Background to
RFM segmentation (part of our 'sales targeting insight' process)
RFM (recency, frequency,
monetary) analysis is a marketing technique used to determine
which customers are
considered best by examining how recently
a customer has purchased (recency), how often they purchase
(frequency), and how much they spend (monetary).
RFM analysis is based
on the marketing principle that "80% of your business comes
from 20% of your customers" and is
a very effective way to start to
develop more productive
and profitable sales and marketing strategies!
For more than 40 years, direct
marketers have deployed RFM to improve their targeting - often
using an informal RFM analysis to target their communications
to individuals most likely to make purchases (or donations).
The reasoning behind RFM is simple:
people who purchased (or donated) once were more likely to purchase
(or donate) again. With the advent of e-mail marketing campaigns
and customer relationship management software, RFM ratings have
become an important tool.
There
are many ways that RFM can be calculated and deployed.
One approach is to assign customers with a ranking number of
1,2,3,4, or 5 (with 5 being highest) for each RFM parameter.
The three scores together are referred to as an RFM "cell".
The database is sorted to determine which customers were "the
best customers" in the past, with a cell ranking of "555" being
ideal.
Whilst
RFM analysis is an extremely powerful tool,
a company must be careful not
to over communicate to customers with the highest rankings.
Also, customers with
low cell rankings should not be neglected, but instead cultivated
to become better customers.
Also, where you are developing strategies to promote specific
products or services, the RFM approach can be developed into
more detailed segmentation.
For instance,
your
database
could first
be segmented by product (or service) group, then a Pareto split
is applied to each segment to form two new segments (80% and
20% for example), each of which can be subjected to RFM categorisation.
Then,
within the top RFM bands, customers can be scored using 'savage'
scores that produce a ranking that takes account of the variance
of customer spend within a segment. This approach will deliver
a much more precise targeting solution.
Why RFM segmentation works
The value of RFM
(Recency, Frequency, Monetary) analysis as a method to identify
high-response customers in sales and marketing promotions, and
to improve overall response rates, is well known and is widely
applied today. Less widely understood, however, is the value
of applying RFM scoring to a customer database and measuring
how customers migrate from cell to cell over time.
Below we
have outlined
one approach to an
RFM Migration analysis which was applied at a logistics company
named XYZ. The company name and numbers used have been disguised
to protect the confidential results of this program.
Customers who have
purchased from you recently are more likely to respond to your
next promotion than those whose last purchase was further in
the past.
This is a universal
principle which has been found to be true in almost all industries:
insurance, banks, cataloguing, retail, travel, etc. It is also
true that frequent buyers are more likely to respond than less
frequent buyers.
Big spenders often
respond better than low spenders. These are the three simple
principles lying behind RFM analysis. What skilled marketers
have done is to take these three ideas and quantify them.
They code all customers
into RFM cells and examine the response rates of the customers
in each cell when exposed to the same promotion.
It is true, of course,
that only a percentage of customers will make an additional
purchase based on a new promotion. But, of those that do respond,
the responses usually come from customers in higher ranking
RFM cells.
Click
here
for an RFM example
