RFM Analysis

RFM Analysis aka Recency, frequency, monetary value is a marketing analysis tool used to identify an organization's best customers by measuring and analyzing spending habits. The RFM model is based on three quantitative factors:

• Recency: How recently a customer has made a purchase: The more recently a customer has made a purchase with a company, the more likely they will continue to keep the business and brand in mind for subsequent purchases. Compared with customers who have not bought from the business in months or even longer periods, the likelihood of engaging in future transactions with recent customers is arguably higher. Such information can be used to get recent customers to revisit the business and spend more. In an effort not to overlook lapsed customers, marketing efforts might be made to remind them that it's been a while since their last transaction, while offering them an incentive to resume buying.
• Frequency: How often a customer makes a purchase: The frequency of a customer’s transactions may be affected by factors such as the type of product, the price point for the purchase, and the need for replenishment or replacement. If the purchase cycle can be predicted — for example when a customer needs to buy more groceries — marketing efforts may be directed towards reminding them to visit the business when staple items run low.
• Monetary Value: How much money a customer spends on purchases: Monetary value stems from how much the customer spends. A natural inclination is to put more emphasis on encouraging customers who spend the most money to continue to do so. While this can produce a better return on investment in marketing and customer service, it also runs the risk of alienating customers who have been consistent but may not spend as much with each transaction.

RFM analysis numerically ranks a customer in each of these three categories, generally on a scale of 1 to 5 (the higher the number, the better the result). The "best" customer would receive a top score in every category. These three RFM factors can be used to reasonably predict how likely (or unlikely) it is that a customer will do business again with a firm or, in the case of a charitable organization, make another donation.

== Steps to Performing RFM Analysis 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:

Recency	Frequency	Monetary
R-Tier-1 (most recent)	F-Tier-1 (most frequent)	M-Tier-1 (highest spend)
R-Tier-2	F-Tier-2	M-Tier-2
R-Tier-3	F-Tier-3	M-Tier-3
R-Tier-4 (least recent)	F-Tier-4 (only one transaction)	M-Tier-4 (lowest spend)

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!