Hierarchical Bayes is an advanced technique that can be used in estimating part worths for conjoint analysis experiments. HB has been described favorably in many journal articles. Its strongest point of differentiation is its ability to provide estimates of individual part worths given limited information from 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 ACA utilities computed using HB are generally superior to those generated by the ACA system under OLS. ACA/HB utilities generally produce better hit rates and more accurate share predictions of holdout validation data. Furthermore, ACA/HB provides a more theoretically sound way to combine information from ACA's priors and pairs.