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Is RFM Useful in Email Marketing?

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by: Arthur Hughes (@IBMforMarketing)
14 February 2012

Direct marketers have been using recency, frequency, and monetary (RFM) analysis to predict customer behavior for more than 60 years. It’s one of the most powerful techniques available to a database marketer. Typically, you begin by sorting your customers by most recent purchase date, divide them into equal quintiles (20 percent in each) and assign numbers to the quintiles. The most recent 20 percent are 5, next 4, next 3, etc. You do the same thing by frequency of purchase and amount of spending.

You end up with a file of 125 exactly equal cells coded from 111 (responded a long time ago, and only once, and spent very little money) to 555 (very recent, very frequent and a high-dollar spender). You make money by sending your direct mail promotions only to those shown by RFM high numbers to be most likely to respond and buy. It works. Direct mail following RFM earns thousands or millions of dollars over almost any other way of mailing from your house file. Here’s a typical RFM response chart:

This chart shows the responses to a test mailing of 30,000 previous buyers divided into 125 RFM cells. Only 34 of the 125 cells broke even or better. The sales to the remainder did not pay the cost of the mailing to the customers in that cell. After the test, the direct mailer sent his roll out from his file of 2 million customers, only mailing to those half-million customers whose RFM codes showed them to be in the 34 winning cells. Result: what might have been a $164,000 loss turned into a $307,000 profit as shown on this chart:

So why isn’t RFM a winner with email? It’s because the key value of RFM is telling you whom not to mail to so as to save postage. With email marketing, the delivery cost is much lower and can be ignored as a constraint on mailing a list. Those coded 111, who would be skipped in a direct mail promotion, could still produce profits from an email campaign. The cost of mailing them is so small that if even one of them buys something, it will probably pay for the emails to everyone in the 111 cell.

Despite this, hundreds of marketers who grew up in the direct mail industry are using RFM for email marketing. They look very professional with their RFM coding and response charts. What they cannot do, however, is show how RFM is making money over other ways of segmenting their house file. A better option than RFM is customer segmentation by the type of product they bought last or when they made their last purchase. Sending emails that contain dynamic personal content will get your emails opened. Using the customer’s previous history in your email will get that person to click on your links. If you know, for example, that some of your customers are college students, others are women with small children, and a third group are empty nesters over 55, and can create different content for each group, you may increase sales far more than any mailing based on RFM codes.

While traditional RFM isn’t ideal for email marketing, the concept of scoring buyers is already heavily in use, especially by B2B marketers who score prospects based on their firmagraphics and interaction with their website, emails, event attendance, content, etc. We believe this will emerge as a next wave for B2C marketers as well.

What’s starting to be increasingly used today by savvy marketers are behavior-based triggers. In other words, instead of using a complex RFM model, marketers can use the essence of the “R” in RFM to identify a customer that’s engaged—e.g., abandoned a shopping cart, clicked on a specific product link in an email, or visited a specific product page on your website. Then you send them an automated, triggered message based on that action. POW! Your open, click and conversion rates will go through the roof.

Related Resources:
1) Blog: “Contact Scoring: Your 10-Step Quick-Start Process
2) White paper: “How Marketing Automation Improves Efficiency and the Buyer Experience
3) Blog: “Post-Purchase Emails that Drive Higher Revenue, Engagement


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