Data Mining in CRM - Data Mining Techniques in CRM: Inside Customer Segmentation - page 4

The goal of such models is to uncover data patterns in the set of input fields.
Unsupervised models include:
• Cluster models: In these models the groups are not known in advance. Instead
we want the algorithms to analyze the input data patterns and identify the natural
groupings of records or cases. When new cases are scored by the generated
cluster model they are assigned to one of the revealed clusters.
• Association and sequence models: These models also belong to the class
of unsupervised modeling. They do not involve direct prediction of a single
field. In fact, all the fields involved have a double role, since they act as inputs
and outputs at the same time. Association models detect associations between
discrete events, products, or attributes. Sequence models detect associations
over time.
DATA MINING IN THE CRM FRAMEWORK
Datamining can provide customer insight, which is vital for establishing an effective
CRM strategy. It can lead to personalized interactions with customers and hence
increased satisfaction and profitable customer relationships through data analysis.
It can support an 'individualized' and optimized customer management throughout
all the phases of the customer lifecycle, from the acquisition and establishment of
a strong relationship to the prevention of attrition and the winning back of lost
customers. Marketers strive to get a greater market share and a greater share of
their customers. In plain words, they are responsible for getting, developing, and
keeping the customers. Data mining models can help in all these tasks, as shown
in Figure 1.1.
More specifically, the marketing activities that can be supported with the use
of data mining include the following topics.
Customer Segmentation
Segmentation is the process of dividing the customer base into distinct and
internally homogeneous groups in order to develop differentiated marketing
strategies according to their characteristics. There are many different segmentation
types based on the specific criteria or attributes used for segmentation.
In behavioral segmentation, customers are grouped by behavioral and usage
characteristics. Although behavioral segments can be created with business rules,
this approach has inherent disadvantages. It can efficiently handle only a few
segmentation fields and its objectivity is questionable as it is based on the personal
perceptions of a business expert. Data mining on the other hand can create