Segmentation for Retailers - Data Mining Techniques in CRM: Inside Customer Segmentation - page 344

5. Development of the RFM segments through clustering: The marketers
of the enterprise decided to also apply a clustering model to analyze the
RFM components. They also decided to enrich the segmentation criteria with
information concerning the purchases of private label products and the average
basket value. This value denotes the average amount spent on each transaction.
Private label products are goods which are only sold in the retailer's stores under
the retailer's brand name and are often positioned as low-cost alternatives to
''named'' brands. The retailer was interested in investigating the favoring of
these special products. Therefore the percentage (%) of the total purchase
amount accounted for by private label products was included in the clustering
fields along with the RFM individual components and the basket value. The
clustering model identified six RFM segments, which are listed in Table 8.1
along with their identified characteristics.
The majority of customers with average RFM patterns were assigned to
the ''Typical'' segment. The ''Dormant'' segment contained customers on the
verge of inactivity with very low purchase rates and the worst RFM profile. At
the other end stood the ''Superstars'' and the ''Golden customers''. These were
high-value customers with increased frequency of transactions. ''Superstars''
in particular seemed to be the most loyal ones, with an increased number of
visits/transactions and a high preference for private label products. ''Everyday

Table 8.1 The RFM segments revealed through clustering.

Superstars

• The most loyal customers
• Highest value
• Highest frequency
• High spending on private labels

Golden customers

• Second highest value
• High frequency
• Average spending on private labels

Typical customers • Average value and frequency

• Average spending on private labels

''Exceptional occa-
sions'' customers

• The second lowest frequency after ''Dormant customers''
• Large basket
• Low recency values (long time since their last visit)

''Everyday'' shoppers

• Increased frequency of transactions
• Small basket
• Private labels
• Medium to low value

Dormant customers • Lowest frequency and value

• Long time since their last visit (lowest recency values)