Updated: Apr 25, 2020
When we work with clients it is always useful to categorize them in some way, usually segmentation is related to a business objective or the client’s contribution to the brand. One of the most common ways to do this type of categorization is through a methodology called RFM — “Recency, Frequency, Monetary Value”.
This technique is one of the simplest customer segmentation methods to implement, and at the same time one of the best results in the short term. It is based on the famous Pareto principle, according to which 20% of the clients generate 80% of the income. The RFM is the best way to verify the extent to which this paradigm is real in our case, and to place each client at the level of the value pyramid.
The analysis consists of classifying the clients by their value according to three variables:
What this methodology does is to help us to group clients into segments that are defined by the time that has passed since their last activity, the frequency with which they usually do that activity and the monetary value that the activity implies. So we are going to have customers who have just had an activity with us, they do it very often and they leave a lot of monetary value in a group and those that we have not seen for a long time, come sporadically and when they do they generate much less income.
RFM segmentation is a framework for segmenting your customers based on their buying behaviour:
Recency– the last time that a customer made a purchase. A recent purchasing customer is more likely to make another purchase than a customer who hasn’t made a purchase in a long time.
Frequency– how many times a customer has made a purchase within a given time frame. A customer who makes frequent purchases is more likely to continue to make purchase than a customer who rarely makes purchases.
Monetary Value — the amount of money a customer has spent within that same time frame. A customer who spends more is likely to return than a customer who spends less.
When RFM segmentation is combined with a marketing automation tool you’ll be able to leverage this segmentation in real time to deliver the right content and offer, to the right customer at the right time.
We build scales, based on these variables, giving each client a value according to the percentile in which it is found (percentiles = n groups of equal size, or number of clients).
The most common is to work with 4 values (quintiles), although it is not uncommon to use 10 values.
The RFM analysis has clear benefits in both strategic and tactical segmentation of retail customers:
Simplicity of interpretation and application.
Relative simplicity of calculation, versus multivariate techniques it is easily integrated into the usual promotional dynamics in a marketing area and is ideal for direct marketing and relational marketing: to which segments do I direct what communication? or which segments respond best to the offer?
Given that the abandonment models are complicated to adjust in retail,