Transcript Marketing Research

Marketing
Research
Aaker, Kumar, Day
Seventh Edition
Instructor’s Presentation
Slides
Chapter Twenty-Two
Multidimensional Scaling
and Conjoint Analysis
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
Marketing Research 7th Edition
© Aaker, Kumar, Day
Approaches to Create
Perceptual Maps
Attribute based approaches
Non attribute based approaches
Marketing Research 7th Edition
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Approaches To
Creating
Perceptual Maps
Perceptual map
Attribute
data
Nonattribute
data
Similarity
Factor
analysis
Correspondence
analysis
Marketing Research 7th Edition
Discriminant
analysis
Preference
MDS
© Aaker, Kumar, Day
Attribute Based Approaches
If MDS used on attribute data, it is known as
attribute based MDS
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
Marketing Research 7th Edition
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Basic Concepts of
Multidimensional
Scaling(MDS)
MDS uses proximities among different
objects as input (proximity is a value
which denotes how similar or how
different two objects, are perceived to
be)
MDS uses this proximities data to
produce a geometric configuration of
points (objects), in a two-dimensional
space as output
Marketing Research 7th Edition
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Evaluating the MDS Solution
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 the objects can be
obtained plotting the stress values against
the number of dimensions
Marketing Research 7th Edition
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Advantages of Attributebased MDS
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 of Attributebased MDS
If the list of attributes is not accurate and
complete, the study will suffer accordingly
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
The power of the technique lies in the ability 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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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 the
perceptual maps cannot be ascertained
The interpretation of dimensions is difficult
When more than two or three dimensions
are needed, the usefulness is reduced
Marketing Research 7th Edition
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Conjoint Analysis
An extremely powerful and useful analysis tool
Used to determine the relative importance of
various attributes to respondents, based on
their making trade-off judgments
Useful in

Helping to select features on a new product/service

Predicting sales

Understanding relationships
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Input
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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Output
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
And the combination with the lowest total
utility is the least preferred
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Limitations
In the trade-off approach, the task is too
unrealistic
Trade-off judgments is being made on two
attributes, holding the others constant
In the full-profile approach, the task can get
very demanding, if there are multiple
attributes and attribute levels
Marketing Research 7th Edition
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