snapshot of their staff’s performance based on metrics that it cares about – speed and accuracy. We focused our analysis on those activities that were directly in the appraisers’ control:

  • Repair percentage of labor amount
  • Parts utilization, amount and count
  • Cycle time (assignment sent to estimate sent days average)
  • Supplement percentage and average supplement amount for claims with supplement

Different measures could easily be substituted for the ones in the hypothetical insurance carrier’s model based on the insurance carrier’s specific needs. Depending on the insurance carrier, there may be priorities that require the use of different metrics. The key is to keep it simple so that once the model is set up, the data can easily be refreshed and results reviewed.

For this hypothetical insurance carrier, each measure is weighted differently but similarly, and the average of these measures becomes the appraiser rating. We decided on a scale of 1 to 100, with a rating of 50 reflecting average performance. This number rating makes it a simple exercise to identify top and bottom performers, so focus can be placed on areas where improvement may be required.

From a practical application perspective, and using the measures outlined above, a manager may find that certain appraisers have a significantly larger average supplement amount than their peers. Assuming that appraiser sees similar vehicles to the rest of the staff, this may be a coaching opportunity.

Looking at the data

For example, let’s say the hypothetical insurance carrier had the following averages for their key performance metrics:  

  • Thirty-six (36) percent of repair percentage labor amount
  • $1,027 in parts cost per claim
    • 6.7 parts replaced per repairable vehicle
    • 2.6 days cycle time (assignment to estimate complete)
    • Thirty-seven (37) percent for supplements
      • $1,400 average supplement amount for claims with supplements

Let us assume that an appraiser for the hypothetical insurance carrier had the above numbers. Their score would be a 50 out of 100. Depending on how this appraiser’s numbers compared to his peers at the insurance carrier, the manager would identify areas for this appraiser to focus on in order to align with the insurance carrier’s expectations. If the overall score for this appraiser was in the top half of his peers, the managers may decide to put more emphasis on staff members that ranked in the bottom quartile of the appraisers.  Alternatively, the manager may decide to focus on individual measures amongst the appraisers.  For example, this appraiser’s supplement frequency may be high compared to his peers (45% versus 37%). The manager may focus more on what is leading to the reopening of the file and examine the appraisers daily work habits to suggest changes.

In today’s data-driven world, measuring performance is essential. Direct Repair Program scorecards have been effective in identifying how repair shops are performing, and this similar application can allow for a simple and quick look at your own staff.

Interested in learning more or setting up your own simple model? Contact me at bmazurek@cccis.com.


 

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Sources

1 Byrne, Kerry of Cold Hard Football Facts.  Sports Illustrated, 2011.