Hierarchical Bayes is an advanced technique for computing individual- level part worths from CBC data. HB has been described favorably in numerous journal articles. Its strongest point of differentiation is its ability to provide estimates of individual part worths given only a few choices by each individual. It does this by "borrowing" information from other individuals.
This technical paper describes the intuition and math behind HB, including results that suggest that HB is generally superior relative to aggregate approaches for estimating individual's choices and aggregate share predictions.