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Straightforward Analytics: Establishing Measures

Originally published April 28, 2011

In last month’s article we considered the first phase in our sequence of steps for establishing a culture of analytics: defining the objective. That phase targets improvement along a selected specific value driver coupled with a specific method for improvement.

Once the value driver and method are identified, the next step is to characterize the current level of performance or success, as well as speculate the potential benefit (or “lift”) that can be achieved as a result of taking actions suggested by analysis.
This means the ability to employ quantifiable measures of and setting specific targets for operational or business performance. For example, if the objective is to increase customer satisfaction by reducing the duration of inbound call hold time (note: the value driver is “customer satisfaction,” and the method is “reducing hold time”), we must establish the measure (average number of seconds on hold), a method for capturing the measures (in this case, pulling the data from call detail information for each call), and set a target (e.g., reducing the average number of seconds of hold time by 20%).

Ultimately, one might expect that any measure of performance or success is directly connected to the organization’s corporate strategy. However, there are a few potential disconnects along the way in linking a measure to a corporate strategy. The first, believe it or not, happens when the organization does not actually have a corporate strategy. Although some organizations will attempt to position themselves with a vision statement, often that vision is hazy and does not lend itself to defined measures of success.

More often, though, the organization does have a vision and a strategy that identifies the key value drivers and corresponding measures of success, but those success measures are not effectively communicated to those who need to be aware of them. In other words, the people in the organization do not have enough familiarity with the business to effectively work toward achieving the strategic goals. As an example, a recent survey of nearly 3000 executives performed by IBM noted that the third most significant barrier to broad use of analytics within the organization is the “lack of skills in lines of business.” In this case, inefficiencies creep into the system and the focus is on tactical operations rather than improvements to the way the processes are intended to work.

The third disconnect occurs even when the organization has a strategy, has defined performance measures, and has implemented those measures. Yet those tasked with executing the tactical components of the vision and strategy do not understand how their own defined measures contribute to the corporate strategy. In a situation like this, there is a temptation to focus solely on increasing the score for the specific measure despite the absence of a line of sight to the business goals. While this may provide temporary improvements, the processes will eventually degenerate through the adoption of shortcuts that increase the measure but do not necessarily impact the business.

We can learn from these scenarios that the process of establishing measures requires more than just simple measures. Rather:

  • There must be well-defined corporate strategic objectives

  • There must be well-defined corporate success measures

  • Organizational value drivers and success measures must be appropriately communicated to all key personnel

  • There must be a line of sight from each individual’s activities to corporate success measures

  • Analytic measures must correspond to the corporate success measures

  • There must be clarity that identified improvements within discrete tasks contribute to overall corporate performance.

There is likely a hierarchical nature to corporate key performance indicators. Each high level assessment of value is measured as a composition of lower-level indicators. For example, “overall customer satisfaction” for an airline might combine other, more discrete measures, such as overall on-time performance, passenger comfort, or cleanliness of planes. Each lower-level indicator, in turn, might again be measured as a composition of yet another layer of indicators. To continue the example, on-time performance might be a combination of a measure of on-time arrival, a measure of turnaround time for a plane at the gate, and a measure of on-time departure. Each of these can be broken down into discrete performance measures for specific activities or processes, at which point the line of sight from specific tasks to organizational performance can be established and communicated to the staff.

Delineating the hierarchal relationship between corporate strategy and individual areas of task or process performance may sound like a lot of work, but the value of the exercise is threefold. First, by connecting discrete tasks to corporate performance, staff members can see how their activities contribute to overall performance and each individual can understand his/her relationship to organizational success. Second, reviewing the connection between operational activities and corporate strategy helps vet the strategy as well as the operational environment, and may even identify replicated or non-contributory tasks.

Third, it will become evident that there are certain processes and activities that contribute to more than one key corporate performance measure. As in our call center example, reducing the average hold time might contribute to customer satisfaction and may also contribute to call center representative productivity by leading to increased throughput (since call times are reduced, more can be taken within the same time frame). Finding those activities that contribute to multiple aspects of corporate strategy allows one to prioritize areas for measurement and performance.

In essence, this performance measure hierarchy can guide the analyst in understanding what constitutes organizational success. These characterizations of success then provide the basis for establishing measures targeted for analysis.

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