Segmentation Applications in Banking - Data Mining Techniques in CRM: Inside Customer Segmentation - page 289

Table 6.24 An outline of the bank's segmentation scheme.

Rule Segment Percentage Definition

1 Affluent 2 Total assets ≥ $100 000

≥ <

2 Affluent Mass 3 Total assets $50 000 and $100 000

3 Business Mass 11 Business loans or other business services

customers

4 Pure Mass 72 (a) Depositors-investors (14%)

(b) Young active customers (20%)

(c) Borrowers-credit card users (22%)
(d) Full relationship customers (16%)

5 Inactive Mass 12 Max total balances in last six months

<

$50 and total number of transactions
in last six months = 0

differentiated needs. Their approach combined business rules and automatic
clustering algorithms. The main dimensions used for grouping the customers were
their total assets, product utilization (balances), and transactional patterns. The
profiling of the segments was supplemented with marketing research findings and
demographic information. The revealed segments are listed in Table 6.24.
Customers with a minimum of $50 000 of assets comprised the ''Affluent''
and ''Affluent Mass'' segments. These segments jointly accounted for 5% of the
customer base. Customers with business products such as business loans, business
accounts, and business services were considered as a special case and assigned to
a distinct ''Business Mass'' segment.
The ''Pure Mass'' segment included the rest of the clients. It was the bank's
most crowded segment with over 70% of the total customer base. Due to its
diversity and the different customer types that it was believed to encompass,
the marketers of the organization decided to further segregate this segment
according to behavioral criteria. As a result, four behavioral sub-segments were
identified, separating ''Pure Mass'' customers into depositors-investors, young
active customers, borrowers-credit card users, and full relationship customers.
''Inactive'' customers, with low asset balances and transaction dormancy, were
distinguished from the rest and made up a segment of their own. These customers
were set apart to investigate the reasons for inactivity and for inclusion in upcoming
reactivation campaigns.