Customer Segmentation - Data Mining Techniques in CRM: Inside Customer Segmentation - page 193

so on, which are estimated by respective classification (propensity) models.
Propensity scores can also be combined with other segmentation schemes to
better target marketing actions. For instance, the value-at-risk segmentation
scheme is developed by combining propensities with value segments to prioritize
retention actions.
4. Loyalty based: Loyalty segmentation involves the investigation of the cus-
tomers' loyalty status and the identification of loyalty-based segments such as
loyals and switchers/migrators. Retention actions can then be focused on high-
value customers with a disloyal profile whereas cross-selling on prospectively
loyal customers.
5. Socio-demographic and life-stage: This type reveals different customer
groupings based on socio-demographic and/or life-stage information such as age,
income, marital status. This type of segmentation is appropriate for promoting
specific life-stage-based products as well as supporting life-stage marketing.
6. Needs/attitudinal: This segmentation type is typically based on market
research data and identifies customer segments according to their needs,
wants, attitudes, preferences, and perceptions pertaining to the company's
services and products. It can be used to support new product development and
to determine the brand image and key product features to be communicated.
VALUE-BASED SEGMENTATION
Value-based segmentation is the process of dividing the customer base according
to value. It should be emphasized that this is not a one-off task. It is vital for the
organization to be able to track value changes across time. The organization should
monitor and, if possible, intervene in order to prevent downward and encourage
upward migrations.
A prerequisite for this segmentation scheme is the development of an accurate
and credible procedure for determining the value of each customer, on a periodic
basis, preferably at least monthly, using day-to-day inputs on revenues and costs.
Value-based segmentation is developed through simple computations and does
not involve the application of a data mining model. Specifically, the identification
of the value segments involves sorting customers according to their value and
their binning in chunks of equal size, for example, of 10% named quantiles. These
quantiles are the basis for the development of value segments of the form low n %,
medium n %, top n %. A list of typical value segments is as follows:
• Gold: Top 20% of customers with the highest value.
• Silver: 30% of customers with the second highest value.
• Bronze: 50% of customers with lowest value.