This is probably not a good approach, unless all your attributes have preferences with a priori and logical order, because importances generally should be calculated at the individual level to avoid the complexity of issues that arise for this due to respondent heterogeneity.
For example, imagine an attribute like brand with just 2 levels: brand a and brand b. Imagine that 50% of the sample loves brand a and 50% of the sample loves brand b to an equal extent. In each alpha draw, the utilities for brand a and brand b will be right around 0 and 0 (due to the typical zero-centering in the utility estimation procedure). The importance calculation using average utilities for the sample will make it look like brand has nearly a zero importance, even though in reality respondents may have very strong opinions about their brand preferences.
On the other hand, if all your attributes are ordered attributes (speed, performance, price, etc.) where essentially all respondents would agree regarding which level was worst and which was best, then the approach like you describe would be reasonable.
All this said, I would probably stop focusing on Importance scores for reporting conjoint analysis results. They have relative meaning, based on the levels the researcher selected to be in the study. If you conduct a new study and shift the attribute ranges in terms of attribute levels, then you will obtain different attribute importances even though respondent preferences may remain constant.
