Customer Segmentation - Data Mining Techniques in CRM: Inside Customer Segmentation - page 192

Table 5.1 Segmentation types and business tasks.

Business situation/task Appropriate segmentation Analytical tools and

criteria techniques

New product design and
development

Needs/attitudinal and
behavioral

Combination of data min-
ing and market sur-
vey/factor and cluster
analysis

Design of customized prod-
uct offering strategies

Behavioral Data mining/factor and

cluster analysis

Brand image and key prod-
uct benefits to be com-
municated

Needs/attitudinal Market surveys/factor anal-

ysis and cluster analysis

Differentiated customer
service

Customer value in com-
bination with other
attributes, for example,
age (tenure) of customer

Binning (grouping in tiles)
of customers according
to their value (e.g., low
n %, medium n %, top
n %) and cross-tabulating
with other attributes, for
example, value and old-
ness of customer

Resource allocation and
prioritization of the mar-
keting interventions that
aim at customer develop-
ment and retention

Customer value supple-
mented by deep under-
standing of what drives
customer decision to buy
and/or to churn

Value tiles and market sur-
vey to identify drivers of
decisions to buy and/or
to churn

Identifying target groups
for campaigns

Propensity scores derived
from relevant classifica-
tion models

Data mining using clas-
sification model-
ing - grouping cus-
tomers according to their
propensity scores and
their likelihood to churn
and/or to buy

usually stored and available in the organization's databases. Customers are
divided according to their identified behavioral and usage patterns. This type of
segmentation is typically used to develop customized product offering strategies.
Also, for new product development, and the design of loyalty schemes.
3. Propensity based: In propensity-based segmentation customers are grouped
according to propensity scores, such as churn scores, cross-selling scores, and