A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation

Gespeichert in:

Bibliographische Detailangaben
Beteiligte: Evgeniou, Theodoros, Pontil, Massimiliano, Toubia, Olivier
In: Marketing Science, 26, 2007, 6, S. 805-818
veröffentlicht:
Institute for Operations Research and the Management Sciences (INFORMS)
Medientyp: Artikel, E-Artikel

Nicht angemeldet

weitere Informationen
Umfang: 805-818
ISSN: 0732-2399
1526-548X
veröffentlicht in: Marketing Science
Sprache: Englisch
Kollektion: sid-55-col-jstoras4
sid-55-col-jstorbusiness1archive
sid-55-col-jstorbusiness
JSTOR Arts & Sciences IV Archive
JSTOR Business I Archive
JSTOR Business & Economics
Inhaltsangabe

<p>We propose and test a new approach for modeling consumer heterogeneity in conjoint estimation based on convex optimization and statistical machine learning. We develop methods both for metric and choice data. Like hierarchical Bayes (HB), our methods shrink individual-level partworth estimates towards a population mean. However, while HB samples from a posterior distribution that is influenced by exogenous parameters (the parameters of the second-stage priors), we minimize a convex loss function that depends only on endogenous parameters. As a result, the amounts of shrinkage differ between the two approaches, leading to different estimation accuracies. In our comparisons, based on simulations as well as empirical data sets, the new approach overall outperforms standard HB (i.e., with relatively diffuse second-stage priors) both with metric and choice data.</p>