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'''Preference regression''' is a statistical technique used by marketers to determine consumers’ preferred core benefits. It usually supplements [[positioning (marketing)| product positioning]] techniques like [[multi dimensional scaling (in marketing)|multi dimensional scaling]] or [[factor analysis]] and is used to create ideal vectors on [[perceptual mapping|perceptual maps]]. <BR><BR>
<div style="float:right;width:350px;margin-left:1.5em;text-align:center">[[image:Image:PerceptualMap3.png|alt text]]<br>''Perceptual Map of Competing Products with Ideal Vectors''</div><BR>Starting with raw data from surveys, researchers apply positioning techniques to determine important dimensions and plot the position of competing [[product (business)|products]] on these dimensions. Next they regress the survey data against the dimensions. The independent variables are the data collected in the survey. The dependent variable is the preference datum. Like all regression methods, the computer fits weights to best predict data. The resultant regression line is referred to as an ideal vector because the slope of the vector is the ratio of the preferences for the two dimensions.▼
▲Starting with raw data from surveys, researchers apply positioning techniques to determine important dimensions and plot the position of competing [[product (business)|products]] on these dimensions. Next they regress the survey data against the dimensions. The independent variables are the data collected in the survey. The dependent variable is the preference datum. Like all regression methods, the computer fits weights to best predict data. The resultant regression line is referred to as an ideal vector because the slope of the vector is the ratio of the preferences for the two dimensions.
If all the data is used in the regression, the program will derive a single equation and hence a single ideal vector. This tends to be a blunt instrument so researchers refine the process with cluster analysis. This creates clusters that reflect [[market segment]]s. Separate preference regressions are then done on the data within each segment. This provides an ideal vector for each segment.
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