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In the last few years, leading academics have developed a new technique for estimating conjoint utilities called Hierarchical Bayes (HB). HB significantly improves conjoint analysis results. While improvements are most dramatic for traditional conjoint (CVA) and choice-based methods (CBC), ACA also benefits from HB estimation.

ACA/HB provides the following benefits:

1. The ACA/HB module improves the quality of each individual's utility estimates by "borrowing" information from other individuals. This translates to more accurate predictions of both individual choices and share estimations.
2. ACA has been criticized because of potential scale incompatibilities between the self-explicated priors and conjoint pairs segments of the interview. ACA/HB provides a more theoretically sound way of combining data from these two sections of the interview. Not only is the technique more defensible, but the results are generally better.
3. ACA/HB does a better job of estimating utilities for the levels not taken forward into pairs when using "Most Likelies" and "Unacceptables."

As an additional benefit, your ACA surveys can now be shorter. ACA/HB does not make use of the "calibration concept" questions asked at the end of ACA surveys. Therefore, unless you require "purchase likelihood" simulations, you can cut this optional section.

ACA/HB is very computationally intensive. We suggest a fast processor (2Ghz or better) and at least 256MB RAM.

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