A Low-Dimension Shrinkage Approach to Choice-Based Conjoint Estimation

50 Pages Posted: 29 Sep 2020 Last revised: 7 Apr 2021

See all articles by Yupeng Chen

Yupeng Chen

Nanyang Business School, Nanyang Technological University

Raghuram Iyengar

University of Pennsylvania - Marketing Department

Date Written: August 12, 2020

Abstract

Estimating consumers' heterogeneous preferences using choice-based conjoint (CBC) data poses a considerable modeling challenge, as the amount of information elicited from each consumer is often limited. Given the lack of individual-level information, effective information pooling across consumers becomes critical for accurate CBC estimation. In this paper, we propose an innovative low-dimension shrinkage approach to pooling information and modeling preference heterogeneity, in which we learn a low-dimensional affine subspace approximation of the heterogeneity distribution and shrink the individual-level part-worth estimates toward this affine subspace. Drawing on recent modeling techniques for low-rank matrix recovery, we develop a computationally tractable machine learning model for implementing this low-dimension shrinkage and apply it to CBC estimation. We use an extensive simulation experiment and a field data set to demonstrate the superior performance of our low-dimension shrinkage approach as compared to alternative benchmark models.

Keywords: Choice-Based Conjoint, Preference Heterogeneity, Low-Dimension Shrinkage, Machine Learning

JEL Classification: M31

Suggested Citation

Chen, Yupeng and Iyengar, Raghuram, A Low-Dimension Shrinkage Approach to Choice-Based Conjoint Estimation (August 12, 2020). Nanyang Business School Research Paper No. 20-19, Available at SSRN: https://ssrn.com/abstract=3672517 or http://dx.doi.org/10.2139/ssrn.3672517

Yupeng Chen (Contact Author)

Nanyang Business School, Nanyang Technological University ( email )

Singapore, 639798
Singapore

Raghuram Iyengar

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States

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