Segmentation Applications in Telecommunications - Data Mining Techniques in CRM: Inside Customer Segmentation - page 301

What If a Value Index Is Not Available?
If there is no calculated valid value index, usage fields, known by business
users to be highly related to value, could be used as temporary substitutes for
the value measure, so that value segmentation could proceed. For instance,
the field of monthly average number of call minutes in telecommunications
may not be ideal component for value segmentation, but it could be used as
a workaround.
In value-based segmentation the number of binning tiles to be constructed
depends on the specific needs of the organization. The derived segments are usually
of the form highest n %, medium high n %, medium low n %, low n %. In general,
it is recommended to select a sufficiently rich and detailed segmentation level,
especially in the top groups, in order to discern the most valuable customers. A
detailed segregation level may not be required for the bottom of the value pyramid.
The segmentation bands selected by the marketers were the following:
1. Platinum: The top 1% MSISDNs with the highest MARPU
2. Gold: The top 4% MSISDNs with the second highest MARPU
3. Silver: 15% of MSISDNs with high MARPU
4. Bronze: 40% of MSISDNs with medium MARPU
5. Mass: 40% of MSISDNs with the lowest MARPU.
Since customers with no usage activity have been excluded from the proce-
dure, a sixth segment, not listed but implied, is the one composed of ''Inactive''
customers.
The whole value-based segmentation procedure is graphically depicted in
Figure 7.3.
IBM SPSS Modeler offers a very useful tool (node) called Binning that can
automate the above procedure. It performs ranking and creates the quantiles in
one step. The respective menu is shown in Figure 7.4.
In order to implement the value-based segmentation, the marketers selected
MARPU as the ''Bin field,'' ''Tiles (equal count)'' as the binning method, and
''Percentile (100)'' for the initial grouping of the MSISDNs. These options created
bands of 1% according to the ranked MARPU.
The derived bands were then been processed with a ''Reclassify'' IBM SPSS
Modeler node, in order to refine the solution and regroup the bands into the
desired segments, as indicated in Figure 7.5.
The ''MARPU_TILE100'' field is the one created by the ''Binning'' node
which denotes the assignment into 100 bands of 1%. These bands were recoded