Who are our best customers? Who hasn’t bought our products for a long time? These are questions that are often asked by many brands, regardless of their size or market. The behavior of customers changes over time, and companies can undertake different types of activity to engage with them. But to do so, there is one extremely important priority – know your customers.
Some customers are particularly sensitive to discounts and promotions, while others can be enticed with just a single email that presents the new collection, because they regularly buy the brand in question. Everyone behaves differently, but that shouldn’t worry a company, because there are different ways of knowing your customers. RFM is one of them.
What is RFM?
The acronym RFM means Recency Frequency Monetary, which is an algorithm that is capable of organizing a brand’s customers into different clusters, according to their purchase behavior. As a result, you can know your contacts better and target them with focused and tailored communications. But what do these parameters mean in detail?
- Recency represents when a customer last made a purchase.
A brand can use this to identify the customers who have bought something recently and those who haven’t made a purchase for a long time.
- Frequency shows how often customers buy something.
Some people are loyal brand customers, who buy regularly and frequently, while others only make occasional purchases.
- Monetary determines how much a customer spends.
All this data about individual contacts is extremely valuable, because it provides accurate information about their purchase behavior.
Because of the importance of the results, the algorithm is tailored to the appropriate industry and market, before it is used to generate the values. This is done because, for example, the results for an FMCG product will be very different to those for a luxury brand, due to factors such as the different purchase frequencies and average receipts.
Take Paul and Mark as a simple example. They are both Maison brand customers, who each bought something two days ago. But Mark also bought a product last week, while Paul hasn’t purchased anything else for four months. It is very clear these two customers don’t represent the same value to the brand, but of equal importance, is that they also need to be engaged in different ways.
Mark could be a regular buyer, who often purchases from Maison, and it may be beneficial for him to receive weekly news about new arrivals and potential discounts. Paul is, however, an occasional buyer who rarely thinks of Maison. For him, a communication a few days after his last purchase could be more relevant, perhaps with a discount that can be used the next time Paul buys something. In this way, he could be encouraged to find Maison more attractive.
How does RFM work?
RFM applies a scale between 1-5 to the results for each of the three parameters. Once the scale value has been determined, the clusters to which each customer belongs can readily be identified. Intersections between the Recency, Frequency and Monetary values can be created, which enable you to identify, for example, those who spend a lot and buy frequently, or people who spend less but have bought recently, by combining the algorithm results in different ways.
Customers with the highest RFM score are regarded as the brand’s TOP spenders, because they are the most active ones, who purchase most frequently and spend a lot when compared with others. As a result, it is worth investing in them, to build long-lasting relationships.
A few examples
RFM is used as a basis for marketing activities in various ways, because it enables a better audience understanding, and allows differentiated marketing communications deliveries to be planned, which are intended to answer particular customer needs as they arise.
Imagine you represent the Maison brand, which is focused on selling clothes through their
e-commerce website. You have a customer cluster that used to buy frequently with a strong monetary value, but recently, they seem to be uninterested in the brand.
By analyzing their behavior over time, you may decide it is appropriate to carry out a re-engagement campaign, through which you can entice the contacts to choose the brand once more, by offering special prices and potential discounts.
The same campaign would be ineffective for customers with high Recency and Frequency scores. Market studies demonstrate that customers who have bought recently are more likely to buy again, immediately after the first purchase. As a result, it could be more meaningful to promote, for example, products that are associated with the initial purchase.
- Discounts and periodic promotions
And what can a brand do for a customer who buys frequently and has purchased something recently? This is the loyal customer, who doesn’t only buy during sales.
As a result, they may be interested in receiving communications that keep them up-to-date about new collections, events connected to the brand and potential offers or special promotions only intended for TOP customers.
As the previous examples demonstrate, every customer behaves differently, according to their diverse needs. It is essential that a brand is able to communicate with each one of them in the most personalized way.
RMF helps a brand know its customers better, and design individually tailored messages and journeys.
For a more technical and analytical RFM view read the article about how the RFM model works with real data.