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Grouping customers into different RFM categories

In the examples above, each division for recency, frequency and monetary value is placed 

in an arbitrary position to place a roughly equal number of customers in each group. This 

approach is also useful since the marketer can set thresholds of value relevant to their under-

standing of their customers.

RFM analysis involves two techniques for grouping customers:

1 

Statistical RFM analysis. This involves placing an equal number of customers in each 

RFM category using quintiles of 20% (10 deciles can also be used for larger databases), as 

shown in Figure 9.22. The figure also shows one application of RFM with a view to using 

communications channels more effectively.  Lower-  cost e-communications can be used to 

communicate with customers who use online services more frequently since they prefer 

these channels and more expensive communications can be used for customers who seem 

to prefer traditional channels. This process is sometimes known as ‘ right-  channelling’ or 

‘ right-  touching’.