A quantile regression perspective on external preference mapping

Cristina Davino, Tormod Næs, Rosaria Romano () and Domenico Vistocco
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Cristina Davino: University of Naples Federico II
Tormod Næs: Nofima AS
Rosaria Romano: University of Naples Federico II

AStA Advances in Statistical Analysis, 2022, vol. 106, issue 4, No 2, 545-571

Abstract: Abstract External preference mapping is widely used in marketing and R&D divisions to understand the consumer behaviour. The most common preference map is obtained through a two-step procedure that combines principal component analysis and least squares regression. The standard approach exploits classical regression and therefore focuses on the conditional mean. This paper proposes the use of quantile regression to enrich the preference map looking at the whole distribution of the consumer preference. The enriched maps highlight possible different consumer behaviour with respect to the less or most preferred products. This is pursued by exploring the variability of liking along the principal components as well as focusing on the direction of preference. The use of different aesthetics (colours, shapes, size, arrows) equips standard preference map with additional information and does not force the user to change the standard tool she/he is used to. The proposed methodology is shown in action on a case study pertaining yogurt preferences.

Keywords: Linear preference map; Variability of liking; Conditional quantiles (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10182-022-00440-0

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