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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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Presentation Transcript

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?


Perceptual map automobile

Project Plan

Perceptual Map: Automobile

Research

Objectives

Understand Customers

Get Data

Survey

Attribute Ratings

Analytic

Approach

Factor Analysis

Analysis

Software

R

Reporting

Perceptual

Map



Factor Analysis

  • Exploratory: no guiding hypotheses

  • Confirmatory: set of hypotheses that form the conceptual basis


Survey attribute ratings
Survey: Attribute Ratings

Many more features, options….


Survey attribute ratings1
Survey: Attribute Ratings


Correlation Matrix

cor(data, digits=2)


install.packages("corrgram")

library(corrgram)

corrgram(data)


Factor analysis variable reduction

Factor Analysis

Factor Analysis / Variable Reduction

Correlation Matrix

  • Correlated variables are grouped together and

  • separated from other variables with low or no correlation


Factor Analysis

F1

F2

FN

F3

….


Factor Analysis

F1

F2

FN

F3

….

b’s Factor Loadings


Psych Package – includes FA


Psych Package – fa

library(psych)

rmodel <- fa(r = corMat, nfactors = 3, rotate = “none", fm = "pa")


Psych Package


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


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


Psych Package – fa

library(psych)

rmodel <- fa(r = corMat, nfactors = 3, rotate = "oblimin", fm = "pa")



Psych Package – principal

library(psych)fit <- principal(ratings6, nfactors=4, rotate=“null")


Model

model <- princomp(data, cor=TRUE)

summary(model)

biplot(model)


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)])


Psych Package – principal

plot(fit)


Output

# scree plot

plot(fit,type="lines")



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


Perceptual Map

Factor Loadings

Weights

Average

Brand Ratings

Variance


Which one

Which Car?

Which One?

Price

$$$

Aaaah factor…

BORING

Sweet!!!

$



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