RFM Segmentation

What is RFM Segmentation?

RFM analysis allows marketers to target specific clusters of customers with communications that are much more relevant for their particular behavior – and thus generate much higher rates of response, plus increased loyalty and customer lifetime value. Like other segmentation methods, an RFM model is a powerful way to identify groups of customers for special treatment. RFM stands for recency, frequency and monetary – more about each of these shortly.

Marketers typically have extensive data on their existing customers – such as purchase history, browsing history, prior campaign response patterns and demographics – that can be used to identify specific groups of customers that can be addressed with offers very relevant to each.

While there are countless ways to perform segmentation, RFM analysis is popular for three reasons:

• It utilizes objective, numerical scales that yield a concise and informative high-level depiction of customers.
• It is simple – marketers can use it effectively without the need for data scientists or sophisticated software.
• It is intuitive – the output of this segmentation method is easy to understand and interpret.

What are Recency, Frequency and Monetary?

Underlying the RFM segmentation technique is the idea that marketers can gain an extensive understanding of their customers by analyzing three quantifiable factors. These are:

• Recency: How much time has elapsed since a customer’s last activity or transaction with the brand? Activity is usually a purchase, although variations are sometimes used, e.g., the last visit to a website or use of a mobile app. In most cases, the more recently a customer has interacted or transacted with a brand, the more likely that customer will be responsive to communications from the brand.
• Frequency: How often has a customer transacted or interacted with the brand during a particular period of time? Clearly, customers with frequent activities are more engaged, and probably more loyal, than customers who rarely do so. And one-time-only customers are in a class of their own.
• Monetary: Also referred to as “monetary value,” this factor reflects how much a customer has spent with the brand during a particular period of time. Big spenders should usually be treated differently than customers who spend little. Looking at monetary divided by frequency indicates the average purchase amount – an important secondary factor to consider when segmenting customers.

Performing RFM Segmentation and RFM Analysis, Step by Step

The following is a step-by-step, do-it-yourself approach to RFM segmentation.

Note that with the aid of software, RFM segmentation – as well as other, more sophisticated types of segmentation – can be done automatically, with more accurate results.

Step 1

The first step in building an RFM model is to assign Recency, Frequency and Monetary values to each customer. The raw data for doing this, which should be readily available in the company’s CRM or transactional databases, can be compiled in an Excel spreadsheet or database:

• Recency is simply the amount of time since the customer’s most recent transaction (most businesses use days, though for others it might make sense to use months, weeks or even hours instead).
• Frequency is the total number of transactions made by the customer (during a defined period).
• Monetary is the total amount that the customer has spent across all transactions (during a defined period).

Step 2

The second step is to divide the customer list into tiered groups for each of the three dimensions (R, F and M), using Excel or another tool. Unless using specialized software, it’s recommended to divide the customers into four tiers for each dimension, such that each customer will be assigned to one tier in each dimension:

This results in 64 distinct customer segments (4x4x4), into which customers will be segmented. Three tiers can also be used (resulting in 27 segments); using more than four, however, is not recommended (because the difficulty in use outweighs the small benefit gain from the extra granularity).

As mentioned above, more sophisticated and less manual approaches – such as k-means cluster analysis – can be performed by software, resulting in groups of customers with more homogeneous characteristics.

Step 3

The third step is to select groups of customers to whom specific types of communications will be sent, based on the RFM segments in which they appear.

It is helpful to assign names to segments of interest. Here are just a few examples to illustrate:

• Best Customers – This group consists of those customers who are found in R-Tier-1, F-Tier-1 and M-Tier-1, meaning that they transacted recently, do so often and spend more than other customers. A shortened notation for this segment is 1-1-1; we’ll use this notation going forward.
• High-spending New Customers – This group consists of those customers in 1-4-1 and 1-4-2. These are customers who transacted only once, but very recently and they spent a lot.
• Lowest-Spending Active Loyal Customers – This group consists of those customers in segments 1-1-3 and 1-1-4 (they transacted recently and do so often, but spend the least).
• Churned Best Customers – This segment consists of those customers in groups 4-1-1, 4-1-2, 4-2-1 and 4-2-2 (they transacted frequently and spent a lot, but it’s been a long time since they’ve transacted).

Marketers should assemble groups of customers most relevant for their particular business objectives and retention goals.

Step 4

The fourth step actually goes beyond the RFM segmentation itself: crafting specific messaging that is tailored for each customer group. By focusing on the behavioral patterns of particular groups, RFM marketing allows marketers to communicate with customers in a much more effective manner.

Again, here are just some examples for illustration, using the groups we named above:

• Best Customers – Communications with this group should make them feel valued and appreciated. These customers likely generate a disproportionately high percentage of overall revenues and thus focusing on keeping them happy should be a top priority. Further analyzing their individual preferences and affinities will provide additional opportunities for even more personalized messaging.
• High-spending New Customers – It is always a good idea to carefully “incubate” all new customers, but because these new customers spent a lot on their first purchase, it’s even more important. Like with the Best Customers group, it’s important to make them feel valued and appreciated – and to give them terrific incentives to continue interacting with the brand.
• Lowest-Spending Active Loyal Customers – These repeat customers are active and loyal, but they are low spenders. Marketers should create campaigns for this group that make them feel valued, and incentivize them to increase their spend levels. As loyal customers, it often also pays to reward them with special offers if they spread the word about the brand to their friends, e.g., via social networks.
• Churned Best Customers – These are valuable customers who stopped transacting a long time ago. While it’s often challenging to re-engage churned customers, the high value of these customers makes it worthwhile trying. Like with the Best Customers group, it’s important to communicate with them on the basis of their specific preferences, as known from earlier transaction data.

Of course, deciding which groups of customers to target and how to best communicate with them is where the art of marketing comes in!

Caveats of RFM Segmentation and RFM Model

RFM segmentation is a straightforward and powerful method for customer segmentation. However, the fact that the RFM model only looks at three specific factors (albeit important ones) means that the method may be excluding other variables that are equally, or more, important (e.g., products purchased, prior campaign responses, demographic details).

Also, RFM marketing is, by its nature, an historical method: it looks at past customer behavior that may or may not accurately indicate future activities, preferences and responses. More advanced customer segmentation techniques are based on predictive analytics technologies that tend to be far more accurate at predicting future customer behavior.

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