# The Basics of Interpreting Conjoint Utilities

Users of conjoint analysis are sometimes confused about how to interpret utilities. Difficulty most often arises in trying to compare the utility value for one level of an attribute with a utility value for one level of another attribute. It is never correct to compare a single value for one attribute with a single value from another. Instead, one must compare differences in values. The following example illustrates this point:

```Brand A 40    Red  20    \$ 50   90 Brand B 60    Blue 10    \$ 75   40 Brand C 20    Pink  0    \$ 100   0 ```

It is not correct to say that Brand C has the same desirability as the color Red. However, it is correct to conclude that the difference in value between brands B and A (60-40 = 20) is the same as the difference in values between Red and Pink (20-0 = 20). This respondent should be indifferent between Brand A in a Red color (40+20=60) and Brand B in a Pink color (60+ 0 = 60).

Sometimes we want to characterize the relative importance of each attribute. We do this by considering how much difference each attribute could make in the total utility of a product. That difference is the range in the attribute's utility values. We percentage those ranges, obtaining a set of attribute importance values that add to 100, as follows:

```                   Range   Percent Importance Brand (B - C)      60 - 20 = 40      26.7 Color (Red - Pink)   20 -  0 = 20      13.3 Price (\$50 - \$100)   90 -  0 = 90      60.0                   ----      ----                   150      100.0 ```

For this respondent, the importance of Brand is 26.7%, the importance of Color is 13.3%, and the importance of Price is 60%. Importances depend on the particular attribute levels chosen for the study. For example, with a narrower range of prices, Price would have been less important.

When summarizing attribute importances for groups, it is best to compute importances for respondents individually and then average them, rather than computing importances using average utilities. For example, suppose we were studying two brands, Coke and Pepsi. If half of the respondents preferred each brand, the average utilities for Coke and Pepsi would be tied, and the importance of Brand would appear to be zero!

Users of ACA or CVA may download a module named IMP.EXE from our Internet home page (http://www.sawtoothsoftware.com) that will read files of individual utilities from ACA or CVA and create a file of individual attribute importances, and print the average importances. This should help you in determining and reporting attribute importances.