Classification trees in consumer studies for combining both product attributes and consumer preferences with additional consumer characteristics

Abstract The main objective of this paper is to describe and discuss the use of classification trees in consumer studies. Focus will be given to the use of the method in relating segments of consumers, based on their acceptance pattern, to additional consumer characteristics, including attitudes, habits and demographics variables. Advantages of the method in handling typical issues from consumer studies will be discussed. Primary interest will be given to the validation of the results, which will also be compared with results from alternative methods widely used in consumer studies. The approach will then be illustrated by using data from a conjoint study of apple juice.

[1]  Pascal Schlich,et al.  Cartographie des préférences : un outil statistique pour l'industrie agro-alimentaire , 1992 .

[2]  Michel Tenenhaus,et al.  PLS Path modelling and multiple table analysis. Application to the cosmetic habits of women in Ile-de-France , 2001 .

[3]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[4]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[5]  Tormod Næs,et al.  Handling of individual differences in rating-based conjoint analysis , 2011 .

[6]  Andreas Herrmann,et al.  Conjoint Measurement: Methods and Applications , 2000 .

[7]  Vincenzo Esposito Vinzi,et al.  Two-step PLS regression for L-structured data: an application in the cosmetic industry , 2007, Stat. Methods Appl..

[8]  D. Benton,et al.  The development of the attitudes to chocolate questionnaire , 1998 .

[9]  K. Roininen,et al.  Quantification of Consumer Attitudes to Health and Hedonic Characteristics of Foods , 1999, Appetite.

[10]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[11]  P. Green,et al.  Conjoint Analysis in Consumer Research: Issues and Outlook , 1978 .

[12]  Tormod Næs,et al.  Choice Probability for Apple Juice Based on Novel Processing Techniques: Investigating the Choice Relevance of Mean-End-Chains , 2011 .

[13]  Evelyne Vigneau,et al.  External preference segmentation with additional information on consumers: A case study on apples , 2014 .

[14]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Tormod Næs,et al.  Statistics for Sensory and Consumer Science , 2010 .

[17]  S. R. Searle Linear Models , 1971 .

[18]  Evelyne Vigneau,et al.  Segmentation of consumers taking account of external data. A clustering of variables approach , 2002 .

[19]  Harald Martens,et al.  Regression of a data matrix on descriptors of both its rows and of its columns via latent variables: L-PLSR , 2005, Comput. Stat. Data Anal..

[20]  Michel Tenenhaus,et al.  PLS methodology to study relationships between hedonic judgements and product characteristics , 2005 .

[21]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[22]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[23]  Tormod Næs,et al.  Likelihood of buying healthy convenience food: An at-home testing procedure for ready-to-heat meals , 2012 .

[24]  Jean A. McEwan,et al.  Preference Mapping for Product Optimization , 1996 .

[25]  Tormod Næs,et al.  Identifying and interpreting market segments using conjoint analysis , 2001 .

[26]  M. Wedel,et al.  Market Segmentation: Conceptual and Methodological Foundations , 1997 .