Segmentation for Retailers - Data Mining Techniques in CRM: Inside Customer Segmentation - page 335

since the last purchase transaction of the customer. Frequency denotes the number
and rate of purchase transactions. Monetary indicates the value of the purchases.
These indicators are typically calculated at a customer (card ID) level through
simple data processing of the available transactional data.
RFM analysis can be used to identify good customers with the best scores
in the relevant KPIs, who generally tend to be good prospects for additional
purchases. It can also identify other purchasing patterns and respective customer
types of interest, such as infrequent big-spenders or customers with small but
frequent purchases who might also have sales perspectives, depending on the
market and the specific product promoted.
In the retail industry, the RFM dimensions are usually defined as follows:
• Recency: The time (in units such as days/months/years) since the most recent
purchase transaction or shopping visit.
• Frequency: The total number of purchase transactions or shopping visits in
the period examined. An alternative, and probably a better defined, approach
that also takes into account the tenure of the customer calculates frequency as
the average number of transactions per unit of time, for instance the monthly
average number of transactions.
• Monetary: The total value of the purchases within the period examined or
the average value (e.g., monthly average value) per time unit. According to an
alternative, but not so popular, definition, the monetary indicator is defined as
the average transaction value (average value per purchase transaction). Since
the total value tends to be correlated with the frequency of the transactions,
the reasoning behind this alternative definition is to capture a different and
supplementary aspect of purchase behavior.
The construction of the RFM indicators is a simple data management task
which does not involve any data mining modeling. It does, however, involve a
series of aggregations and simple computations that transform the raw purchase
records into meaningful scores. In order to perform RFM analysis each transaction
should be linked with a specific customer (card ID) so that the customer's purchase
history can be tracked and investigated over time. Fortunately, in most situations,
the use of a loyalty program makes the collection of ''personalized'' transactional
data possible.
RFM components should be calculated on a regular basis and stored along
with the other behavioral indicators in the organization's mining data mart and
MCIF table. They can be used as individual fields in subsequent tasks, for instance
as inputs, along with other predictors, in upcoming supervised cross-selling models.
They can also be included as clustering fields for the development of a multi-
attribute behavioral segmentation scheme. Usually they are simply combined to
form a single RFM measure and a respective cell-based segmentation scheme.