Personalization activities for even more effective customer engagement -oriented marketing strategies : let’s go back to the RFM matrix , one of the most effective audience profiling and segmentation models in the e-commerce field.
In a previous article , we had the opportunity to delve deeper, from a theoretical point of view, into this model, understanding its potential compared to more traditional profiling and segmentation models.
>Unlike the latter, in fact, the RFM matrix model examines historical and behavioral data of the entire audience, it is not based on general demographic data, nor on representative samples.
>Precisely by its nature , this profiling and segmentation model allows you to better know your customers and identify the most interesting clusters on which to implement effective engagement strategies aimed at increasing their Customer Lifetime Value .
- Who are my portugal phone number data best customers?
- Which customers are potentially more “likely” to buy at a higher price?
- Which customers can be retained?
- Which ones, on the other hand, are most likely to stop buying from our store?
These are just some of the questions that a careful analysis of its audience carried out through an RFM matrix allows us to answer.
>But let’s discover, in detail, some clusters that can be identified and the strategies that we can put in place for each of them.
RFM Matrix: Advanced Profiling and Segmentation
As we have already had the opportunity to explore in depth, the RFM model is based on three different variables:
- recency : indicates the number the conversation with the audience was not as effective of days that have passed since the customer’s last purchase;
- Frequency : Identifies the number of orders placed during a given period, usually one year;
- Monetary : refers to the total amount spent by the customer during a given period, again the year is taken as a reference.
The data collect relating to the three bf leads variables above are then categorize using a scoring system that allows the creation of the RFM model at all points ( click here to learn more).
The result? Advanced profiling and segmentation of your audience using the RFM model.
Here are some clusters that can be discovere:
- high spenders, recent purchasers, and frequent purchasers (high recency, high monality, high frequency);
- customers who buy frequently, have recently purchased but do not spend much (high frequency, high recency, low monetary);
- customers we can’t lose because they have purchased a lot in the past (high frequency, high currency, low recency);
- customers who haven’t purchased for a long time, who may have only made one purchase and spent little;
- lost customers, i.e. those with the lowest values in terms of frequency, recency and amount spent.
