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Figure 2.1 Graphical representation of supervised modeling.
PREDICTING EVENTS WITH CLASSIFICATION MODELING
As described above, classification models predict categorical outcomes by using a
set of input fields and a historical dataset with pre-classified data. Generatedmodels
are then used to predict the occurrence of events and classify unseen records. The
general idea of a classification models is described in the next, simplified example.
A mobile telephony network operator wants to conduct an outbound cross-
selling campaign to promote an Internet service to its customers. In order to
optimize the campaign results, the organization is going to offer the incentive of a
reduced service cost for the first months of usage. Instead of addressing the offer
to the entire customer base, the company decided to target only prospects with
an increased likelihood of acceptance. Therefore it used data mining in order to
reveal the matching customer profile and identify the right prospects. The company
decided to run a test campaign in a random sample of its existing customers which
currently were not using the Internet service. The campaign's recorded results
define the output field. The input fields include all the customer demographics
and usage attributes which already reside in the organization's data mart.
Input and output fields are joined into a single dataset for the purposes of
model building. The final form of the modeling dataset, for eight imaginary cus-
tomers and an indicative list of inputs (gender, occupation category, volume/traffic
of voice and SMS usage), is shown in Table 2.1.
The classification procedure is depicted in Figure 2.2.
The data are then mined with a classification model. Specific customer profiles
are associated with acceptance of the offer. In this simple, illustrative example,
none of the two contacted women accepted the offer. On the other hand, two out of
the five contacted men (40%) were positive toward the offer. Among white-collar
men this percentage reaches 67% (two out of three). Additionally, all white-collar
men with heavy SMS usage turned out to be interested in the Internet service.
These customers comprise the service's target group. Although oversimplified, the
described process shows the way that classification algorithms work. They analyze
predictor fields and map input data patterns with specific outcomes.
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