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The value of RFM (Recency, Frequency, Monetary) analysis as a method to identify high-response customers in marketing promotions, and to improve overall response rates is well known and is widely applied today. RFM has been around for more than forty years. Less widely understood, however, is the value of applying RFM scoring to a customer database and measuring how customers migrate from cell to cell over time. This article examines one approach to an RFM Migration analysis which was applied recently in a segmentation exercise at Federal Express. The analysis explained herein actually took place during the past year. The numbers used have been disguised to protect the confidential results of this program.

Why RFM works.

Customers who have purchased from you recently are more likely to respond to your next promotion than those whose last purchase was further in the past. This is a universal principle which has been found to be true in almost all industries: insurance, banks, cataloging, retail, travel, etc. It is also true that frequent buyers are more likely to respond than less frequent buyers. Big spenders often respond better than low spenders. These are the three simple principles lying behind RFM analysis. What skilled marketers have done is to take these three ideas and quantify them. They code all customers into RFM cells and examine the response rates of the customers in each cell when exposed to the same promotion. It is true, of course, that only a percentage of customers will make an additional purchase based on a new promotion. But, of those that do respond, the responses usually come from customers in higher ranking RFM cells. Federal Express has good data on customer purchase history. They used this data to code all customers by RFM. Then, they used this coding in a very interesting way.

Objectives

The goals of the analysis were to:

Identify patterns of migration from RFM cell to cell over time.

Determine the extent to which customer migration patterns fell into discernible clusters

Identify investment and marketing strategies appropriate to each migration cluster.

Assess the effectiveness of RFM migration vs. other segmentation and targeting strategies available for marketing promotions.

Migration means, of course, that some customers improve their performance over time. They move to a higher ranking RFM cell. Other customers regress to lower ranking RFM cells. Profitable marketing comes from anticipating the migration of groups of customers so that the marketing and service dollars are spent on higher value customers who will, in return, improve their spending and retention habits. Marketing dollars are thus not wasted on lower value customers who are less likely to migrate up.

The customers for this exercise at Federal Express were selected based on whether they had purchased any one of a particular “family” of express services in a two year period. Purchase transactions were summarized at a half year level for each of the four services. The analysis file included the summary transaction data along with other demographic or behavioral data such as SIC code, company size, age of account, discount status, and whether customers used a FedEx automation device for their shipping.

The first step was to determine which products to use in the migration analysis. Migration takes time. The goal was to select customers who used the service over both of the two years. The following chart shows the result of the preliminary analysis: