Market Research Applications Lecture 6.Pptx
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Transcript of Market Research Applications Lecture 6.Pptx
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5/21/2018 Market Research Applications Lecture 6.Pptx
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Multidimensional Scaling andConjoint Analysis
http://images.google.com/imgres?imgurl=http://www.xlstat.com/imagesdemo/mds6.gif&imgrefurl=http://www.xlstat.com/demo-mds.htm&h=359&w=446&sz=11&hl=en&start=1&tbnid=llJVHk7DxTRLuM:&tbnh=99&tbnw=124&prev=/images%3Fq%3DMultidimensional%2Bscaling%26svnum%3D10%26hl%3Den%26lr%3D%26rls%3DRNWE,RNWE:2004-17,RNWE:en%26sa%3DN -
5/21/2018 Market Research Applications Lecture 6.Pptx
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Topics
Multi-dimensional Scaling
Perceptual Mapping
Discriminant Analysis 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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Perceptual map
Attribute data Nonattribute
data
Similarity Preference
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 interval scaled and continuous
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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
Factor Analysis
Groups attributes that are similar
Based on both perceived
differences between objects and
differences between people'sperceptions of objects
Dimensions provide more
interpretive value than
discriminant analysis
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Perceptual Map of Pain Relievers
Gentleness
.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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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 preferencebetter 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 ofunderlying 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 areasonably 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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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 eachfeature 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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Full-Profile Approach
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Trade-off Approach
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Outputs in Conjoint Analysis
A value of relative utility is assigned to each level of anattribute called part worth 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 numberof 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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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