Segmentation for Retailers - Data Mining Techniques in CRM: Inside Customer Segmentation - page 345

shoppers'' made frequent but low-value transactions, probably to cover their
daily needs. They also showed increased preference for private label brands.
Occasional customers on the other hand made infrequent visits to the store
branches but of high average value.
A deployment procedure was also developed to support future updating
of the segments. The clustering model was supplemented with a classifica-
tion model, a decision tree in particular, which identified the input patterns
associated with each revealed RFM segment. The tree rules were saved for
the segment assignment of new records. The deployment plan also included a
periodic cohort analysis, a type of ''before-after'' examination of the customer
base, with simple reports that could identify the migrations of customers across
segments over time.
RFM: BENEFITS, USAGE, AND LIMITATIONS
The individual RFM components and the derived segments convey useful infor-
mation with respect to the purchasing habits of consumers. Undoubtedly, any
retail enterprise should monitor the purchase frequency, intensity, and recency
as they represent significant dimensions of the customer's relationship with the
enterprise.
Moreover, by following RFM transitions over time an organization can keep
track of changes in the purchasing habits of each customer and use this information
to proactively trigger appropriate marketing actions. For instance, specific events,
like the decline in the total value of purchases, a sudden drop in the frequency
of visits, or no-shows for an unusually long period of time, may indicate the
beginning of the end of the relationship with the organization. These signals,
if recognized in time, should initiate event-triggered retention and reactivation
campaigns.
RFM analysis was originally developed for retailers, but with proper modifi-
cations it can also be applied in other industries. It originated from the catalogue
industry in the 1980s and proved quite useful in targeting the right customers
in direct marketing campaigns. The response rates of the RFM cells in past
campaigns were recorded and the best-performing cells were targeted in the next
campaigns. An obvious drawback of this approach is that it usually ends up with
almost the same target list of good customers, who could become annoyed with
repeated contacts. Although useful, the RFM approach, when not combined with
other important customer attributes such as product preferences, fails to provide
a complete understanding of customer behavior. An enterprise should have a
complete view of the customer and use all the available information to guide its
business decisions.