multidimensional scaling & conjoint analysis
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this presentation is about Multidimensional Scaling & Conjoint analysisTRANSCRIPT
Multidimensional Scaling and Conjoint Analysis
By: Omer MaroofMBA: 3rd Sem…….Enroll: 110130
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Marketing Research 10th Edition http://www.drvkumar.com/mr10/
Multidimensional ScalingUsed 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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Perceptual map
Attribute data Nonattribute data
Similarity Preference
Correspondence analysis
MDSDiscriminant analysis
Factor analysis
Approaches To Creating Perceptual Maps
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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
• 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
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Factor AnalysisDiscriminant Analysis
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Perceptual Map of a Beverage Market
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Perceptual Map of Pain RelieversGentleness
. Tylenol
Effectiveness. Bufferin
. Advil
. Nuprin
. Excedrin
. Private-label
aspirin
. Bayer
. Anacin
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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
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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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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 DataSimilarity 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
• 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
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Utilities for Credit Card Attributes
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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
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Source: Adapted from Dick Westwood, Tony Lunn, and David Bezaley, ‘‘The Trade-off Model and Its Extensions’’
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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
.785b .616 .488 6.921Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: Door, MPG, Price, Makeb.
Dependent Variable: Rankc.
Coefficientsa,b
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
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: Ranka.
Linear Regression through the Originb.
ANOVAc
921.200 4 230.300 4.808 .015a
574.800 12 47.900
1496.000 16
Regression
Residual
Total
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: Door, MPG, Price, Makea.
Dependent Variable: Rankc.
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Part-worth Utilities
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Foreign Domestic
Make
Uti
lity
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
18,000 22,000
Price
Uti
lity
0
1
2
3
4
5
6
28 22
MPG
Uti
lity
0
0.5
1
1.5
2
2.5
3
4-Dr 2-Dr
Door
Uti
lity
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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%
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Limitations of Conjoint AnalysisTrade-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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