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Buy!!! What’s best for me? Which brand to buy? What Style? Color?

Business. Customer. Complex products, such as a cars, have dozens of criteria to consider. Sell!!! What matters to customers? Where are we positioned relative to competitors?. Buy!!! What’s best for me? Which brand to buy? What Style? Color?. Project Plan. Perceptual Map: Automobile.

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Buy!!! What’s best for me? Which brand to buy? What Style? Color?

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  1. Business Customer Complex products, such as a cars, have dozens of criteria to consider. • Sell!!! • What matters to customers? • Where are we positioned relative to competitors? • Buy!!! • What’s best for me? • Which brand to buy? • What Style? Color?

  2. Project Plan Perceptual Map: Automobile Research Objectives Understand Customers Get Data Survey Attribute Ratings Analytic Approach Factor Analysis Analysis Software R Reporting Perceptual Map

  3. Surveys

  4. Factor Analysis • Exploratory: no guiding hypotheses • Confirmatory: set of hypotheses that form the conceptual basis

  5. Survey: Attribute Ratings Many more features, options….

  6. Survey: Attribute Ratings

  7. Correlation Matrix cor(data, digits=2)

  8. install.packages("corrgram") library(corrgram) corrgram(data)

  9. Factor Analysis Factor Analysis / Variable Reduction Correlation Matrix • Correlated variables are grouped together and • separated from other variables with low or no correlation

  10. Factor Analysis F1 F2 FN F3 ….

  11. Factor Analysis F1 F2 FN F3 …. b’s Factor Loadings

  12. Psych Package – includes FA

  13. Psych Package – fa library(psych) rmodel <- fa(r = corMat, nfactors = 3, rotate = “none", fm = "pa")

  14. Psych Package

  15. Rotations Rotation Courtesy of Professor Paul Berger Factor 2 Each variable (circle) loads on both factorsand there is no clarity about separating thevariables into different factors, to give thefactors useful names. Factor 1

  16. Rotations Rotation Courtesy of Professor Paul Berger “CLASSIC CASE” NOW, all variables are loading on one factor and not at all the other; This is an overly “dramatic” case. After rotation of ~450 • Not Correlated  Orthogonal • Varimax = Orthogonal Rotation

  17. Psych Package – fa library(psych) rmodel <- fa(r = corMat, nfactors = 3, rotate = "oblimin", fm = "pa")

  18. Principal Components Analysis

  19. Psych Package – principal library(psych)fit <- principal(ratings6, nfactors=4, rotate=“null")

  20. Model model <- princomp(data, cor=TRUE) summary(model) biplot(model)

  21. Psych Package – principal Orthogonal / No Correlation library(psych)fit <- principal(ratings6, nfactors=4, rotate="varimax“) corrgram(ratings6[,(1,2,9,12,3,4,6,8,10,5,11,7,13)])

  22. Psych Package – principal plot(fit)

  23. Output # scree plot plot(fit,type="lines")

  24. 3 Factor vs. 4 Factor

  25. 3 Factor vs. 4 Factor Style Comfort Color Upgrade Packages Reliability Safety Country Origin Horsepower Nice Dash Miles Per Gallon Initial Price # of Miles on Car Financing Options Aaahh!!! Factor Money

  26. Perceptual Map Factor Loadings Weights Average Brand Ratings Variance

  27. Which Car? Which One? Price $$$ Aaaah factor… BORING Sweet!!! $

  28. Factor Analysis Recap

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