Thursday, March 31, 2011

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How to apply RFM to your database marketing

Posted by Sundeep Kapur | Thursday, March 31, 2011


RFM or Recency, Frequency, & Monetary - these are three typical measures of how a direct marketer targets their customer. Recency refers to how long ago your customer purchased from you, Frequency refers to how often the customer has purchased, and Monetary refers to the amount of money that the customer has spent with you over time.

All three are key drivers of how lists are put together by direct marketers. This is how catalogs are typically mailed. Customers are assigned scores from 1 - 5 for each category. So a very high Monetary customer might get 5 compared to a low value customer who gets a 1. Similarly, a recent transaction might get a 5 and very frequent buyer might get a 5 with the 1's being given to those that score low.

If you are a bank or a credit union the same formula can apply. New customers and interactions equal Recency; repeated transactions equal Frequency; more than one service relationship equals Monetary. The way a direct marketer can apply this formula depends on the business.

Marketers then assign these numeric values to their customers. A perfect customer score is 5 x 5 x 5 = 125. Similarly an average customer might be at 4 x 3 x 2 = 24. The scoring of these customers drives marketing programs.

The high scoring customers are well treated and courted often. Monetary is very important and it shows the lifetime value of that customer - this is the ideal customer. As Frequency builds up it shows that the marketer is beginning to earn the business of the customer. From a one time purchase the transactions continue and the overall Monetary value adds up.

Recency is all about timing. The more recent customers are often courted with offers in attempt to drive the next transaction, followed by more, ultimately leading to the 'ideal' high Monetary customer.

Apply these simple tips to build up a simple RFM scoring model for your customers. Of course there are other factors that drive how data is stored and prospects are scored. Also, don't make the same offer to everybody - think about scoring your prospects.

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