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
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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
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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
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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.
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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.
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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
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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
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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
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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...
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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.
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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
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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.
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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.