Two hands protecting a group of wooden figures

RFM, short for Recency, Frequency, Monetary Value, helps to identify particularly attractive customers. So, how can you use RFM for your own campaigns and strategy?

What does RFM mean?

Recency – When did the customer last buy something?

Frequency – How often does the customer buy something?

Monetary Value – How much money does the customer spend on purchases?

RFM analysis measures customers in each category, according to Troy Segal’s article in Investopedia, a measurement value of 1-5 is common, with 5 as the highest/best value. The average of these categories results in a ranking of the „best“ customers.

Why is RFM used?

According to the Pareto principle, quite often 80 % of all results are based on 20% of actions/measures, which is why it’s also known as the „80/20 principle“. An interesting example is an insight into Microsoft’s daily IT routine. There, the IT team found out that 80 % of all software problems were caused by 20 % of all programming errors. Even more, 50 % of all problems were based on 1 % of all programming errors (source: via CRN). With this insight, many resources could be saved, since the developers could concentrate on the 1 % or the 20 %.

In marketing, the Pareto principle is often interpreted to mean that 80 % of the revenue is based on 20 % of the total customer base. RFM thereby determines characteristics that make up loyal, high-revenue customers. However, these cannot only be used as an overall value for ranking. If the individual values are considered, they can be used to identify specific campaigns and measures to strengthen customer loyalty.

Recency

Customers who have just purchased something are more likely to be loyal than customers whose last purchase was months or even years ago. Of course, it’s worth distinguishing new customers from existing customers. Was the most recent purchase the customer’s first purchase, or has the customer shopped with the company before? But even with new customers, this information can be used to subsequently place content or messages that motivate a repeat purchase and strengthen the „early bond“.

Frequency

Loyal customers buy more frequently from a company. Accordingly, the value of purchase frequency is higher for them than for other customers. It is particularly valuable to determine whether certain frequencies are so „reliable“ that the next purchase can be predicted. With the use of artificial intelligence, it is possible, for example, to prepare e-mail campaigns that draw attention to certain offers before the purchase. Other measures could be offering discounts or additional suitable products and services for up/cross-selling options. In addition, subscription offers that save money and time could be a great tool for more customer loyalty.

Monetary Value

Loyal customers often spend more money with their favorite companies than other customers. They can be motivated, for example, by promotions, product recommendations and offers. For this KPI, it’s also relevant how high the „share of wallet“ of the respective customer is for the company. „Share of Wallet“ means the percentage of a budget for a product group, service, etc. a customer is willing to spend with a specific company. A customer might, for example, spend roughly 60 % of their food budget at a local supermarket and 40 % at a nearby organic shop. With targeted promotions, for example, this share can be increased even further.

Meanwhile, if customers have a very fixed share that they spend at regular intervals, for example, they need to be approached differently than customers who vary their spending depending on the offer. Companies can therefore use the Monetary Value metric to differentiate between different customer segments and thus plan the right measures.

Customers with low RFM should not be ignored

Loyal customers must be cared for and developed. This requires a personal approach, relevance in communication, and clear added value for customers. Customers with a low RFM value can potentially develop into customers with a high RFM value with the right communication.

A good mix of creative marketing methods and regular monitoring of the measured values is recommended here. On the one hand, this allows the success of methods to be validated. On the other hand, it can also identify other customer characteristics that signal whether a customer with a low RFM value has a higher or lower probability of becoming a customer with a high RFM value.


Address the right customers with the right campaigns. Find out in our whitepaper, how uplift modeling helps to optimize your engagement rates with predictive analytics.

New call-to-action