How to Use It for Your E-commerce Strategy
Posted: Wed Dec 04, 2024 7:04 am
From more traditional campaigns to inbound marketing activities : too often, within companies, the attention of marketers and strategists is oriented towards the acquisition of new customers rather than the maintenance of those already acquired, in fact a real wealth for the company itself.
For the same product, a purchase made by an existing customer costs the company on average 5 times less than one made by a new customer, a figure that should stimulate us to reflect and understand the importance of knowing and making the most of so-called returning customers .
In today's article we discover how to identify and better understand our best customers, thanks to the RFM matrix analysis model , particularly used in the marketing automation field for the implementation of marketing retention and customer loyalty strategies .
RFM Matrix: What it is and why it can be important for strategy
As anticipated, the RFM marketing matrix is an analysis model that allows you to identify the potentially best customers for the company, through the combination of three different variables.
Recency: indicates the time elapsed since the last purchase
;
Frequency: indicates the number of times the user made a purchase in a given period of time (usually 1 year) ;
Monetary: The customer's total spending in the reporting period.
This model is based on the Pareto theory , according to which 80% of the turnover is generated by 20% of the customers and starts from the consideration of three fundamental assumptions:
Customers who have purchased more recently are generally more likely to purchase than those who have not done so in a while;
Customers who purchase more frequently are more likely to purchase again than those who purchase only once;
Customers who spend more are more likely to buy again
.
Already from this first introduction to the list of antarctica consumer email RFM model it is possible to understand the potential of this analysis as a tool for creating segments and commerce: users with higher RFM scores will be our best customers, those on which it is worth investing time and energy .
But let's delve even deeper into the topic
.
RFM Matrix: How to Build and Implement It
Implementing an RFM analysis model manually is quite complex, especially during data maintenance and updating.
This is why more and more marketing automation platforms , such as Blendee, are offering this functionality integrated into Analytics and as an audience segmentation tool .
But let's start with the three basic variables: by analyzing our customers' data we should not only determine the values related to the three basic variables, but create a scoring system that allows us to assign them a score.
The latter can be based on an empirical and subjective nature , that is, by defining the values of the various thresholds at will (perhaps more recommended for small businesses) or in statistical mode through weighting estimation or calculation of percentiles.
Let's start with an example: the values reported are to be considered merely illustrative, as scores and threshold values must be evaluated on the basis of the data relating to your eCommerce.
For the same product, a purchase made by an existing customer costs the company on average 5 times less than one made by a new customer, a figure that should stimulate us to reflect and understand the importance of knowing and making the most of so-called returning customers .
In today's article we discover how to identify and better understand our best customers, thanks to the RFM matrix analysis model , particularly used in the marketing automation field for the implementation of marketing retention and customer loyalty strategies .
RFM Matrix: What it is and why it can be important for strategy
As anticipated, the RFM marketing matrix is an analysis model that allows you to identify the potentially best customers for the company, through the combination of three different variables.
Recency: indicates the time elapsed since the last purchase
;
Frequency: indicates the number of times the user made a purchase in a given period of time (usually 1 year) ;
Monetary: The customer's total spending in the reporting period.
This model is based on the Pareto theory , according to which 80% of the turnover is generated by 20% of the customers and starts from the consideration of three fundamental assumptions:
Customers who have purchased more recently are generally more likely to purchase than those who have not done so in a while;
Customers who purchase more frequently are more likely to purchase again than those who purchase only once;
Customers who spend more are more likely to buy again
.
Already from this first introduction to the list of antarctica consumer email RFM model it is possible to understand the potential of this analysis as a tool for creating segments and commerce: users with higher RFM scores will be our best customers, those on which it is worth investing time and energy .
But let's delve even deeper into the topic
.
RFM Matrix: How to Build and Implement It
Implementing an RFM analysis model manually is quite complex, especially during data maintenance and updating.
This is why more and more marketing automation platforms , such as Blendee, are offering this functionality integrated into Analytics and as an audience segmentation tool .
But let's start with the three basic variables: by analyzing our customers' data we should not only determine the values related to the three basic variables, but create a scoring system that allows us to assign them a score.
The latter can be based on an empirical and subjective nature , that is, by defining the values of the various thresholds at will (perhaps more recommended for small businesses) or in statistical mode through weighting estimation or calculation of percentiles.
Let's start with an example: the values reported are to be considered merely illustrative, as scores and threshold values must be evaluated on the basis of the data relating to your eCommerce.