Chapter14 Slides

7/31/2019 Chapter14 Slides

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Welcome to Powerpoint slidesfor

Chapter 14

Multidimensional Scaling

for

Brand Positioning

Marketing Research

Text and Cases

by

Rajendra Nargundkar



1. The most common and useful marketing applicationof multidimensional scaling is in brand positioning.

2. Positioning is essentially concerned with mapping aconsumer’s mind and placing all the competing brandsof a product category in appropriate slots or “positions” on it.

3. For example, a product category of shampoos couldbe identified as having 5 attributes important to the

consumer - price, lather, fragrance, consistency andfavorable effects on hair.

4. If these were to be rated on a 7 point scale for say,six leading brands of shampoo A, B, C, D, E and F, thenwe could pickup any two attributes and plot the six

brands on a map according to the consumer ratings.

5. This is called a perceptual map of consumerperception about competing brands in a productcategory. This is the type of map useful for deliberatepositioning of a new brand, based on "gaps" in the

current map, or for finding out the current position of an existing brand on the map. If the desired position of an existing brand owned by our company is differentfrom the one perceived by consumers, an option is to"reposition" the brand.

Slide 1



1. The above method may not capture the consumer’s mind accurately.

2. If we assume that the consumer simultaneously thinks

of several product dimensions or attributes rather thanone attribute at a time, the above method is only anapproximation of that process

3. Multidimensional scaling, on the other hand, capturesthe complex interactions between attributes and brands in

a particular way, and then “derives” attributes ordimensions which explain the “positions” given byconsumers to various brands.

4. There are two basic methods used in multidimensionalscaling-Attribute based approach, and

Similarity/Dissimilarity based approach

5. The attribute-based approach is similar to what wehave described in the previous section, except that theseinput data are then further analysed using either factoranalysis or discriminant analysis.

6. The second approach is very easy to understandintuitively, and quite useful in gaining a goodunderstanding of consumer psyche, so we will discussonly this (similarity and dissimilarity based) approach.

Slide 2



1. In the similarity/dissimilarity-based approach,

we need some kind of a distance measure betweenthe brands being rated. The distance measure being

input could be a simple ranking of distances

between a brand and all other brands by a

customer.

2. One way to do this is to provide a customer

(respondent) with cards, each containing a pair of

brands written on it, and asking him to write down

a number indicating the difference between the two

brands on any numerical scale which can represent

distance.

3. This is then repeated for all pairs of brands

being included in the research. No attributes are

specified by which the customer is asked to decide

on the difference.

4. This distance measure or dissimilarity measure

can be compiled into a matrix of the type shown in

Fig.1.

Slide 3



Fig.1

Var1 Var2 Var3 Var4 Var5 Var6 Var7 VaVar1 .0 3.0 6.0 8.0 1.0 2.0 7.0

Var2 3.0 .0 4.0 6.0 4.0 5.0 2.0

Var3 6.0 4.0 .0 3.0 2.0 4.0 6.0 1

Var4 8.0 6.0 3.0 .0 3.0 5.0 4.0

Var5 1.0 4.0 2.0 3.0 .0 2.0 8.0

Var6 2.0 5.0 4.0 5.0 2.0 .0 3.0

Var7 7.0 2.0 6.0 4.0 8.0 3.0 .0Var8 8.0 5.0 1.0 7.0 5.0 6.0 5.0

1. Fig. 1 takes the example of eight brands of TV

available in the Indian market. Both the rows and

columns represent brands of TV. Eg: Var. 1 is

brand 1, var. 2 is brand 2, and so on.2. Input data were collected from a sample of

respondents each of whom was asked to rate the

dissimilarity between all pairs of TV brands on a

numerical scale

3. We will use multidimensional scaling to

determine how these 8 brands are perceived byIndian consumers, and plot a positioning map of

the eight brands.

We will also attempt to find out how many

dimensions the consumers seem to be using, when

they think of TV brands.

Slide 4



1. In Figs. 2(a), 2(b), 3(a), 3(b), 4(a) and 4(b), we have

the SPSS outputs of multidimensional scaling on our

data.

2. Figs. 2(a) and 2(b) contain the 3-dimensional solution.

Figs 3(a) and 3(b) contain the 2- dimensional solution.

Figs. 4(a) and 4(b) contain the 1-dimensional solution.

3. Our first task is to determine how many dimensionsthe data seems to indicate (in which we feel the best

solution exists). For this, we look at the stress value for

various solutions in different dimensions. From Figs.

