Post by nurnobisorker14 on Oct 31, 2024 4:23:06 GMT
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 bulk email campaigns 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 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 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 collected relating to the three variables above are then categorized using a scoring system that allows the creation of the RFM model at all points ( click here to learn more).
In this phase, the adoption of a Customer Data Platform , such as Blendee, allows the potential of artificial intelligence to be exploited not only to collect and analyze data, but also to process it thanks to the adoption of a scoring system.
The result? Advanced profiling and segmentation of your audience using the RFM model.
Here are some clusters that can be discovered:
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);
Hot customers who have recently purchased, spent a lot, but have not yet purchased often (high recency, high price, low frequency)
In addition to the most promising clusters, such as those listed above, we can identify others that rather require activities aimed at a final ring:
customers who haven't purchased for a long time, who may have only made one purchase and spent little;
customers who have purchased frequently in the past, perhaps during promotions, have not spent much and have not purchased for a long time;
lost customers, i.e. those with the lowest values in terms of frequency, recency and amount spent.
RFM Matrix and Personalization: The Right Strategy for Each Segment!
Once the different segments and clusters of users have been identified, implementing the most effective strategy and personalizing the customer experience will be even easier and more immediate:
High spenders, recent buyers and frequent buyers can be the best brand ambassadors and could be hired for new product launches;
Most loyal customers who buy often, even if they don't spend much, can be incentivized to purchase higher value products, perhaps with personalized product recommendations;
Customers who haven't purchased in a long time, but have purchased a lot in the past, could be re-engaged in surveys to get feedback and ad hoc promotions;
More offer-sensitive customers could be rewarded with product offers, even at higher prices, but still in line with their recent purchases;
On the other hand, we may send periodic offers to customers who have the lowest values in terms of frequency, monetary and recency.
In the era of customer experience personalization , knowing your audience is the first step to implementing successful marketing strategies. The RFM matrix represents, without a shadow of a doubt, one of the most effective models at this stage because it allows you to identify, empirically, the user clusters that are most interesting to work on and allows you to focus on the most effective strategy to engage them.
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 bulk email campaigns 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 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 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 collected relating to the three variables above are then categorized using a scoring system that allows the creation of the RFM model at all points ( click here to learn more).
In this phase, the adoption of a Customer Data Platform , such as Blendee, allows the potential of artificial intelligence to be exploited not only to collect and analyze data, but also to process it thanks to the adoption of a scoring system.
The result? Advanced profiling and segmentation of your audience using the RFM model.
Here are some clusters that can be discovered:
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);
Hot customers who have recently purchased, spent a lot, but have not yet purchased often (high recency, high price, low frequency)
In addition to the most promising clusters, such as those listed above, we can identify others that rather require activities aimed at a final ring:
customers who haven't purchased for a long time, who may have only made one purchase and spent little;
customers who have purchased frequently in the past, perhaps during promotions, have not spent much and have not purchased for a long time;
lost customers, i.e. those with the lowest values in terms of frequency, recency and amount spent.
RFM Matrix and Personalization: The Right Strategy for Each Segment!
Once the different segments and clusters of users have been identified, implementing the most effective strategy and personalizing the customer experience will be even easier and more immediate:
High spenders, recent buyers and frequent buyers can be the best brand ambassadors and could be hired for new product launches;
Most loyal customers who buy often, even if they don't spend much, can be incentivized to purchase higher value products, perhaps with personalized product recommendations;
Customers who haven't purchased in a long time, but have purchased a lot in the past, could be re-engaged in surveys to get feedback and ad hoc promotions;
More offer-sensitive customers could be rewarded with product offers, even at higher prices, but still in line with their recent purchases;
On the other hand, we may send periodic offers to customers who have the lowest values in terms of frequency, monetary and recency.
In the era of customer experience personalization , knowing your audience is the first step to implementing successful marketing strategies. The RFM matrix represents, without a shadow of a doubt, one of the most effective models at this stage because it allows you to identify, empirically, the user clusters that are most interesting to work on and allows you to focus on the most effective strategy to engage them.
