Extension of the consistency of the data obtained with the Ideal Profile Method: Would the ideal products be more liked than the tested products?

The Ideal Profile Method is a sensory method in which, for each product tested, consumers are asked to rate both the perceived and ideal intensities of a list of attributes. In addition, they are also required to indicate how much they like each product. At the end of the task, three blocks of data are collected from each consumer: the product profiles, their ideal profile and the liking ratings.The ideal profiles can be used to help improving the existing products. However, this information should be carefully managed since (1) it is obtained from consumers, and (2) it describes a virtual product. In order to use the full potential of the ideal profiles, and to avoid a possible misinterpretation of the data, one has to ensure that the information collected is consistent.The process checking for the consistency of the ideal profiles proposed here is based on the liking ratings: an ideal product should achieve higher hedonic ratings than the tested products, if it would be tested. But since the liking scores of the ideal products are unknown, they are estimated first. However, the comparison between liking scores (estimated for the ideals, measured for the tested products) would only make sense if the ideal descriptions have not been randomly given. For that matter, a hypothesis test checking for the significance of the ideal profiles is defined.In the perfume example provided, it appears that most of the consumers did not describe their ideals randomly. In addition, the estimations of the ideals liking scores are high compared to those given to the tested products. Hence, for most of the consumers, the ideal profiles are considered as consistent according to the potential liking of their ideal profiles.

► Ideal descriptions are not given randomly by most of the consumers.
► Ideal descriptions are potentially more appreciated than the tested products.
► Models based on PCR using only linear effects on more dimensions seem to be the most suitable.
► Ideal descriptions can be considered as consistent for most of the consumers.