Manual implementation of an RFM analysis model is quite complex, especially when maintaining and updating data.
This is why more and more marketing automation platforms , such as Blendee, offer this functionality integrated into Analytics and as an audience segmentation tool .
But let's start with the three basic variables: when analyzing our customer data, we australia cell phone number list must not only determine the values of the three basic variables, but create a scoring system that allows us to assign a score to them.
The latter can be based on an empirical and subjective character , that is to say by defining at will the values of the different thresholds (perhaps more recommended for small companies) or in statistical mode by estimating the weighting or calculating percentiles.
Let's start with an example: the values given should be considered as mere examples, as the scores and threshold values should be evaluated based on your e-commerce data.
How to use the RFM matrix
Now let's combine the score values with frequency, monetary and recency data, processed on three sample customers.
Monetary Recency Frequency in RFM Matrix
Although at first glance customer 1675 seems the best, considering the amount spent, the matrix reveals that the best customer is 1289 with a purchase made in a more recent period.
Once the different user segments have been identified (hero users, at-risk users, users who buy frequently but spend little, users who buy frequently, spend a lot but have not bought for a long time, etc.) it will be possible to create ad hoc strategies to re-engage them, increasing their value over time.
Analysis and strategy: what sales levers to put in place As
you can easily guess, the RFM matrix not only allows you to identify the best customers but also allows you to Map your audience in order to identify the most interesting customers to work on to move them from one level to another.
All this implies the implementation of ad hoc sales strategies.
Let's take a few examples:
To a customer who has already made a purchase > we can offer a discount on the second purchase
to a customer who is a VIP > we can offer a loyalty program or access to exclusive promotions
To a user who buys frequently > we can offer benefits
As you can see, the combination of the different variables allows you to create several segments
:
customers who buy frequently but spend little (high frequency, low monetary);
customers who buy frequently and spend a lot but haven't purchased in a long time (high frequency, high monetary, low recency);
High spenders, recent purchasers, and frequent purchasers (high recency, high currency, high frequency)
.
For each identifiable segment, it is thus possible to set up a commercial offer and a targeted communication proposal .
The RFM matrix has enormous potential and can be the basis of truly effective marketing loyalty strategies : don't just focus on acquiring new customers, but bring revenue to your advertising investments and increase the value of existing customers over time.