Hierarchical Bayes is an advanced technique for computing individual- level estimates of regression coefficients or part worths. HB has been described favorably in numerous journal articles. Its strongest point of differentiation is its ability to provide estimates of individual parameters given only a few observations by each individual. It does this by "borrowing" information from other individuals.
HB-Reg is a generalized software program for running Regression-based HB. The user provides the data in an Excel-compatible .csv file. Potential uses for HB-Reg include traditional ratings-based conjoint experiments, customer satisfaction studies, or price elasticity measurement from scanner data.
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 regression coefficients or part worths for conjoint experiments.
