Preference regression: Difference between revisions

Revision as of 04:29, 11 July 2003

Revision as of 02:21, 13 January 2004

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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 (in marketing)|factor analysis]] and is used to create ideal vectors on [[perceptual mapping|perceptual maps]]. <BR><BR>

<div style="float:right;width:350px;margin-left:5.5em;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.

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An alternative method is the '''self-stated importance method''' in which direct survey data is used to determine the weightings rather than statistical imputations. A third method is [[conjoint analysis (in marketing)|conjoint analysis]] in which an additive method is used.

''See also : [[marketing]], [[product management]], [[positioning (marketing)|positioning]], [[marketing research]], [[perceptual mapping]], [[multi dimensional scaling (in marketing)|multi dimensional scaling]], [[factor analysis (in marketing)|factor analysis]], [[discriminant analysis (in marketing)|discriminant analysis]]''