Select, Organize Your Measures

As the amount of data grows and your access to it increases, you can quickly lose track of critical relationships among data. Building new reports becomes easier but can lead to a confusing array that loses focus on what is important. Organizing data means identifying the operating outcomes that are most crucial to you and using them as your mainstay.

The first step in gaining better control is to generate a list of measures that you care about and then put them into groups. Different companies will identify different measures; however, they fall into four distinct categories:

• Quality measures, which include the average and median amounts paid, quality review scores, and numbers indicating procedural compliance (such as alternative parts utilization in physical damage or validation of codes on medical bills).
• Efficiency measures, such as cost per claim and claims per adjuster.
• Cycle time measures focus on speed of completion and generally revolve around the time a claim is reported until closure.
• Customer measures often revolve around the results of customer satisfaction surveys.

It is critical to select a small set of key performance measures (KPMs), as few as one measure for each of the categories listed above. One should note that the average or median paid is a risky key measure. Making it such can result in unintended actions that are good neither for you nor your customers). These measures tie directly to your strategic objectives and are the ones that people at all levels of your organization know.

A small set of diagnostic measures should be identified. These numbers have a direct relationship with your KPMs and provide a context for more rapidly understanding your results. For instance, claim cycle time for a property damage claim might be supplemented with measures such as the average time to make contact and complete inspections. If overall claim cycle time starts to slip, then you can generally spot the trouble areas quickly in one or more of these diagnostic numbers.

Create a balanced set of measures that ensure your leadership does not push results in one area only to negatively impact them in another. Hold your leadership to a balanced assessment of performance. That is, set a standard that one-dimensional performance improvements do not make for excellence. Without a structured set of data using a format like the one described here, different managers will form different views of what is important. Consequently, they will react in completely divergent ways to get results.

When choosing your KPMs and diagnostic measures, combine qualitative and quantitative information. Qualitative measures refer generally to those that are derived from observations by people about work. Claim quality scores and customer surveys are good examples. Quantitative measures are derived by applying algorithms to the data in your systems. Calculating cycle time is a good example of a quantitative measure.

It is easy to focus on the quantitative and exclude the qualitative as you examine the data available. This is where consideration of measures is critical. Leaving out qualitative data deprives you of the human observations most effective in identifying root cause and understanding opportunity. In short, it produces an incomplete picture that can cause reactions to symptoms and without curing the disease.

Root Cause Analysis

Use performance goals for each KPM to guide assessment of results. Having measures without goals makes your purpose ambiguous and can prevent being able to gauge progress. Any result can feel significant, while none seem to provide guidance. When specific performance goals are identified, managers and leaders know what results to scrutinize to pinpoint causes of change. This starting point is critical for understanding what (if any) action should be taken.

Root cause analysis arises in two different scenarios, both of which should use a “hypothesis-confirmation” approach. Identify which situation you face. The first scenario involves investigating changes in results where there is some certainty as to the cause. For example, you may notice a spike in the average age of closed claims when reviewing cycle time. You may also know that one or more teams within the organization started efforts to reduce inventory by focusing on closing out older cases. With this as your hypothesis, honing in on these teams to confirm they caused the spike is quick and easy.

The second scenario is more complicated and infrequent. This scenario occurs when you are uncertain about the cause of changing performance. Before jumping into the data, you must methodically develop hypotheses to explain the change. These investigations are iterative, with each round offering insight as well as questions. Refer to the sidebar for more information about crafting hypotheses and acting accordingly.

To understand whether you have reached the source of your problems, answer this question: “If I changed X, then could I be confident that results would improve?” In the example used here, you might find that the change in procedure has had some impact. However, when you consider changing it, you may conclude this would not reverse the problem. Keep looking.

This may seem like a lot of work. Being anxious for results and bypassing these steps, though, often has the impact of prolonging the problem and, even worse, creating new causes for variance. To fix the problem, you have to remove the cause. Anything else is tampering.

Teach Users How to Review Data

One of the most important (but often overlooked) steps is teaching your managers and leaders how to use data. Assumptions are often made that the data will tell the story and the intuitive nature of reporting will make not only insight, but also action, self-explanatory. The simple fact is, most of us struggle to reach accurate conclusions without guidance. While we are surrounded by data in claims, and yet there is little done to formally educate managers on using it.

Develop rules of thumb for deciding when a result needs attention. When a number moves in a negative direction in a week or month, this does not mean that it needs to acted upon. Big moves and trends are important to dig into. Decide what constitutes a trend and a big move. Otherwise managers should note the change and monitor it.

Stay focused on plans that address problem results. In many cases, results don't change overnight. When deciding whether to respond to changes, managers need to consider the capacity of the organization to deal with the change and the recent history of changes. Sometimes, you will be faced with issues you want to address but must put off because your organization is currently responding to other challenges. Conversely, your organization may have the capacity, but you recently made changes in response to other, unrelated results. Implementing another change can create a “flavor of the moment” impression. It is counterproductive to have your team cringe each month or week when new reports come out. Discretion is the better part of valor in these situations.

Implement periodic commentary by managers about results. Having numbers is great. Teaching leaders to put together a cohesive story about them is greater. Monthly reports are drudgery when they are exercises in paper shuffling. When used as a platform to discuss results and insight, they are developmental exercises that create better leaders. Be sure to use these opportunities to improve judgment.