Our standard Hierarchical Bayes (HB) models rely only on respondent-level choices during estimation. However, in practice, conjoint results are primarily used to predict aggregate or segment-level choice shares. We explore a modification in which a semi-aggregate choice-share likelihood term is added to the HB likelihood. Early results suggest that this can improve aggregate share predictions while preserving the benefits of respondent-level estimation. This approach may also reduce the need for post-hoc adjustments such as scale corrections.
