Customer Segmentation - Data Mining Techniques in CRM: Inside Customer Segmentation - page 204

of a marketing objective to a data mining goal is not always simple and
straightforward. However, it is one of the most critical issues, since a possible
misinterpretation can result in failure of the entire data mining project.
It is very important for the analysts involved to explain from the outset to
everyone involved in the data mining project the anticipated final deliverables
and to make sure that the relevant outcomes cover the initially set business
requirements.
2. Selection of the appropriate segmentation criteria: One of the key ques-
tions to be answered before starting the behavioral segmentation is what
attributes are to be used for customer grouping. The selection of the appro-
priate segmentation criteria depends on the specific business issue that the
segmentation model is about to address. The business needs imply, if not
impose, the appropriate input fields. Usually people with domain knowledge
and experience can provide suggestions on the key attributes related to the busi-
ness goal of the analysis. All relevant customer attributes should be identified,
selected, and included in the segmentation process. Information not directly
related to the behavioral aspects of interest should be omitted.
For instance, if a mobile telephony operator wants to group its customers
according to their use of services, all relevant fields, such as the number
and volume/minutes of calls by call type, should be included in the analysis.
On the contrary, customer information related to other aspects of customer
behavior, such as payment or revenue information, should be excluded from
the segmentation.
3. Determination of the segmentation population: This task involves the
selection of the customer population to be segmented. An organization may
decide to focus on a specific customer group instead of the entire customer
base. In order to achieve more refined solutions, groups that have apparent
differences, such as business or VIP customers and typical consumer customers,
are usually handled by separate segmentations.
Similarly, customers belonging to obvious segments, such as inactive
customers, should be set apart and filtered out from the segmentation procedure
in advance. Otherwise, the large differences between active and inactive
customers may dominate the solution and inhibit identification of the existing
differences between active customers.
If the size of the selected population is large, a representative sample could
be selected and used for model training. In that case, though, a deployment
procedure should be designed, for instance through the development of a
relevant classification model, which will enable the scoring of the entire
customer base.
4. Determination of the segmentation level: The segmentation level defines
what groupings are about to be revealed, for instance groups of customers,