Multidimensional scaling & Conjoint Analysis

Multidimensional Scaling and
Conjoint Analysis
By: Omer Maroof
MBA: 3rd Sem…….
Enroll: 110130
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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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Marketing Research http://www.drvkumar.com/mr10/ 10th Edition

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Approaches To Creating Perceptual Maps
Perceptual map
Attribute data Nonattribute data
Similarity Preference
Correspondence
analysis
Discriminant MDS
analysis
Factor
analysis

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
Discriminant Analysis Factor Analysis
• Identifies clusters of attributes
on which objects differ
• Identifies a perceptual
dimension even if it is
represented by a single attribute
• Statistical test with null
hypothesis that two objects are
perceived identically
• Groups attributes that are
similar
• Based on both perceived
differences between objects and
differences between people's
perceptions of objects
• Dimensions provide more
interpretive value than
discriminant analysis
5

Perceptual Map of a Beverage Market
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Perceptual Map of Pain Relievers
Gentleness
. Tylenol
. Bufferin Effectiveness
. Advil
. Nuprin
. Excedrin
. Private-label
aspirin
. Bayer
. Anacin

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
9
Due to large increase in the stress values from two dimensions to one,
two dimensions are acceptable

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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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

Application of MDS With Nonattribute Data
Similarity Data
11
• 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

Similarity Judgments
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Perceptual Map Using Similarity Data
13

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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

Issues in MDS
• Perceptual mapping has not been shown to be reliable
across different methods
15
• 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

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
• 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
16

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
17
• Respondents make judgments about the concept either by
considering
▫ Two attributes at a time - Trade-off approach
▫ Full profile of attributes - Full profile approach

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
18

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
19

Steps in Conjoint Analysis
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Utilities for Credit Card Attributes
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Source: Paul E. Green, ‘‘A New Approach to Market Segmentation,’’

Utilities for Credit Card Attributes (Contd.)
22

Full-profile and Trade-off Approaches
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Source: Adapted from Dick Westwood, Tony Lunn, and David Bezaley, ‘‘The Trade-off Model and Its Extensions’’

Conjoint Analysis - Example
Make Price MPG Door
0 Domestic $22,000 22 2-DR
1 Foreign $18,000 28 4-DR
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Conjoint Analysis – Regression Output
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Model Summaryc
R R Square
Adjusted
R Square
Std. Error of
the Estimate
.785b .616 .488 6.921
Model
1
b. Predictors: Door, MPG, Price, Make
c. Dependent Variable: Rank
Model
1
a. Predictors: Door, MPG, Price, Make
c. Dependent Variable: Rank
Coefficientsa,b
Unstandardized
Coefficients
B Std. Error
Regression
Residual
Total
Standardized
Coefficients
Beta
Sum of
Squares df Mean Square F Sig.
921.200 4 230.300 4.808 .015a
574.800 12 47.900
1496.000 16
t Sig.
1.200 3.095 .088 .388 .705
4.200 3.095 .307 1.357 .200
5.200 3.095 .380 1.680 .119
2.700 3.095 .197 .872 .400
Make
Price
MPG
Door
Model
1
a. Dependent Variable: Rank
b. Linear Regression through the Origin
ANOVAc

Part-worth Utilities
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Foreign Domestic
Make
Utility
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
18,000 22,000
Price
Utility
6
5
4
3
2
1
0
28 22
MPG
Utility
3
2.5
2
1.5
1
0.5
0
4-Dr 2-Dr
Door
Utility
26
26

Relative Importance of Attributes
Attribute Part-worth Utility Relative
Importance
Make 1.2 9%
Price 4.2 32%
MPG 5.2 39%
Door 2.7 20%
27
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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
28

Multidimensional scaling & Conjoint Analysis

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