Capturing Heterogeneity Among Consumers with Multitaste Preferences

51 Pages Posted: 10 Feb 2016 Last revised: 2 Sep 2021

See all articles by Liu Liu

Liu Liu

University of Colorado at Boulder - Leeds School of Business

Daria Dzyabura

New economic school

Date Written: September 1, 2021

Abstract

In some product categories, consumers’ individual preferences may consist of multiple distinct tastes defined over product attributes. Capturing heterogeneity among multitaste consumers requires new models, as a consumer simultaneously belongs to multiple segments. This is a different type of heterogeneity than that captured by existing models, such as mixed logit or latent class models, which estimate one taste per individual. In this paper, we propose a model that allows individual consumers to express multitaste preferences, and we provide an estimation procedure that scales to high-dimensional attribute spaces. Through extensive simulation experiments, we demonstrate that the proposed algorithm accurately recovers parameters, whereas single-taste benchmark models underfit and generate a misleading representation of both population- and individual-level preferences. We apply the algorithm to a large dataset of recipe choices to uncover rich patterns of preference heterogeneity. The proposed model fits the data better than single-taste benchmarks and provides additional individual-level insights.

Keywords: consumer heterogeneity, preferences, segmentation, machine learning, optimization

Suggested Citation

Liu, Liu and Dzyabura, Daria, Capturing Heterogeneity Among Consumers with Multitaste Preferences (September 1, 2021). Available at SSRN: https://ssrn.com/abstract=2729468 or http://dx.doi.org/10.2139/ssrn.2729468

Liu Liu (Contact Author)

University of Colorado at Boulder - Leeds School of Business ( email )

Boulder, CO 80309-0419
United States

HOME PAGE: http://www.colorado.edu/business/liu-liu

Daria Dzyabura

New economic school ( email )

100A Novaya Street
Moscow, Skolkovo 143026
Russia

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