Segmentation for Retailers - Data Mining Techniques in CRM: Inside Customer Segmentation - page 339

threshold value were considered as dormant and were also excluded from the
subsequent segmentation.
3. Data preparation and computation of the R, F, and M measurements:
Transactional data were aggregated (grouped by) at a customer (card ID) level.
A two-fold aggregation procedure was followed. Initially, records were
grouped by card ID and transaction ID. Then they were further grouped by
card ID as shown in the IBMSPSSModeler Aggregate screenshot in Figure 8.3.
The information summarized for each customer included:
(a) Date of the latest (maximum in the case of a date or timestamp field)
purchase transaction. This information was then used to derive recency as
the number of days since the most recent purchase transaction. The IBM
SPSS Modeler Derive node and a date function were used to return the
number of days from the last transaction to the current date (represented
Figure 8.3 The IBM SPSS Modeler Aggregate node for summarizing purchase data at
a customer level.