An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 40

Figure 2.9 Graphical representation of unsupervised modeling.
Below, we will briefly present all these techniques before focusing on the
clustering and data reduction techniques used mainly for segmentation purposes.
The different uses of supervised modeling techniques are depicted in
Figure 2.9.
SEGMENTING CUSTOMERS WITH CLUSTERING TECHNIQUES
Consider the situation of a social gathering where guests start to arrive and mingle
with each other. After a while, guests start to mix in company and groups of
socializing people start to appear. These groups are formed according to the
similarities of their members. People walk around and join groups according
to specific criteria such as physical appearance, dress code, topic and tone of
discussion, or past acquaintance. Although the host of the event may have had
some initial presumptions about who would match with whom, chances are that at
the end of the night some quite unexpected groupings would come up.
Grouping according to proximity or similarity is the key concept of clustering.
Clustering techniques reveal natural groupings of ''similar'' records. In the small
stores of old, when shop owners knew their customers by name, they could handle
all clients on an individual basis according to their preferences and purchase habits.
Nowadays, with thousands or even millions of customers, this is not feasible. What
is feasible, though, is to uncover the different customer types and identify their
distinct profiles. This constitutes a large step on the road from mass marketing
to a more individualized handling of customers. Customers are different in terms
of behavior, usage, needs, and attitudes and their treatment should be tailored to
their differentiating characteristics. Clustering techniques attempt to do exactly
that: identify distinct customer typologies and segment the customer base into
groups of similar profiles so that they can be marketed more effectively.
These techniques automatically detect the underlying customer groups based
on an input set of fields/attributes. Clusters are not known in advance. They are