Please be warned that you probably cannot correctly compare attribute importance between groups of people if you are just using the standard aggregate logit utility estimation routine. That is because aggregate logit will often lead to seriously misleading attribute importance calculations due to not recognizing differences between people's preferences.
For example, imagine that half of the respondents love brand A and half love brand B (and these are the only two levels in your brand attribute). Imagine this attribute is extremely important. Aggregate logit simply averages across everybody, leading to utilities that are tied for Brand A and Brand B. So, the standard importance computation looks at the difference between Brand A and Brand B preference (no difference) and reports that Brand has zero importance for the sample. This is obviously not correct.
That's just one of the reasons that HB analysis (the standard way to analyze CBC data by our users) is so valuable. Each respondent's utility scores are estimated, so that the importance calculation is done within each individual, and the importance results are averaged across people. Under HB, the average utilities for the sample will still be reported to be tied, but the importance calculation will show that the importance for Brand for the sample is very large (since it is computed at the individual-level).
And, the software (SMRT) will allow you to split the HB utility results out by Male and Female, so you will see the zero-centered diffs (normalized utilities) summarized for the Males and the Females.
Using zero-centered diffs helps adjust for the fact that with CBC analysis (utility analysis) the raw utilities can be much larger magnitude for a group of respondents that answers with less error than another group. These differences can at times be very large...so the zero-centered diffs normalization affords some protection against making wrong conclusions regarding the utility one one level for one group vs. the same level for a different group.
Of course, importance scores (that are normalized to sum to 100) automatically take out differences in scale factor between people or groups. Importances calculated from zero-centered diffs or raw utilities are exactly the same (except for maybe a tiny bit of rounding error).