Marketing Research

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Marketing Research

• Aaker, Kumar, Day
• Eighth 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 Create Perceptual Maps

• Attribute based approaches
• Non attribute based approaches

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Perceptual map
Approaches To Creating Perceptual Maps
Attribute data
Nonattribute data
Similarity
Preference
Correspondence analysis
Discriminant analysis
Factor analysis
MDS
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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

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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

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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

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Advantages of Attribute-based 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 Attribute-based 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

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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