We started off the
class in a pretty nervous fashion as the presentations were being taken. Four
groups presented in random order each presenting their understanding of the K –
means and Hierarchical clustering techniques.
Perceptual Mapping
Perceptual mapping offers
a unique ability to communicate the complex relationships between marketplace
competitors and the criteria used by buyers in making purchase decisions and
recommendations. Perceptual maps may be used for market segmentation, concept
development and evaluation, and tracking changes in marketplace perceptions
among other uses. Perceptual mapping involves two steps: (1) data collection and (2) data analysis and
presentation.
The method of data
collection can be executed in two ways:
Similarity Based
method
Attribute Based
method
Similarity based method
An example for
perceptual map using similarity based method was done with the file “Inter”.
Here what we did was we grouped the services together based on the similarities
exhibited by them to the customers. This gives us an analytical approach and
devises the formulation of further strategies as well.
An advantage of this
type can be that the individual is required to give their overall perception
without defining the attribute used by them for evaluation.
On the other hand a
limitation of this can be that we face difficulty in deciphering as to what
attributes are being used by the respondent and we can only make use of general
guidelines.
Attribute based method
As it is mentioned in
the name itself, this method works on the perception of consumers regarding
various attributes of a particular product. Thus it is a more clear way of
going about data collection. The example considered in class had stores as
products and various satisfaction levels as attributes which were grade on a
scale of 1 to 5. These when mapped on the permap we get:
How to interpret the above map:
·
The arrow
indicates the direction in which that attribute is increasing.
·
Length of the
line from the origin to the arrow is an indicator of the variance of that
attribute explained by the 2D map. The
longer this line, the greater is the importance of that attribute in explaining
variance.
·
Attribute that
are both relatively important (i.e., long vector) and close to the horizontal
(vertical) axis help interpret the meaning of axis.
The advantage of this
type of perceptual mapping is mainly that explicit description of the
dimensions can be mapped as the attributes given are very specific. A more
important benefit is that it enables representation of more than one brand and
attributes on a single map which is very useful in competitive analysis.
Trilochan Pariyar
Team C
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