We often hear this question, because there is a correlation analysis embedded in our E-Value2 Data Analysis spreadsheets. The paragraphs below describe how the spreadsheets calculate importance, and how to interpret the results.

The correlations in our spreadsheets are between overall satisfaction and each of the attributes and benefits. A perfect correlation would be "1," indicating that an attribute always is rated higher by the same relative amount as the overall satisfaction score for each individual. No correlation would be "0," and a perfect negative correlation would be "-1."

The spreadsheets sort the attributes and benefits from high to low, based upon correlation to overall satisfaction. In effect, this yields "derived" importance. This differs from "stated" importance, in which people rate the importance of each attribute. In my opinion (and in the opinions of many others), derived importance is a superior measure. While it is quite possible for someone to "state" that something is the most important item, the derived importance may find it to be relatively low in importance, as it relates to overall satisfaction. The reverse also holds true.

By using derived importance, you can avoid focusing on improvements to attributes that will not have a positive impact on overall satisfaction. For example, an attribute may have a relatively low "performance" (agreement) score, but you wouldn't want to do much about it if it had a low correlation to overall satisfaction. The example I like to use is "carpeting." Suppose we had included an attribute named "I love the color of the carpeting." One wouldn't expect this to be as highly related to overall satisfaction as "I am treated as a valued employee" (which almost always ranks in the top ten in importance), because it has little to do with satisfaction. I would expect the correlation on such an item to be very low, which would indicate that you shouldn't run out and buy new carpeting.

Now, one caveat may be in order here. Some attributes may have ranked high in stated importance, but will be low in derived importance. This doesn't mean that you can cut back efforts in these areas. What the data are saying in this case is that "things are fine as is" with regard to that particular attribute. Letting performance slip may cause an attribute to rise in importance the next time you survey participants.