An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 35

The model is trained on a six-month historical dataset. The methodological
approach is outlined by the following points:
• The input fields used cover all aspects of the customer relationship with
the organization: customer and contract characteristics, usage and behavioral
indicators, and so on, providing an integrated customer view also referred to as
the customer signature.
• The model is trained on customers who were active at the end of the historical
period (end of the six-month period). These customers comprise the training
population.
• A three-months period is used for the definition of the target event and the
target population.
• The target population consists of those who have voluntary churned (applied for
disconnection) by the end of the three-month period.
• The model is trained by identifying the input data patterns (customer charac-
teristics) associated with voluntary churn.
• The generated model is validated on a disjoint dataset of a different time period,
before being deployed for scoring presently active customers.
• In the deployment or scoring phase, presently active customers are scored
according to the model and churn propensities are generated. The model
predicts churn three months ahead.
• The generated churn propensities can then be used for better targeting of an
outbound retention campaign. The churn model results can be combined and
cross-examined with the present or potential value of the customers so that the
retention activities are prioritized accordingly.
• All input data fields that were used for the model training are required, obviously
with refreshed information, in order to update the churn propensities.
• Twomonths have been reserved to allow for scoring and preparing the campaign.
These two months are shown as gray boxes in the figure and are usually referred
to as the latency period.
• A latency period also ensures that the model is not trained to identify ''imme-
diate'' churners. Even if we manage to identify those customers, the chances
are that by the time they are contacted, they could already be gone or it will
be too late to change their minds. The goal of the model should be long term:
the recognition of early churn signals and the identification of customers with
an increased likelihood to churn in the near but not immediate future, since for
them there is a chance of retention.
• To build a long-term churn model, immediate churners, namely customers who
churned during the two-month latency period, are excluded from the model
training.