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
