Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis—A Sparse Learning Approach

Yupeng Chen (), Raghuram Iyengar () and Garud Iyengar ()
Additional contact information
Yupeng Chen: Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Raghuram Iyengar: Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Garud Iyengar: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027

Marketing Science, 2017, vol. 36, issue 1, 140-156

Abstract: Consumers’ preferences can often be represented using a multimodal continuous heterogeneity distribution. One explanation for such a preference distribution is that consumers belong to a few distinct segments, with preferences of consumers in each segment being heterogeneous and unimodal. We propose an innovative approach for modeling such multimodal distributions that builds on recent advances in sparse learning and optimization. We apply the model to conjoint analysis where consumer heterogeneity plays a critical role in determining optimal marketing decisions. Our approach uses a two-stage divide-and-conquer framework, where we first divide the consumer population into segments by recovering a set of candidate segmentations using sparsity modeling, and then use each candidate segmentation to develop a set of individual-level heterogeneity representations. We select the optimal individual-level heterogeneity representation using cross-validation. Using extensive simulation experiments and three field data sets, we show the superior performance of our sparse learning model compared to benchmark models including the finite mixture model and the Bayesian normal component mixture model.

Keywords: sparse machine learning; multimodal continuous heterogeneity; conjoint analysis (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://doi.org/10.1287/mksc.2016.0992 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:36:y:2017:i:1:p:140-156

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().