Metrics_ How to Improve Key Business Results - Martin Klubeck [98]
You can imagine the many permutations possible that can affect the viewer's perception of the data. One may make you feel the data is “bad,” another that the data is “all right,” and another may make you see it as “good.”
The interpretations of the metric based on how it is presented must be limited. This is done through consistent and thorough communications on what you are looking for—anomalies. You cannot get caught up with how things look; not with how the charts look or how they make you or your unit look.
It is critical that you understand expectations and how to evaluate the charts presented. You're looking for trends and anomalies, not a feeling of “goodness” based on the colors or values. This is the reason I have attempted to give these charts only neutral colors and even leave off any obvious demarcations of the values that constitute an Opportunity for Improvement vs. Exceeding Expectations. Table 10-3 shows the expectations for percentage of rework.
We again were able to use percentages—which provided a consistent view of the different measures. While we used various measures (triangulation), we simplified it all by using a consistent form, a consistent view (the customers') and a consistent set of “grades” for each. We were able to keep to this established set of norms with Usage.
Usage
Usage was defined by the number of unique customers (Table 10-4). The number (data) by itself was meaningless. But simply putting it into context in the form of a measure, percentage of customer base, made it more useful. When we started measuring it, we looked at a year at a time. We showed the number of unique customers per month (a running total) so we could see slow months from more active ones. When we showed the measures over time, we started with a blank slate each year. This always put us below expectations at first, and showed a steady increase over the year until at the end of the year, we were well within expectations (see Figure 10-8).
Since this always gave the impression (and grade) of an Opportunity to Improve until the second quarter, we relooked at the presentation of the information. Not because it “looked bad” but because it was telling the wrong story. Our usage wasn't lower at the beginning of the year—we were incorrectly starting with a clean slate each year.
Figure 10-8 shows how the usage, when viewed over a year's time, gives the impression that there is an anomaly for the first three months.
Figure 10-8. Usage: first time callers cumulative over time
So, a better representation was to have the measures shown over a running period of time. We could show it over a full year's time (since we had enough historical data, or a smaller span of time. Options included showing a running three month or six month total as well as the twelve month total. Whichever we chose, we'd have to determine the expectations—what percentage of unique customers do we expect to use our services in a year, half a year, or a quarter?
Another factor to consider was the expected frequency of use for the service. For services that were likely to be used only once a year (like a car tune up), the service provider would measure first time callers over an annual period.
If the service were a semi-annual one, say more like an oil change, it would make sense to measure it at that interval. The point being, it would depend on how often you would expect customers to come back to use your service or buy your product. Even a restaurant would work in this manner. A higher-scale restaurant may expect to see repeat customers on a monthly or quarterly basis, while a fast-food restaurant may expect a higher frequency.
The measure of unique customers can easily be combined with repeat customers. Most businesses rely on repeat customers for the majority of their income. Repeat-customer rates speak directly to the relationship the business has with their customers. To grow, much less survive, the business must satisfy their customer base so that they earn their