market research applications lecture 6.pptx

5/21/2018 Market Research Applications Lecture 6.Pptx

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Multidimensional Scaling andConjoint Analysis
http://images.google.com/imgres?imgurl=http://www.xlstat.com/imagesdemo/mds6.gif&imgrefurl=http://www.xlstat.com/demo-mds.htm&h=359&w=446&sz=11&hl=en&start=1&tbnid=llJVHk7DxTRLuM:&tbnh=99&tbnw=124&prev=/images%3Fq%3DMultidimensional%2Bscaling%26svnum%3D10%26hl%3Den%26lr%3D%26rls%3DRNWE,RNWE:2004-17,RNWE:en%26sa%3DN


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Topics

Multi-dimensional Scaling

Perceptual Mapping

Discriminant Analysis 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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Perceptual map

Attribute data Nonattribute

data

Similarity Preference

MDSDiscriminant

analysis

Factor

analysis

Approaches To Creating Perceptual Maps


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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 interval 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'sperceptions of objects

Dimensions provide more

interpretive value than

discriminant analysis

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Perceptual Map of Pain Relievers

Gentleness

.Tylenol

Effectiveness. Bufferin

. Advil. Nuprin

. Excedrin

. Private-label

aspirin

. Bayer

. Anacin


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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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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 preferencebetter 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 ofunderlying 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 areasonably 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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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 eachfeature 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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Full-Profile Approach


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Trade-off Approach


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Outputs in Conjoint Analysis

A value of relative utility is assigned to each level of anattribute called part worth 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 numberof 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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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

market research applications lecture 6.pptx

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