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A quantile regression perspective on external preference mapping

Author

Listed:
  • Cristina Davino

    (University of Naples Federico II)

  • Tormod Næs

    (Nofima AS)

  • Rosaria Romano

    (University of Naples Federico II)

  • Domenico Vistocco

    (University of Naples Federico II)

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.

Suggested Citation

  • Cristina Davino & Tormod Næs & Rosaria Romano & Domenico Vistocco, 2022. "A quantile regression perspective on external preference mapping," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 545-571, December.
    • Handle: RePEc:spr:alstar:v:106:y:2022:i:4:d:10.1007_s10182-022-00440-0
    DOI: 10.1007/s10182-022-00440-0
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    References listed on IDEAS

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    6. Cristina Davino & Rosaria Romano & Domenico Vistocco, 2020. "On the use of quantile regression to deal with heterogeneity: the case of multi-block data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 771-784, December.
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