Web-based
customer panels and web-based multimedia capabilities offer
the potential to get in-formation from customers rapidly and
iteratively based on virtual product profiles. However, web-based
respondents are impatient and wear out more quickly. At the
same time, in commercial applications, conjoint analysis is
being used to screen large numbers of product features. Both
of these trends are leading to a demand for conjoint analysis
methods that provide reasonable estimates with fewer questions
in problems involving many parameters.
In this
paper we propose and test new adaptive conjoint analysis methods
that attempt to reduce respondent burden while simultaneously
improving accuracy. We draw on recent "interior-point" developments
in mathematical programming which enable us to quickly select
those questions that narrow the range of feasible partworths
as fast as possible. We then use recent centrality concepts
(the analytic center) to estimate partworths. These methods
are efficient, run with no noticeable delay in web-based questionnaires,
and have the potential to provide estimates of the partworths
with fewer questions than extant methods.
After introducing
these "polyhedral" algorithms we implement one such algorithm
and test it with Monte Carlo simulation against benchmarks such
as efficient (fixed) designs and Adaptive Conjoint Analysis
(ACA). While no method dominates in all situations, the polyhedral
algorithm appears to hold significant potential when (a) profile
comparisons are more accurate than the self-explicated importance
measures used in ACA, (b) when respondent wear out is a concern,
and (c) when the product development and marketing teams wish
to screen many features quickly. We also test a hybrid method
that combines polyhedral question selection with ACA estimation
and show that it, too, has the potential to improve predictions
in many contexts. The algorithm we test helps to illustrate
how polyhedral methods can be combined effectively and synergistically
with the wide variety of existing conjoint analysis methods.
We close
with suggestions on how polyhedral algorithms can be used in
other preference meas-urement contexts (e.g., choice-based conjoint
analysis) and other marketing problems.
Download
Paper (pdf)
.
