Technical Papers
Title: CBC/HB Description: Hierarchical Bayes is a relatively new technique for computing individual- level part worths from CBC data. HB has been described favorably in several recent journal articles. Its strongest point of differentiation is its ability to provide estimates of individual part worths given only a few choices by each individual. It does this by "borrowing" information from other individuals. Although ICE also makes use of information from other individuals, HB does so more effectively, and requires fewer choices from each individual.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 choices and aggregate share predictions. Sawtooth Software is not an expert in Bayesian data analysis. In producing this software they have been helped by several sources. They have benefited particularly from the materials provided by Professor Greg Allenby in connection with his tutorials at the American Marketing Association\'s Advanced Research Techniques Forum. Download: |
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International Market Research Agency
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