Marketing Research
Aaker, Kumar, Day
Ninth Edition
Instructor’s Presentation Slides
1
Chapter Twenty-two
Multidimensional Scaling and
Conjoint Analysis
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Multidimensional Scaling
Used to:
 Identify dimensions by which objects are perceived or
evaluated
 Position the objects with respect to those dimensions
 Make positioning decisions for new and old products
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Approaches To Creating Perceptual Maps
Perceptual map
Attribute data
Nonattribute
data
Similarity
Factor
analysis
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Correspondence
analysis
Discriminant
analysis
4
Preference
MDS
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Attribute Based Approaches
 Attribute based MDS - MDS used on attribute data
 Assumption
 The attributes on which the individuals' perceptions of objects are
based can be identified
 Methods used to reduce the attributes to a small number of dimensions
 Factor Analysis
 Discriminant Analysis
 Limitations
 Ignore the relative importance of particular attributes to customers
 Variables are assumed to be intervally scaled and continuous
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Comparison of Factor and Discriminant
Analysis
Factor Analysis
Discriminant Analysis
 Groups attributes that are
similar
 Identifies clusters of
attributes on which objects
differ
 Identifies a perceptual
dimension even if it is
represented by a single
attribute
 Based on both perceived
differences between objects
and differences between
people's perceptions of
objects
 Statistical test with null
hypothesis that two objects
are perceived identically
 Dimensions provide more
interpretive value than
discriminant analysis
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Perceptual Map of a Beverage Market
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Perceptual Map of Pain Relievers
Gentleness
. Tylenol
. Bufferin
Effectiveness
. Bayer
. Private-label
aspirin
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. Advil
. Nuprin
. Anacin
. Excedrin
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Basic Concepts of Multidimensional Scaling
(MDS)
 MDS uses proximities ( value which denotes how similar or how
different two objects are perceived to be) among different objects as
input
 Proximities data is used to produce a geometric configuration of
points (objects) in a two-dimensional space as output
 The fit between the derived distances and the two proximities in each
dimension is evaluated through a measure called stress
 The appropriate number of dimensions required to locate objects can
be obtained by plotting stress values against the number of
dimensions
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Determining Number of Dimensions
Due to large increase in the stress values from two dimensions to
one, two dimensions are acceptable
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Attribute-based MDS
Advantages
 Attributes can have diagnostic and operational value
 Attribute data is easier for the respondents to use
 Dimensions based on attribute data predicted preference
better as compared to non-attribute data
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Attribute-based MDS (contd.)
Disadvantages
 If the list of attributes is not accurate and complete, the study
will suffer
 Respondents may not perceive or evaluate objects in terms of
underlying attributes
 May require more dimensions to represent them than the use of
flexible models
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Application of MDS With Nonattribute Data
Similarity Data
 Reflect the perceived similarity of two objects from the
respondents' perspective
 Perceptual map is obtained from the average similarity ratings
 Able to find the smallest number of dimensions for which there is
a reasonably good fit between the input similarity rankings and
the rankings of the distance between objects in the resulting
space
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Similarity Judgments
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Perceptual Map Using Similarity Data
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Application of MDS With Nonattribute
Data (Contd.)
Preference Data
 An ideal object is the combination of all customers' preferred
attribute levels
 Location of ideal objects is to identify segments of customers
who have similar ideal objects, since customer preferences
are always heterogeneous
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Issues in MDS
 Perceptual mapping has not been shown to be reliable across
different methods
 The effect of market events on perceptual maps cannot be
ascertained
 The interpretation of dimensions is difficult
 When more than two or three dimensions are needed,
usefulness is reduced
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Conjoint Analysis
 Technique that allows a subset of the possible
combinations of product features to be used to determine
the relative importance of each feature in the purchase
decision
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Conjoint Analysis
 Used to determine the relative importance of various
attributes to respondents, based on their making trade-off
judgments
 Uses:
 To select features on a new product/service
 Predict sales
 Understand relationships
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Inputs in Conjoint Analysis
 The dependent variable is the preference judgment that a
respondent makes about a new concept
 The independent variables are the attribute levels that need to
be specified
 Respondents make judgments about the concept either by
considering
 Two attributes at a time - Trade-off approach
 Full profile of attributes - Full profile approach
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Outputs in Conjoint Analysis
 A value of relative utility is assigned to each level of an attribute
called partworth utilities
 The combination with the highest utilities should be the one that
is most preferred
 The combination with the lowest total utility is the least
preferred
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Applications of Conjoint Analysis
 Where the alternative products or services have a number
of attributes, each with two or more levels
 Where most of the feasible combinations of attribute levels
do not presently exist
 Where the range of possible attribute levels can be
expanded beyond those presently available
 Where the general direction of attribute preference
probably is known
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Steps in Conjoint Analysis
1.
Choose product attributes (e.g. size, price, model)
2.
Choose the values or options for each attribute
3.
Define products as a combination of attribute options
4.
A value of relative utility is assigned to each level of an attribute
called partworth utilities
5.
The combination with the highest utilities should be the one that
is most preferred
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Utilities for Credit Card Attributes
Source: Paul E. Green, ‘‘A New Approach to Market Segmentation,’’
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Utilities for Credit Card Attributes
(contd.)
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Full-profile and Trade-off Approaches
Source: Adapted from Dick Westwood, Tony Lunn, and David Bezaley, ‘‘The Trade-off Model and Its Extensions’’
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Conjoint Analysis - Example
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Make
Price
MPG
Door
0
Domestic
$22,000
22
2-DR
1
Foreign
$18,000
27
28
4-DR
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Conjoint Analysis – Regression Output
Model Summary
Model
1
R
Adjusted
R Square
R Square
.785 b
c
.616
Std. Error of
the Es timate
.488
6.921
b. Predictors: D oor, MPG, Price, Make
c. Dependent Variable: Rank
ANOVA c
Sum of
Squares
Model
1
df
Mean Square
Regress ion
921.200
4
230.300
Residual
574.800
12
47.900
1496.000
16
Total
F
Sig.
.015 a
4.808
a. Predictors: D oor, MPG, Price, Make
c. Dependent Variable: Rank
Coefficients
Unstandardized
Coefficients
Model
1
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B
Std. Error
a,b
Standardized
Coefficients
Beta
t
Sig.
Make
1.200
3.095
.088
.388
.705
Price
4.200
3.095
.307
1.357
.200
MPG
5.200
3.095
.380
1.680
.119
Door
2.700
3.095
.197
.872
.400
a. Dependent Variable: Rank
28
b. Linear Regression through the Origin
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Part-worth Utilities
1.4
4.5
1.2
4
3.5
3
Utility
Utility
1
0.8
0.6
2.5
2
1.5
0.4
1
0.5
0.2
0
0
Foreign
Domestic
18,000
Make
Price
3
6
2.5
5
2
Utility
4
Utility
22,000
3
1.5
1
2
0.5
1
0
0
28
4-Dr
22
Door
MPG
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2-Dr
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Relative Importance of Attributes
Attribute
Make
Part-worth Utility
1.2
Relative
Importance
9%
Price
4.2
32%
MPG
5.2
39%
Door
2.7
20%
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Limitations of Conjoint Analysis
Trade-off approach
 The task is too unrealistic
 Trade-off judgments are being made on two attributes,
holding the others constant
Full-profile approach
 If there are multiple attributes and attribute levels, the task
can get very demanding
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