An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 22

of their particular objectives, always targeting those customers with the relatively
higher probabilities.
The purpose of all classification models is to provide insight and help in the
refinement and optimization of marketing applications. The first step after model
training is to browse the generated results, which may come in different forms
according to the model used: rules, equations, graphs. Knowledge extraction is
followed by evaluation of the model's predictive efficiency and by the deployment
of the results in order to classify new records according to the model's findings.
The whole procedure is described in Figure 2.3, which is explained further
below.
The following modeling techniques are included in the class of classification
models:
• Decision trees: Decision trees operate by recursively splitting the initial
population. For each split they automatically select themost significant predictor,
the predictor that yields the best separation with respect to the target field.
Through successive partitions, their goal is to produce ''pure'' sub-segments,
with homogeneous behavior in terms of the output. They are perhaps the
most popular classification technique. Part of their popularity is because they
produce transparent results that are easily interpretable, offering an insight
into the event under study. The produced results can have two equivalent
formats. In a rule format, results are represented in plain English as ordinary
rules:
IF (PREDICTOR VALUES) THEN (TARGET OUTCOME AND CONFIDENCE SCORE).
For example:
IF (Gender=Male and Profession=White Collar and SMS_Usage > 60
messages per month) THEN Prediction=Buyer and Confidence=0.95.
In a tree format, rules are graphically represented as a tree in which the
initial population (root node) is successively partitioned into terminal nodes or
leaves of sub-segments with similar behavior in regard to the target field.
Decision tree algorithms provide speed and scalability. Available algorithms
include:
-C5.0
-CHAID
- Classification and Regression Trees
- QUEST.