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Perceptual Mapping

Skander Esseghaier

Data for this session is available in Data – Perceptual Mapping

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In this session, you will learn:

How to construct a map of product locations in the perceptual space of consumers

How to do it using Minitab

What attributes you should use when constructing a perceptual map

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What is Perceptual Mapping

A technique to understand the position of brands as consumers perceive them

The output is a map of product locations in the perceptual spaceof consumers

Though consumers may think about a number of attributes in evaluating products, it may be possible to summarize these attributes because consumer perceptions along these attributes may be correlated

We can use factor analysis to find this reduced perceptual spaceand map the products in this space

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Perceptual Map… Final Output

Perceptual Map for Cars

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Fashion

Economy Taurus

VW Golf

Camry

Dodge Neon

Lexus ES 300

BMW325

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Perceptual Maps: An Illustration Using the Car Market

Cars Considered

Ford TaurusToyota CamryVolkswagen GolfBMW 3-SeriesLexus ES300Dodge Neon

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Survey…

Each respondent is asked to rate 6 cars on a number of attributes on a 1-7 scale

AffordabilityPracticalityClassinessSportinessYouth AppealFun to Drive

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Data…

Respondent Car Afford Practical Classy Sporty Youthful Fun 1 Taurus 7 5 2 5 5 5 1 Neon 1 Camry 1 Lexus 1 BMW 1 VW 2 Taurus 2 Neon 20 Taurus 20 Neon 20 Camry 20 Lexus 20 BMW 20 VW

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Quick and Dirty Sense of the Data: Looking at the Correlation Matrix

Fair amount of correlations between variables

indicates that Factor Analysis may be useful

Afford Practical Class Sporty Youth App FunAfford 1Practical 0,013004 1Class -0,61826 -0,48435 1Sporty -0,34601 -0,77767 0,798962 1Youth App -0,0619 -0,72219 0,257668 0,636417 1Fun -0,17834 -0,73562 0,573691 0,852368 0,64312 1

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Simple Approach: Plot Attribute by Attribute

Practicality Vs Sportiness

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Practicality

Sportiness

Neon

BMW

Lexus

Taurus

Camry

Volkswagen

Can lead to too many maps

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First Step: Do Principal Component Analysis (PCA)

This allows us to select the # of factors

PCA uses the correlation matrix of the data and constructs factors

if there are n variables we will have n factorsfirst factor will explain most variance, second next and so on…

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Minitab Output of PCA: Eigen Analysis

Eigenanalysis of the Correlation Matrix

Eigenvalue 3.7336 1.3383 0.4641 0.2558 0.1514 0.0568Proportion 0.622 0.223 0.077 0.043 0.025 0.009Cumulative 0.622 0.845 0.923 0.965 0.991 1.000

84.5% of variance in 6 variables is explained by just 2 factors

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Minitab Output of PCA: Scree Plot

654321

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Component Number

Eig

enva

lue

Scree Plot of Afford-Fun

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Second Step: Do Factor Analysis

Perform factor analysis with the factors selected from Step 1

Interpret resulting factorsuse factor loadings and loading plot to interpret factorsif it is not interpretable use rotation options until we get something that can be interpreted

Look at factor equations and factor scoresscore plots will be useful

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Unrotated Factor Loadings: Variable’s Correlation with the Factors

Unrotated Factor Loadings and Communalities

Variable Factor1 Factor2 CommunalityAfford 0.376 -0.841 0.849Practica 0.849 0.367 0.855Class -0.774 0.529 0.879Sporty -0.965 0.050 0.934Youth Ap -0.740 -0.434 0.736Fun -0.890 -0.158 0.818

Variance 3.7336 1.3383 5.0719% Var 0.622 0.223 0.845

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Interpreting Factors: Looking at Loading Plot without Rotation

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First Factor

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Fac

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Loading Plot of Afford-Fun

Fun

Youth Ap

Sporty

Class

Practica

Afford

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Rotated Factor Loadings and CommunalitiesVarimax Rotation

Variable Factor1 Factor2 CommunalityAfford 0.063 0.919 0.849Practica -0.922 0.075 0.855Class 0.435 -0.831 0.879Sporty 0.829 -0.498 0.934Youth Ap 0.857 0.036 0.736Fun 0.860 -0.279 0.818

Variance 3.2045 1.8674 5.0719% Var 0.534 0.311 0.845

Rotated Factor Loadings: Variable’s Correlation with the Factors

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Interpreting Factors: Looking at Loading Plot with Rotation

Attribute-Factor Relationship

(Loading Plot)

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Factor 1

Factor 2

Affordability

Practicality

Classiness

Sportiness

Fun

Youth

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Naming Factors

Can we name these factors?

This highlights the subjectivity involved here

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How Did Cars Score on Fashion and Economy Factors?

Fashion = 0.206 Affordability - 0.330 Practicality - 0.003 Classiness + 0.211 Sportiveness + 0.327 Youthful Appeal + 0.266 Fun

Economy = 0.602 Affordability -0.135 Practicality -0.446 Classiness - 0.154 Sportiveness + 0.193 Youthful Appeal - 0.008 Fun

Factor Score Coefficients

Variable Factor1 Factor2Afford 0.206 0.602Practica -0.330 -0.135Class -0.003 -0.446Sporty 0.211 -0.154Youth Ap 0.327 0.193Fun 0.266 -0.008

We standardize the variables and then take the average of the 20 consumer’s ratings

on the standardized variable to plug in

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Alternative Approach To Compute Factor Scores for Each Car

Store Minitab’s factor scores for each car based on each consumer’s ratings

we will have 20*6=120 numbers

Average the factor scores across consumers for each carwe will get the factor score for each car

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Where are Cars Located in the Perceptual Space?

Perceptual Map for Cars

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Fashion

Economy

Taurus

Volkswagen

Camry

Neon

Lexus

BMW

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Applications of Perceptual Maps

Who are our competitors?

On what dimensions do we compete?

Where to introduce new products?you also need to be aware of consumer preferenceslook for locations with relatively more consumers but limited competition

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Caveats

Identify relevant attributesdon’t miss important attributes (Exploratory Research is important)no point asking about unimportant attributes conjoint analysis may be useful in identifying what attributes are important to consumers

Identify discriminating attributesdon’t use primary attributes (like cleaning power of detergents)there should be real perceptual differences on average for the product

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Step 1: Choose Number of Factors to Extract

Do Principal Component Analysis (PCA)

In Minitab select Stat>Multivariate>Principal Components…

Select the variables you want to factor analyze in Variables box

Select “Correlation” as the data that will be analyzed; this will mean that the data will be standardized and therefore each variable will have equal effect

Ask for Scree Plot (using Graphs button) which graphs the amount of variance explained by each factor

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Step 2: Perform Factor Analysis

Do Factor Analysis

in Minitab, Stat>Multivariate>Factor Analysis….

number of Factors to extract should be from Step 1

try “None” rotation for a start (else try Varimax or others if it doesn’t work)

In Graphs: select loading plot (score plot is not useful here)

In Storage: in the scores box store the factor scores by selecting 2 variables

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Step 3: Plot the Perceptual Map

Take the average of the factor scores for each car

Use these average scores to plot the perceptual map