Preference Mapping (PREFMAP)

What is preference mapping?

Preference Mapping allows to build maps which are useful in a variety of domains. A preference map is a decision support tool in analyses where a configuration of objects has been obtained from a first analysis (PCA, MCA, MDS), and where a table with complementary data describing the objects is available (attributes or preference data).

There are two types of preference mapping methods:

1. External preference mapping or PREFMAP, which is detailled here.
2. Internal preference mapping

What can I learn using preference mapping?

In the market research and consumer analytics domains (sensory data analysis), Prefmap is used to analyze products (the objects) and to answer questions such as:

• How is our product positioned compared with the competitors' products?
• Which product is the closest to ours?
• Which type of consumer prefers my product?
• Why are the competitors' products positioned as such?
• How can I reposition my product so that it fits better my target group?
• What success can I expect from my product?
• Which new products should I encourage the R&D department to create?

Preference mapping provides a powerful approach to optimizing product acceptability.

Preference mapping projection methods

XLSTAT offers several regression models to project complementary data on the objects maps:

• Vector model,
• Circular ideal point model,
• Elliptical ideal point model,
• Quadratic ideal point model.

XLSTAT results for preference mapping

XLSTAT displays detailed results in addition to the preference map to facilitate the interpreting of results.

Preference map

The preference map is a summary view of three types of elements: The judges (or groups of judges if a classification of judges has been carried out beforehand) represented in the corresponding model by a vector, an ideal point (labeled +), an anti-ideal point (labeled -), or a saddle point (labeled o); The objects whose position on the map is determined by their coordinates; The descriptors which correspond to the representation axes with which they are associated (when a PCA precedes the PREFMAP, a biplot from the PCA is studied to interpret the position of the objects as a function of the objective criteria).

The PREFMAP, with the interpretation given by the preference map is an aid to interpretation and decision-making which is potentially very powerful since it allows preference data to be linked to objective data. However, the models associated with the judges must be adjusted correctly in order that the interpretation is reliable.

Preference scores

The preference score for each object for a given judge, whose value is between 0 (minimum) and 1 (maximum), is calculated from the prediction of the model for the judge. The more the product is preferred, the higher the score. A preference order of objects is deducted from the preference scores for each of the judges.

Contour plot

The contour plot shows the regions corresponding to the various preference consensus levels on a chart whose axes are the same as the preference map. At each point on the chart, the percentage of judges for whom the preference calculated from the model is greater than their mean preference is calculated. In the regions with cold colors (blue), a low proportion of models give high preferences. On the other hand, the regions with hot colors (red) indicate a high proportion of models with high preferences.