Developed some decades ago, the RFM Analysis is a great marketing model used to segment a customer list based on their behavior. This method has proven itself time and time again, by helping direct and online marketers minimize their costs while, at the same time, maximizing their returns.
The RFM Analysis is based on the Pareto principle, or the 80/20 rule, which states that 80% of the effects come from 20% of the causes. This principle is found everywhere in nature and holds equally as well in economics. With RFM, we are able to deduce who are those 20% of clients that generate 80% of the revenue and then use that information to our advantage.
How Does It Work?
The secret to the RFM Analysis lies in its name. Recency, Frequency, and Monetary (RFM) are the three criteria used to rank individual customers, based on their past purchasing behavior. The model looks at the most recent purchase date, the number of transactions over a given time period, and the total revenue attributed to each person. The customers with the highest scores are the previously-mentioned 20%.
With this information, businesses can better understand their clients by seeing what products they are more interested in, and who are the likeliest to purchase in the future. Direct marketers have used this technique for decades by sending catalogs to those who would be the most inclined to buy, while at the same time, formulating better strategies on how to engage the rest.
The order in which these criteria are set is pivotal in getting the most accurate results. Recency is the first on the list because it shows the time since the last purchase. It is a well-known fact that a customer who has recently interacted with your business is more likely to interact with it again. Likewise, the more time it takes someone to return to your services, the less likely it is that he or she will do so again. But while Recency offers valuable insight into your client’s behavior, it cannot distinguish between an old and a new customer. Here is where Frequency comes into play.
After learning who are the most recent customers, we need to know the Frequency in which they use your services. This step also weeds out first-time clients and differentiates between those who buy regularly and those who don’t. This parameter is especially important since it can also predict a pattern of future purchases, allowing you to prepare accordingly.
The Monetary aspect of the formula indicates the total money spent by a client over a given period of time. This final criterion gives depth to all the data from before and adds an extra level of detail about your clients. This metric, in combination with the frequency of purchase, can generate some surprising results. Your heaviest spenders, for instance, may not actually be your most loyal customers, nor even in your top 20 percent of revenue generators.
By knowing your best client’s purchasing behavior, you can formulate a cohesive strategy aimed at those who are not yet loyal to you. In addition, you can now use this information to improve the efficiency of your marketing campaigns, increase its relevance to the customer, reduce overall costs, and increase sales.