multidimensional scaling & conjoint analysis

Multidimensional Scaling and Conjoint Analysis

By: Omer MaroofMBA: 3rd Sem…….Enroll: 110130

1

Marketing Research 10th Edition http://www.drvkumar.com/mr10/

Multidimensional ScalingUsed 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

2


3

Perceptual map

Attribute data Nonattribute data

Similarity Preference

Correspondence analysis

MDSDiscriminant analysis

Factor analysis

Approaches To Creating Perceptual Maps


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

4


Comparison of Factor and 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

• 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

5

Factor AnalysisDiscriminant Analysis


Perceptual Map of a Beverage Market

6


7

Perceptual Map of Pain RelieversGentleness

. Tylenol

Effectiveness. Bufferin

. Advil

. Nuprin

. Excedrin

. Private-label

aspirin

. Bayer

. Anacin


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

8


Determining Number of Dimensions

9

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


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

10

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


Application of MDS With Nonattribute DataSimilarity 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

11


Similarity Judgments

12


Perceptual Map Using Similarity Data

13


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

14


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

15


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

• 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

16


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

17


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

18


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

19


Steps in Conjoint Analysis

20


Utilities for Credit Card Attributes

21

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


Utilities for Credit Card Attributes (Contd.)

22


Full-profile and Trade-off Approaches

23

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


Conjoint Analysis - Example

Make Price MPG Door

0 Domestic $22,000 22 2-DR

1 Foreign $18,000 28 4-DR

24

24


Conjoint Analysis – Regression Output

25

Model Summaryc

.785b .616 .488 6.921Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: Door, MPG, Price, Makeb.

Dependent Variable: Rankc.

Coefficientsa,b

1.200 3.095 .088 .388 .705

4.200 3.095 .307 1.357 .200

5.200 3.095 .380 1.680 .119

2.700 3.095 .197 .872 .400

Make

Price

MPG

Door

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Ranka.

Linear Regression through the Originb.

ANOVAc

921.200 4 230.300 4.808 .015a

574.800 12 47.900

1496.000 16

Regression

Residual

Total

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: Door, MPG, Price, Makea.

Dependent Variable: Rankc.


Part-worth Utilities

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Foreign Domestic

Make

Uti

lity

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

18,000 22,000

Price

Uti

lity

0

1

2

3

4

5

6

28 22

MPG

Uti

lity

0

0.5

1

1.5

2

2.5

3

4-Dr 2-Dr

Door

Uti

lity

26

26


Relative Importance of Attributes

Attribute Part-worth Utility Relative Importance

Make 1.2 9%

Price 4.2 32%

MPG 5.2 39%

Door 2.7 20%

27

27


Limitations of Conjoint AnalysisTrade-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

28

multidimensional scaling & conjoint analysis

Leadership & Management

reducedmarketing research

anacinmarketing research

different objects

objects anddifferences

interpretation of dimensions

mds perceptual mapping

smallest number of dimensions

application of mds