Segmentation Applications in Telecommunications - Data Mining Techniques in CRM: Inside Customer Segmentation - page 306

Once again, the interpretation is quite clear since the contrast in usage
between the top and bottom segments is very intense. In almost all of these KPIs
(for instance, total outgoing calls, total outgoing voice calls and minutes) users at
the top of the value pyramid present values up to 10 times higher than those at
the bottom. For example, the average number of total outgoing calls reaches 700
calls per month for top users, while this value does not exceed 70 calls per month
among mass users.
The two-fold value segmentation project concluded by developing an analo-
gous segmentation scheme at a customer level, at the end of which each customer
was allocated to a corresponding value group.
The business benefits from the identification of value segments are prominent.
The implemented segmentation can provide valuable help to the marketers in
setting appropriate objectives for their marketing actions according to customer
value. High-value customers are the heart of the organization. Their importance
should be recognized and rewarded. They should perceive their importance every
time they interact with the operator. Preventing the defection of such customers is
vital for any organization. Identifying valuable customers at risk of attrition should
trigger an enormous effort to avoid losing them to competitors. For medium- and
especially low-value customers, marketing strategies should focus on driving up
revenue through targeted cross- and up-selling campaigns to make it approach that
of high-value customers.
Use of Value Segments in Acquisition Campaigns
Value-based segments can provide useful information for the development of
effective acquisition models. Acquisition campaigns aim at increasing market
share through expansion of the customer base to include customers new to
the market or drawn from competitors. In mature markets there is fierce
competition for acquiring new customers. Each organization incorporates
aggressive strategies, massive advertisements, and attractive discounts.
Predictive models can be used to guide customer acquisition efforts.
However, a typical difficulty with acquisition models is the availability of
input data. The amount of information available on people who do not yet
have a relationship with the organization is generally limited compared to
information on existing customers. Without data one cannot build predictive
models. Thus data on prospects must be collected. Most often, buying data
on prospects at an individual or post code level can resolve this issue.
The usual approach in such cases is to run a test campaign on a random
sample of prospects, record their responses, and analyze themwith predictive