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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

Marketing Research

Aaker, Kumar, Day

Ninth Edition

Instructor’s Presentation Slides

chapter twenty two
Chapter Twenty-two

Multidimensional Scaling and Conjoint Analysis

multidimensional scaling
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


Approaches To Creating Perceptual Maps

Perceptual map

Attribute data

Nonattribute data



Factor analysis

Correspondence analysis

Discriminant analysis


attribute based approaches
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

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

Comparison of Factor and Discriminant Analysis

perceptual map of a beverage market
Perceptual Map of a Beverage Market


Perceptual Map of Pain Relievers


. Tylenol

. Bufferin


. Bayer

. Private-label


. Advil

. Nuprin

. Anacin

. Excedrin

basic concepts of multidimensional scaling mds
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

determining number of dimensions
Determining Number of Dimensions

Due to large increase in the stress values from two dimensions to one, two dimensions are acceptable

attribute based mds
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

attribute based mds contd
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

application of mds with nonattribute data
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

similarity judgments
Similarity Judgments

perceptual map using similarity data
Perceptual Map Using Similarity Data

application of mds with nonattribute data contd
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

issues in mds
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

conjoint analysis
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

conjoint analysis19
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

inputs in conjoint analysis
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

outputs in conjoint analysis
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

applications of conjoint analysis
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

steps in conjoint analysis
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

utilities for credit card attributes
Utilities for Credit Card Attributes

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

full profile and trade off approaches
Full-profile and Trade-off Approaches

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

conjoint analysis example
Conjoint Analysis - Example

conjoint analysis regression output
Conjoint Analysis – Regression Output

part worth utilities
Part-worth Utilities

relative importance of attributes
Relative Importance of Attributes

limitations of conjoint analysis
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