This paper was originally published in the March 2000 Quirk's Marketing Research Review. HB has been receiving a lot of attention lately. Until recently, desktop PCs weren't powerful enough to handle typical data sets and commercial software wasn't available. Now HB is accessible to mainstream market researchers. HB is receiving so much attention because it consistently matches or beats traditional OLS estimation for individual-level parameters, and can estimate individual-level models for choice-based conjoint (CBC) data. Traditional aggregation methods confound heterogeneity with noise. By modeling the heterogeneity in the data, HB can achieve more precise estimates. This usually leads to more accurate models, whether the researcher is interested in aggregate or individual-level predictions. This paper gives examples of how HB can be applied to traditional regression-based problems (like customer satisfaction data sets), ACA data or choice-based conjoint (CBC). It explains why HB is beneficial for each of those applications.