If you've been to a technical market research conference lately, you've likely heard presentations advocating a technique called Hierarchical Bayes estimation (HB). The possible applications for HB are far-reaching. If there is heterogeneity among individuals, HB can significantly improve upon traditional aggregate models such as: OLS regression or logit for conjoint/choice analysis, customer satisfaction, brand image studies, or any other situation in which respondents provide multiple observations.
Until recently, the individuals advocating HB were academics and a few practitioners expert in statistics. HB is demanding both in terms of computational time and complexity. For realistic market research data sets, the run times were counted in days rather than minutes or hours. Given that no easy-to-use HB software existed and computers were not fast enough to deal with real world problems in a reasonable time frame, it is not surprising that some practitioners were skeptical of HB and the hype surrounding it.
Until recently, we too were doubtful that HB would soon achieve very wide-spread use in the marketing research community. But recent advances in the processing speed of PCs have exceeded our expectations and knowledgeable academics such as Greg Allenby of Ohio State have taught tutorials, published algorithms on HB estimation, and been supportive of our efforts to develop easy-to-use software.
In short, why all of the attention for HB?
- In application after application where respondents provide multiple-observation data, HB estimation seems at least to match and usually to beat traditional models. Of particular interest to Sawtooth Software users, conjoint analysis is a prime example of an application that benefits from HB estimation.
- HB estimation is robust. The HB models supported by Sawtooth Software are not subject to local minima.
- HB permits estimation of individual-level models, which lets marketers more accurately target/model individuals. More specifically, HB permits estimation of models too demanding for traditional methods: even when estimating more beta coefficients per individual than there are individual observations.
- Aggregate estimation models confound heterogeneity and noise. By modeling individuals rather than the "average," HB can separate signal (heterogeneity) from noise. This leads to more stable, accurate models whether viewed in terms of individual- or aggregate-level performance.
- The "draws" (replicates) for each respondent provide a rich source of information for more accurately conducting statistical tests and, for example, estimating nonlinear functions of parameters such as shares of preference.
Two other articles in this newsletter illustrate the benefits of HB estimation with respect to ACA and regression-based models. We do not suggest that HB is a panacea. However, we have been impressed by the way HB handles numerous real-world and synthetic data sets that we have tested. It generally beats other analytical techniques with which we are familiar. We expect HB soon to become a mainstream analytical technique for market research.