Hierarchical Bayes: Why All the Attention? (2000)

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.

  Download PDF   View in new window

Lighthouse Studio

Lighthouse Studio (formerly SSI Web) is our flagship software for producing and analyzing online and offline surveys. It contains modules for general interviewing, choice-based conjoint, adaptive choice-based conjoint, adaptive choice analysis, choice-value analysis, and maxdiff exercises.

Try Lighthouse Studio

  Buy Lighthouse Studio

Getting Started with Conjoint Analysis

New to conjoint analysis? Pick up a copy of Bryan Orme's best-selling book today!

Learn More