The customers that visit your eCommerce site are not all the same. They are interested in different items, have a different purchase history, fall into different demographics and most importantly, spend a different amount of money. This is why communicating with them should be differentiated, to generate the highest levels of response, grow their loyalty and increase customer lifetime value. RFM segmentation is a great tool used by marketers to identify these groups and address them with messages that are relevant for their particular behavior.
While there are many ways to perform segmentation, the RFM method, which stands for Recency, Frequency and Monetary, is by far the most popular because it is simple, intuitive, and utilizes objective, numerical scales that generate a concise and informative high-level image of customers.
The first metric, Recency, shows how long has passed since a customer’s last interaction with the brand. The higher the value, the more likely it is that the customer will be responsive to communications from the brand.
Frequency will show how often a customer has transacted or interacted with the brand during a particular period of time. Customers with frequent activities are more engaged, and probably more loyal.
The Monetary value reflects how much a customer has spent with the brand during a particular period of time.
Therefore, these three metrics are some of the most important indicators for an eCommerce business. The frequency and monetary values affect a customer’s lifetime value, and recency affects retention, a measure of engagement.
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If done manually, the first step in building an RFM model is to have a look at the data for your customer base and assign Recency, Frequency and Monetary values to each customer in an Excel spreadsheet or a database.
For each of the RFM values, we will divide our customers into 3, 4 or 5 groups, depending on how thorough you want your analysis to be. We recommend using 4 groups, with the highest score being numbered as 1 and the lowest, as 4.
As an example, when it comes to recency, we scored customers who visited our website in the last 5 days with 1 (the highest ranking), from 5 to 10 days with 2, from 10 to 15 days with 3 and over 15 days with 4 (the lowest ranking).
The same principle will apply to the other metrics, frequency and monetary.
At this point we have the analysis done. Next comes the segmentation, which is the most important part of the process.
As we said at the beginning, the importance of the RFM model lies in the fact that it helps us understand our customers’ buying behaviours and based on these, we can address them with a message that creates an impact. eCommerce is about generating constant emotions and creating long-time relationships.
One of the most important groups are our Core customers, and based on the RFM model, they are the customers that score the highest in all 3 columns, with a 1-1-1 score – Customer 3 in our example. They are highly engaged customers who have bought the most recent, the most often, and generated the most revenue.
Marketing strategies for this type of customers should focus on loyalty programs and new product introductions. These customers have proven to have a higher willingness to pay, so we should not use discount pricing to generate increased sales. Instead, we should focus on value added offers through product recommendations based on previous purchases.
Another important group is Our most loyal customers. The RFM score for these ones is X-1-X. Marketing strategies should focus on loyalty programs and benefits for leaving reviews. At the same time, you should consider rewarding these customers with Free Shipping or other benefits.
With an RFM score of X-X-1, High Paying Customers are the ones that have demonstrated a high willingness to pay. For these ones we should consider sending premium offers, subscriptions tiers, luxury products, cross/up-sells to increase average order value.
Faithful customers, with a RFM Score X-1-3, X-1-4, are customers who return often, but do not spend a lot. Marketing strategies should focus on increasing monetization through product recommendations based on past purchases and incentives tied to spending thresholds.
The RFM score 1-4-X are first time buyers and for these ones, welcome emails and promotions are a good way to build loyalty.
The RFM Score: 4-4-X describes past customers who haven’t bought in awhile. They should be incentivised with price deals, new product launches, or other retention strategies.
These are some of the most important groups, but marketers should assemble groups of customers most relevant for their particular business objectives and retention goals, and it is not uncommon to find more detailed groups like High-spending New Customers, Low-Spending Active Loyal Customers, or other variations.
Building your own RFM model manually might be suitable for small eCommerce stores, but for companies with hundreds or thousands of orders every day, it might not be an easy task.
For Magento there is no standard RFM segmentation reporting available, unless you are the lucky owner of an expensive Magento Business Intelligence reporting package. There are some online tools to help you generate these reports, the RFM Calculator being one of them. Simply export your order file from your Magento 2 order screen via the ‘Export -> CSV’ option in the top right of the screen, upload it to their systems and we’ll generate the appropriate RFM reporting for your data, including automatically assigning the group boundaries for Recency, Frequency and Monetary.
Owners of an Adobe Commerce License have the acclaimed Business Intelligence Tools to help them gain the insight they need to create the right marketing strategies and make sound business decisions.
There are several retail analytics tools available on the market like Reveal from Omniconvert, SalesManago or the Segmentor from Optimove, that offer advanced reporting for eCommerce. Their automated RFM segmentation and analysis enables you to extract customer insights without having to manually compile data.
The RFM model is indeed one powerful method to differentiate your marketing efforts on groups of customers, but as eCommerce is changing and developing day by day, looking at your customers from a historical point of view is simply not enough. More advanced customer segmentation techniques based on predictions are used to read future customer behaviours, but these are not as simple, and require the use of predictive analytics and machine learning technologies.
Many companies want to gain new customers as well as retain current customers. So, because RFM segmentation will not help acquire new customers, it needs to be combined with other strategies.
But the clear benefit of the RFM model is that it is simple and intuitive and can be done in-house by your marketing team, without the need to break the bank. If done right, it enables you to get an at-a-glance view so you can create actionable strategies based on each group of customers.