Customer Segmentation - Data Mining Techniques in CRM: Inside Customer Segmentation - page 209

1. ''Technical'' evaluation of the clustering solution: The internal cohesion
and separation of the clusters should be assessed with the use of descriptive
statistics and specialized technical measures (standard deviations, interclass and
intraclass distances, silhouette coefficient, etc.) such as the ones presented in the
previous chapter. Additionally, data miners should also examine the distribution
of customers in the revealed clusters as well as consistency of the results in
different datasets. All these tests assess the segmentation solution in terms of
''technical'' adequacy. Additionally, the segments should also be assessed from a
business perspective in terms of actionability and potential benefits. To facilitate
this evaluation, a thorough profiling of the segments' characteristics is needed.
2. Profiling of the revealed segments: A profiling phase is typically needed
in order to fully interpret the revealed segments and gain insight into their
structure and defining characteristics. Profiling supports the business evaluation
of the segments as well as the subsequent development of effective marketing
strategies tailored for each segment.
Segments should be profiled by using all available fields as well as external
information. The description of the extracted segments typically starts with
the examination of the centroids table. The centroid of each cluster is a vector
defined by the means of its member cases on the clustering fields. It represents
the segment's central point, the most representative case of the segment.
The profiling phase also includes the use of simple reporting and visualization
techniques for investigating and comparing the structures of the segments.
All fields of interest, even those not used in the formation of the segments,
should be cross-examined with them to gain deeper insight into their meaning.
However, analysts should always look cautiously at the demographics of the
derived segments. In many cases the demographic information may have not
been updated since the customer's first registration. Analysts should also bear
in mind that quite often the person using the service (credit card, mobile
phone, etc.) may not be the same one registered as a customer.
3. Cluster profiling with supervised (classification) models: Classification
models can augment reporting and visualization tools in the profiling of the
segments. The model should be built with the segment assignment field as the
target and the profiling fields of interest as inputs. Decision trees in particular,
due to the intuitive format of their results, are typically used to outline the
segment profiles.
4. Usingmarketing research information to evaluate and enrich the behav-
ioral segments: Marketing research surveys are typically used to investigate
the needs, preferences, opinions, lifestyles, perceptions, and attitudes of the
customers. They are also commonly used in order to collect valid and updated
demographic information. It is strongly recommended that the data mining-
driven behavioral segments are combined with the market research-driven