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analysis. Their ''contribution'' is crucial in the building of a churn model but they
have nothing to offer in a segmentation scheme mainly involving phone usage. In
addition, the segmentation population was narrowed down even more by excluding
users with no incoming or outgoing usage within the past threemonths. These users
have been flagged as inactive and selected for further examination and profiling.
They could also form a target list for an upcoming reactivation campaign, but
they do not have much to contribute to a behavioral analysis since, unfortunately,
inactivity is their only behavior at the moment.
BEHAVIORAL AND VALUE-BASED SEGMENTATION - SETTING
UP THE PROJECT
The methodological approach followed was analogous to the general framework
presented in detail in the relevant chapter. In this section we just present some
crucial points concerning the project's implementation plan, which obviously
affected the whole application.
Customers may own more than one MSISDN, which may be used in a
different manner to cover different needs. In order to capture all the potentially
different usage behaviors of each customer, it was decided to implement the
behavioral segmentation at MSISDN level. Therefore, relevant input data have
been aggregated accordingly and the derived cluster model assigned eachMSISDN
to a distinct behavioral segment.
A two-way approach was decided for the value-based segmentation. The value
segments were identified at both an MSISDN and a customer level, providing a
complete view of profitability. Since the methodological approach does not depend
on the level of the analysis, only the MSISDN value segmentation will be described
here.
The behavioral segmentation implementation included the application of a
data reduction technique (PCA in particular) to reveal the distinct dimensions of
information, followed by a clustering technique to identify the segments. Once
again the mining data mart tables, presented in detail in the corresponding chapter,
comprised the main sources of input data. Table 7.1 outlines the main usage aspects
that were covered.
On the other hand, value-based segmentation relies only on a single field. It
does not need the application of a data mining algorithm either. It only involves
a simple sorting of records (MSISDNs or customers) according to a profitability
index and an assignment to corresponding groups. In the case presented here, it
is assumed that the respective value index is already calculated and stored in the
organization's MCIF. The marketers of the telephone operator used the already
calculated marginal average revenue per user (MARPU), a marginal profitability
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