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1.3. What’s RFM Analysis
RFM
From Wikipedia, the free encyclopedia
RFM is a method used for analyzing customer
behavior and defining market segments. It is
commonly used in database marketing
and direct marketing and has received particular
attention in retail
.
RFM stands for
Recency - How recently did the customer purchase?
Frequency - How often do they purchase?
Monetary Value - How much do they spend?
To create an RFM analysis, one creates categories for each attribute. For instance, the
R
ecency attribute might be broken into three categories: customers with purchases within
the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may
be arriv
ed at by applying business rules, or using a data mining technique, such as CHAID
,
to find meaningful breaks.
Once each of the attributes has appropriate categories defined, segments are created from
the intersection of the values. If there were three categories for each attribute, then the
resulting matrix would have twenty-seven possible combinations (one well-known
commercial approach uses five bins per attributes, which yields 125 segments). Companies
may also decide to collapse certain subsegments, if the gradations appear too small to be
useful. The resulting segments can be ordered from most valuable (highest recency,
frequency, and value) to least valuable (lowest recency, frequency, and value). Identifying
the most valuable RFM segments can capitalize on chance relationships in the data used for
this analysis. For this reason, it is highly recommended that another set of data be used to
validate the results of the RFM segmentation process.
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