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Marketing Data Analysis
Rian BEISE-ZEE
From Marketing Metrics, 2nd Ed. by Paul W. Farris, et. al.
(ISBN: 0321750403) Copyright © 2012 Pearson Education, Inc. All rights reserved.
Perceptual Mapping
Positioning
… is establishing a specific customer perception about an existing product or brand.
Positioning
• Happens in the mind of the customers.
• We can position the same product differently.
• Defined relative to competitors, therefore can
keep a product/service “different” from competitors
products in the mind of the customer even if it is
physically the same
Positioning Principles
Same Product - Different Perception
Mouthwash
Perceptual Mapping
(or: Multidimensional Scaling)
• We can think of a variety of product attributes that possibly differentiate a product; however, consumers are unlikely to keep many attributes in mind.
• We can position a brand, but we don’t know if consumers are really “buying it”.
1. Perceptual mapping is a tool to measure what dimensions consumers use to differentiate products.
2. For marketers two or three dimensions allow a graphic representation, which makes strategy building a lot easier (but it does not mean that the world is really that simple).
Perceptual Map
The outcome of a multidimensional scale is
called ‘perceptual map’, a visual representation
which helps to derive strategic conclusions
about:
• What main dimensions are used by consumers to compare
Brands? (but not its importance!)
• Who are our competitors and how similar are they to our
Brand?
• What product clusters do consumers perceive?
• Are there holes in the market that are not yet covered?
Example: Detergent
Methods
• There are several methods
– Attribute-based: we start with many attributes and
try to reduce them to 2 or 3 main dimensions (by
means of factor analysis or discriminant analysis)
– Comparison-based: We start with comparisons
between two brands and end up with interpreting a
distance field
Attribute based MDS
1 2 3 4 5
Heavy
Action
Good Value
Budget
Relaxation
Popular with women
Sporty
Popular with men
Premium
Special Occasion
Sex Appeal
Rate each beer brand‘s association
Not
descriptive
at all
Totally
descriptive
From Moore and Pessemier (1993).
Comparison-based MDS
Beer Chang vs. Singha Beer
Totally Extremely
similar dissimilar
--- --- --- --- --- --- ---
1 2 3 4 5 6 7
Expl: Similarity ratings of 4 Objects
(for example products or companies)
A B C D
A
B 3.2
C 1.7 3.9
D 5.1 3.3 4.7
A Two Dimensional Solution of Empirical
Distances
Dimensionality
• How many dimensions a perceptual map has depends on the complexity of the similarity data. Often, two dimensions are sufficient. More than three are difficult to interpret. But one dimension is also quite possible.
• Two dimensions are often selected because it looks better.
• The correct method to decide on the number of dimensions is the stress value and the scree plot.
Labeling the axes
(= interpreting the dimensions)
• MDS only delivers the map but no clue about how to
interpret the dimensions
• Based on individual judgment of the researcher, e.g.
by
– known objective attributes
– Based on additional questions on the Brands’ attributes
• Based on objective vector algorithms with additional
attribute information
Interpreting the Dimensions:
Soft Drinks Example
Perceptual Map for Beer in Thailand
Interpreting the Dimensions
Problems with MDS
• Different methods lead to different maps (use several and analyze the differences)
• Different consumer groups have different perceptual maps (e.g. users/non-users) but MDS assumes homogeneity
• Different users consider different attributes and attach different importance to attributes
• Less than 4 objects per dimensions lead to misleading solutions but a large number of comparisons often overexert respondents.
Correspondence Analysis
• Another method to derive a perceptual map
based on the relationship between objects
(brands, products) and nominal attributes
(gender, age group, lifestyle)
• A perceptual map can be derived from any
cross-tabulation (frequency count)
Expl. Cross-tabulation
Product Sales
A B C Total Young adults (>35)
20 20 20 60
Middle age (36-55)
40 10 40 90
Mature (55+)
20 10 40 70
Total
80 40 100 220
Process of Correspondence Analysis
• Expected cell count under independence = (column total/total * Row total/total)*total
• Chi-quare value for each cell = actual cell count – expected cell count
expected cell count
• Chi-sqare is used as a measure of association
(similarity) between row and column :
– The greater chi-square the higher the similarity
– The more negative the higher the dissimilarity
Derived Perceptual Map
The Ford Ka
Which of these cars is more similar to the
Ford Ka?
VW Polo
VW Polo Ad
VW Polo Ad
Renault Twingo Ads
Toyota Rav4
Fiat 500 (Cinquecento)
Opel Tigra
One Dimension Solution of all Respondents
Object Points
Common Space
Dimension 1
1.51.0.50.0-.5-1.0
FIAT500 MICRA P106POLO FIESTACORSA TWINGO RAV4 KA TIGRA
Two dimension solution of all respondents
Object Points
Common Space
Dimension 1
1.51.0.50.0-.5-1.0
Dim
en
sio
n 2
.6
.4
.2
-.0
-.2
-.4
-.6
FIAT500
MICRA
P106
POLO
FIESTA
CORSA
TWINGO
RAV4
KA
TIGRA
Solution for Respondents with Ka
Preference
Object Points
Common Space
Dimension 1
1.51.0.50.0-.5-1.0
Dim
en
sio
n 2
.6
.4
.2
0.0
-.2
-.4FIAT500
MICRA
P106
POLO
FIESTA
CORSA
TWINGO
RAV4
KA
TIGRA
Solution for Respondents with low Ka-liking
Object Points
Common Space
Dimension 1
1.51.0.50.0-.5-1.0
Dim
en
sio
n 2
.8
.6
.4
.2
-.0
-.2
-.4
-.6
FIAT500MICRA
P106POLO
FIESTA
CORSA
TWINGO
RAV4
KA
TIGRA