An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 21

In this naive example, the identification of potential buyers could also be
done with inspection by eye. But imagine a situation with hundreds of candidate
predictors and tens of thousands of records or customers. Such complicated but
realistic tasks which human brains cannot handle can be easily and effectively
carried out by data mining algorithms.
What If There Is Not an Explicit Target Field to Predict?
In some cases there is no apparent categorical target field to predict. For
example, in the case of prepaid customers in mobile telephony, there is
no recorded disconnection event to be modeled. The separation between
active and churned customers is not evident. In such cases a target event
could be defined with respect to specific customer behavior. This handling
requires careful data exploration and co-operation between the data miners
and the marketers. For instance, prepaid customers with no incoming or
outgoing phone usage within a certain time period could be considered as
churners. In a similar manner, certain behaviors or changes in behavior,
for instance a substantial decrease in usage or a long period of inactivity,
could be identified as signals of specific events and then used for the def-
inition of the respective target. Moreover, the same approach could also
be followed when analysts want to act proactively. For instance, even when
a churn/disconnection event could be directly identified through a cus-
tomer's action, a proactive approach would analyze and model customers
before their typical attrition, trying to identify any early signals of defec-
tion and not waiting for official termination of the relationship with the
customer.
At the heart of all classification models is the estimation of confidence scores.
These are scores that denote the likelihood of the predicted outcome. They are
estimates of the probability of occurrence of the respective event. The predictions
generated by the classification models are based on these scores: a record is
classified into the class with the largest estimated confidence. The scores are
expressed on a continuous numeric scale and usually range from 0 to 1. Confidence
scores are typically translated to propensity scores which signify the likelihood of a
particular outcome: the propensity of a customer to churn, to buy a specific add-on
product, or to default on a loan. Propensity scores allow for the rank ordering
of customers according to the likelihood of an outcome. This feature enables
marketers to tailor the size of their campaigns according to their resources and
marketing objectives. They can expand or reduce their target lists on the basis