Preference regression

It usually supplements product positioning techniques like multi dimensional scaling or factor analysis and is used to create ideal vectors on perceptual maps.

Starting with raw data from surveys, researchers apply positioning techniques to determine important dimensions and plot the position of competing products on these dimensions.

Like all regression methods, the computer fits weights to best predict data.

This tends to be a blunt instrument so researchers refine the process with cluster analysis.

Separate preference regressions are then done on the data within each segment.