Lecture Marketing research (12th edition) - Chapter 21: Multidimensional scaling and conjoint analysis
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Marketing Research
Aaker, Kumar,
Leone and Day
Twelfth Edition
Instructor’s
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Chapter TwentyOne
Multidimensional Scaling and
Conjoint Analysis
Marketing Research 12th Edition
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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
Preference
Similarity
Factor
analysis
Correspondence
analysis
Discriminant
analysis
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
Marketing Research 12th Edition
Comparison of Factor
and Discriminant
Analysis
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
•
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Factor Analysis
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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Perceptual Map of a Beverage
Market
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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 twodimensional 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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Attributebased MDS
Advantages
•
•
Attributes can have diagnostic
and operational value
Disadvantages
•
Attribute data is easier for the
respondents to use
•
•
Dimensions based on attribute
data predicted preference better
as compared to nonattribute
data
•
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
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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
Used to determine the relative importance of various attributes to
respondents, based on their making tradeoff 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 Tradeoff 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
Source: Paul E. Green, ‘‘A New Approach to Market Segmentation,’’
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Utilities for Credit Card Attributes (Contd.)
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Fullprofile and Tradeoff
Approaches
Source: Adapted from Dick Westwood, Tony Lunn, and David Bezaley, ‘‘The Tradeoff Model and Its Extensions’’
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Limitations of Conjoint Analysis
Tradeoff approach
•
•
The task is too unrealistic
Tradeoff judgments are being made on two attributes,
holding the others constant
Fullprofile approach
•
If there are multiple attributes and attribute levels, the task
can get very demanding
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End of Chapter TwentyOne
Marketing Research 12th Edition
