An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 63

models can support many important marketing applications that are related to
the prediction of events, such as customer acquisition, cross/up/deep selling, and
churn prevention. These models estimate event scores or propensities for all the
examined customers, which enable marketers to efficiently target their subsequent
campaigns and prioritize their actions. Estimation models, on the other hand, aim
at estimating the values of continuous target fields. Supervised models require
a thorough evaluation of their performance before deployment. There are many
evaluation tools and methods which mainly include the cross-examination of the
model's predicted results with the observed actual values of the target field.
In Table 2.15 we present a list summarizing the supervised modeling tech-
niques, in the fields of classification and estimation. The table is not meant to
be exhaustive but rather an indicative listing which presents some of the most
well-known and established algorithms.
While supervised models aim at prediction, unsupervised models are mainly
used for grouping records or fields and for the detection of events or attributes
that occur together. Data reduction techniques are used to narrow the data's
dimensions, especially in the case of wide datasets with correlated inputs. They
identify related sets of original fields and derive compound measures that can
effectively replace them in subsequent tasks. They simplify subsequent modeling
or reporting jobs without sacrificing much of the information contained in the
initial list of fields.

Table 2.15 Supervised modeling techniques.

Classification techniques Estimation/regression techniques

• Logistic regression
• Decision trees:

• Ordinary least squares regression
• Neural networks
• Decision trees:

• C5.0
•CHAID
• Classification and Regression Trees
• QUEST

•CHAID
• Classification and Regression Trees

• Support vector machine
• Generalized linear models

• Decision rules:

• C5.0
• Decision list

• Discriminant analysis
• Neural networks
• Support vector machine
• Bayesian networks