2(a), 3(a) and 4(a), we see the following values of stress.

3-dimensional solution : 0.05230

2-dimensional solution : 0.24015 1-dimensional solution : 0.43159

4. Clearly, the 1- dimensional solution is not a good one.

Remember, the stress value indicates lack of fit, so it

should be as close to zero as possible. The 2-

dimensional solution is better, but the 3-dimensional

solution looks the best, as the stress value is a low 0.05.

Slide 5



1. Let us assume we have decided that the 3-

dimensional solution is the best, based on the low

stress value.

2. Then, our next task now would be to name the

dimensions. For doing so, our previous knowledge

of the brands may become important. For example,

let us assume that the eight brands of TV were as

follows :-

1. Aiwa

2. Videocon

3. LG

4. Samsung

5. Sony

6. Onida

7. Thomson

8. BPL

Slide 6



Stimulu Stimulus 1 2 3 1 VAR00001 1.9512 .2028 .0664 2 VAR00002 -.1995 1.3140 .7743 3 VAR00003 -.6043 -1.3429 .4680 4 VAR00004 -.9038 -.2969 -1.8497 5 VAR00005 .8931 -1.0092 -.0350 6

VAR00006

1.1045

.1529

-.7070

7 VAR00007 -1.1031 1.6088 -.1289 8 VAR00008 -1.1381 -.6295 1.4121

If these had been the eight brands, then we look at

the qualities of various attributes offered by these

brands either through our judgment or knowledge

of the market or through a survey of consumers, or

a combination of these methods.

Fig. 2(b)Stimulus Coordinates for 3 dimensional solutio

For example, we could look at the above 3

dimensional solution of multidimensional scaling,

and the scores for the eight brands on the 3dimensions, and decide on the following names for

the 3 dimensions -

Slide 7



Dimension 1 : Value for MoneyDimension 2 : After Sales Service

Dimension 3 : Current Brand Image

We could then look at the brand scores (positions)

on the three dimensions and conclude that some

brands like BPL, and Videocon, currently enjoy agood brand image, but brands like Aiwa, Onida

and Thomson are leading in “Value for Money”

perceptions. Also, Videocon and Thomson may be

perceived as having the best after-sales service.

Slide 7 contd...



Slide 8

If we were to choose the 2-dimensional solution

instead of the 3-dimensional one, it could be plotted

on a graph and would be visually easier to interpret.

Just as an illustration, we will do it for this example.The plot of the 2-dimensional solution is shown in

fig. 5 and the brands can be seen to form distinct

clusters based on their perceived similarity.



Fig. 5MULTIDIMENSIONAL SCALING

2-DIMENSIONAL OUTPUT : 8 COLOUR TVS

BRANDS : 1 = AIWA 5 = SONY2 = VIDEOCON 6 = ONIDA3 = LG 7 = THOMSON

4 = SAMSUNG 8 = BPL

-1.5 -1.0 -0.5

0.5 1.0 1.5

1.5

1.0

0.5

-0.5

-1.0

DIMENSION

6

1

2

7

53

8

4

DIMENSION

Slide 8 contd...



For example, brands 1 and 6 are perceived to be

similar, whereas brand 5 is a standalone brand. So is

brand 3, to some extent. Here again, knowledge of

the brand names and their attributes or qualitieswould be used to name the two dimensions. Again,

dimension 1 could be value for money. Dimension 2

could be after-sales service. But notice that we are

losing some information on the third dimension

which we had called brand image in the 3-

dimensional solution. The loss of information mayturn out to be critical in some cases.

Slide 8 contd...



Slide 9Additional Comments

1. MDS can be performed even with a sample size of 1.

2. It can be used to get a composite picture of a segment'sperception, by combining the responses of any one segment,and repeating the MDS for each of the major segments.

3. It can also be done across all segments (a single MDS) byaggregating responses for the entire sample.

4. If we have a significant marketing decision hinging on theresults, the author recommends that approaches 2 and 3(segment wise and across segments) both be used and if thereare significant differences, try and see if the positioning needsto be different for different segments. That may indeed be thecase, in these days of diversity of consumer preferences.

5. It would be tempting to do one MDS for each respondent,but the analysis would remain meaningless unless there aresufficient numbers of each consumer type which meansdetermining the segments after the MDS. This is a possibility,but would involve a lot of work in the analysis stage.

6. It is best left to the judgment of the researcher whichapproach he would like to follow.

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