One cannot infer from mean utilities what the distribution looks like. The standard deviation will tell you something about the distribution, though. Better, though, to look at histograms if you want to see something more about the distribution...especially if it is multi-modal.
Sometimes you will find that a brand that is preferred to another brand under average utilities actually becomes less preferred than the other brand within market simulations (even though you are holding all the other attribute levels constant for those two product alternatives within the market simulator). This is more likely to occur if the following two issues are happening for your data:
1) Imagine that there are 5 brands in the study. Imagine that Brand A is second best for all respondents, but never the best. On average, Brand A will have a high utility, but its simulated share of preference versus the other brands will be lower than expected. Simulated shares of preferences tend to give more weight to topmost brands because it tends to be a more extreme voting scheme than reflected by the underlying metric quality of the raw utilities.
2) The differences in utility between the two brands in question is actually fairly small.
If you are using our CBC software and its default "effects-coding" (part-worth) coding method and if you have no prohibitions between the two attributes you are interacting, then the main effects are estimated orthogonally to the interaction effects. That means that you can directly interpret just the main effects of brands with respect to one another, ignoring the interaction effects.