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Marketing Research. Aaker, Kumar, Day Ninth 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

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### Marketing Research

Aaker, Kumar, Day

Ninth Edition

Instructor’s Presentation Slides

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

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

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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

Comparison of Factor and 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

. 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

• 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.)

• 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
• Choose product attributes (e.g. size, price, model)
• Choose the values or options for each attribute
• Define products as a combination of attribute options
• 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

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Utilities for Credit Card Attributes

Source:Paul E. Green, ‘‘A New Approach to Market Segmentation,’’

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Source:Adapted from Dick Westwood, Tony Lunn, and David Bezaley, ‘‘The Trade-off Model and Its Extensions’’

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Conjoint Analysis - Example

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Conjoint Analysis – Regression Output

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Part-worth Utilities

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Relative Importance of Attributes

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Limitations of Conjoint Analysis