How to Incorporate RFM Segmentation With Predictive Models
With predictive models all the rage in direct marketing analytics these days, recency-frequency-monetary value segmentation may seem outdated. But the truth of the matter is, RFM still has a place in modern database marketing when used side by side with predictive models. After all, the best predictor for future behavior is past behavior, and that’s exactly what RFM offers.
RFM should be used in concert with predictive models to optimize database marketing plans. Generally, predictive models do a superior job of predicting sales and have better ROI than segmentations based on RFM. But RFM is still useful to help validate predictive models before you contact customers.
For example, when your database firm brings you attractive statistics, you should verify those statistics before adjusting your marketing plan. Review the sample sizes, statistical and sampling techniques, treatment of outliers, treatment of incomplete information (blanks, nulls, garbage), transformation of variables, etc. Then, you should ask for a cross-tab comparing its score versus RFM data.
The cross-tab should contain not only population counts, but also ratios such as life-to-date (LTD) dollars/buyers, average order size, average days since last purchase and LTD orders/buyer. Look for a correlation between the best scores and the best ratios. If there are attractive ratios in the basement of your predictive model, you may be missing out on opportunities with good customers.
While RFM provides a broader view of segments than predictive models, it also has some advantages over them. For instance, you can build it yourself and do so quickly; it’s portable across industries; can quantify seasonality issues to a degree; applies to all customers in your database; and can be used for reactivation, cross-sell, renewal and acquisition campaigns.
Certainly, RFM is not a replacement for inferential statistics from predictive models. Rather, it should add value in its unique ability to amplify management understanding, ensure data quality and substitute for predictive models when models are not practical.
Consider these best practices when applying RFM to your segmentation process:
• Use common sense. With durable goods and large ticket items (i.e., cars, furniture), RFM may actually work in reverse (where less recent is better than more recent). This also may be true with seasonal business. Be flexible in how you operationalize the values within an RFM model. For example, Web marketing and e-commerce have their own RFM. Recency may be measured in days, not months. Frequency may turn into how frequently visitors return to your site, what they purchase or view, and where they click. Monetary may be extended to include what’s in the shopping cart (before abandons).
• 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 email@example.com or call (312) 629-5043.