Marketing Research

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Marketing Research

• Aaker, Kumar, Day
• Ninth Edition
• Instructors Presentation Slides

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Chapter Twenty-two
Multidimensional Scaling and 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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Approaches To Creating Perceptual Maps
Perceptual map
Attribute data
Nonattribute data
Preference
Similarity
Correspondence analysis
Discriminant analysis
Factor 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

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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'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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Perceptual Map of Pain Relievers
Gentleness
. Tylenol
. Bufferin
Effectiveness
. Bayer
. Advil
. Private-label aspirin
. Nuprin
. Anacin
. Excedrin
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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
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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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 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
• 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

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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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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

1. Choose product attributes (e.g. size, price,
   model)
2. Choose the values or options for each attribute
3. Define products as a combination of attribute
   options
4. A value of relative utility is assigned to each
   level of an attribute called partworth utilities
5. The combination with the highest utilities should
   be the one that is most preferred

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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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Full-profile and Trade-off Approaches
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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Part-worth Utilities
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Relative Importance of Attributes
Attribute Part-worth Utility Part-worth Utility Part-worth Utility Part-worth Utility Relative Importance
Make Make 1.2 9 9 9
Price Price 4.2 4.2 32 32
MPG MPG 5.2 5.2 39 39
Door Door 2.7 2.7 20 20
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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