Preference regression: Difference between revisions

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{{unreferenced|date=February 2008}}

[[Image:PerceptualMap3.png|thumb|right|Perceptual map of competing products with ideal vectors]]

'''Preference regression''' is a statistical technique used by marketers to determine consumers’ [[Preference|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]].

==Application==

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 [[regression analysis|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 (in marketing)|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.

==Alternative methods==

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==See also==

* [[Marketing]]

* [[Product management]]

* [[Positioning (marketing)]]

* [[Marketing research]]

* [[Perceptual mapping]]

* [[Multidimensional scaling]]

* [[Factor analysis]]

* [[Linear discriminant analysis#Marketing]]

* [[Preference-rank translation]]

==References==

[[Category:Market research]]

[[Category:Marketing]]

*{{Cite book | last1 = Park | first1 = S. T. | last2 = Chu | first2 = W. | doi = 10.1145/1639714.1639720 | chapter = Pairwise preference regression for cold-start recommendation | title = Proceedings of the third ACM conference on Recommender systems - RecSys '09 | pages = 21 | year = 2009 | isbn = 9781605584355 }}

*Jarboe, G.R.; McDaniel, C.D.; Gates, R.H. (1992). "Preference regression modeling of multiple option healthcare delivery systems". ''Journal of Ambulatory Care Marketing'', 5(1), p.71-82.

[[Category:MarketingChoice modelling]]

[[Category:MarketQuantitative marketing research]]

[[Category:Product management]]

[[Category:ConsumerDimension behaviourreduction]]

[[Category:RegressionMarketing analysisanalytics]]

[[Category:Multivariate statistics]]