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Margaret Everitt
II.B. Extended internal preference mapping output Extended internal preference mapping is most commonly used when the focus of the research is to understand the most important sensory drivers of consumer liking and thereby improve an existing product. The data are usually pretreated using a cluster analysis technique, typically a hierarchical method such as average linkage or Ward’s method, to formally test for the presence of segments (clusters) of consumers with different preference patterns. Figure 8.1 illustrates an extended internal preference map to which cluster analysis has been applied. The analysis provides a correlation coefficient for each sensory attribute in relation to consumer liking, and is done for each cluster. This enables the key attributes (both negative and positive) to be confidently defined in relation to the most-liked products. The analysis also provides an indication of the level of importance, i.e. the weighting that each of these attributes should be given in the sensory specification. Looking at the example of the ANOVA table for consumer ‘preference’ Cluster 1 in Figure 8.2, the product mean scores listed to the right suggest that this consumer group is quite decisive about which products they like and dislike. Product 301 is distinguished from all others as the most liked statistically, with products 741 and 893 clearly the least liked. It is also clear that flavor is far more important (Figure 8.3) in this group of consumers than appearance or texture, as the most significant liking correlations mainly relate to flavor attributes. In this example, cinnamon,
Figure 8.1 Illustration of extended internal preference map to which cluster analysis has been applied. The preference direction of four clusters is indicated by the black arrows. The PCA (principal component analysis) map is created based on consumer liking data; the sensory attributes are then regressed into the liking space in relation to each preference axis.