Data Mining in CRM - Data Mining Techniques in CRM: Inside Customer Segmentation - page 10

Figure 1.5 The next best activity strategy in action.
4. The defection risk/churn propensity as estimated by a voluntary churn model.
5. The payment behavior and credit score of the customer.
In order to better understand the role of these components and see the NBA
strategy in action (Figure 1.5), let us consider the following simple example. A
high-value banking customer has a high potential of getting a mortgage loan but
at the same time is also scored with a high probability to churn. What is the best
approach for this customer and how should he be handled by the organization?
As a high-value, at-risk customer, the top priority is to prevent his leaving and
lure him with an offer that matches his particular profile. Therefore, instead of
receiving a cross-selling offer, he should be included in a retention campaign and
contacted with an offer tailored to the specific characteristics of the segment to
which he belongs.
THE DATA MINING METHODOLOGY
A data mining project involves more than modeling. The modeling phase is just
one phase in the implementation process of a data mining project. Steps of critical
importance precede and follow model building and have a significant effect on the
success of the project.