How to Incorporate RFM Segmentation With Predictive Models
January 7, 2009 By Roy Wollen And Joe Boland, Assistant Editor, Target Marketing• Seasonality makes defining the recency ranges challenging. Does your zero- to 12-month range include “back to school,” Valentine’s Day” and “holiday shopping”?
• Augment RFM where you can if it helps to understand your business dynamics. Direct marketing innovator Bob Kestnbaum pioneered the concept of RFM, adding a “product” dimension to RFM. Kestnbaum called it “FRAC,” where “F” was frequency (the first and most predictive variable in his mind), “R” was recency, “A” was average dollars and “C” was category of purchase. The best predictor of future product purchases is past product purchases. This addresses the challenge of seasonality. Your key business levers also are viable candidates to extend the simple RFM summary. For B-to-C direct marketing, this might be RFMI (income); for B-to-B, this might be RFMI (industry). Both sectors might see benefits in an RFMC (channel) segmentation scheme.
• RFM also can be put to work to acquire new customers. There is some current thinking on calculating RFM for each ZIP code on file to target prospects based on observed RFM for customers that live in the same neighborhoods.
Roy Wollen is CEO and managing consultant for Database Insight Inc., a Chicago-based direct and interactive marketing firm. Contact him at roy@databaseinsight.com or call (312) 629-5043.
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