Segmentation for Retailers - Data Mining Techniques in CRM: Inside Customer Segmentation - page 336

Typically, RFM analysis involves the grouping (binning) of customers into
chunks of equal size, or quantiles, in a way similar to the one presented for value-
based segmentation. This binning procedure is applied independently to the three
RFM component measures. Customers are sorted according to the respective
measure and are grouped in classes of equal size. For instance, the breakdown
into four groups results in quartiles of 25%, and the breakdown into five groups,
in quintiles of 20%. As a consequence the RFM measures are transformed into
ordinal scores. In the case of binning into quintiles, for example, the RFMmeasures
are converted into rank scores ranging from 1 to 5. Group 1 includes the 20% of
customers with the lowest values and group 5 the 20% of customers with the top
values in the corresponding measure. Especially for the recency measure, the scale
of the derived ordinal score should be reversed so that larger scores represent the
most recent buyers.
The derived R, F, and M bins become the components for the RFM cell
assignment. These bins are combined with a simple concatenation to provide the
cell assignment. Customers with the top RFM values and quintile values of 5 are
assigned to cell 555. Similarly, customers with the average recency (quintile 3),
top frequency (quintile 5), and lowest monetary values (quintile 1) form cell 351,
and so on.
This procedure for constructing the RFM cells is illustrated in Figure 8.1.
Figure 8.1 Assignment to the RFM cells.