Predictive modeling based on RFM scoring indicators RFM - is a ranking model when all customers are ranked according to their purchasing F requency, R recency and M monetary value. This indicator is highly used by marketing departments of various organizations to segment customers into groups according to customer value.

The question is following: are there any substantial models based on RFM scoring (or related to) which have solid predictive power?

Update:


predicting which customer will most likely spend more
who is going to upgrade/renew subscribtion/refund etc


Update2:


I understand, this is simple problem with three independent variable and one classifier. My guess and experience say these pure three factors do not predict future customer value. But they can be used together with another data or can be an additional input into some model.
Please share which methodologies worked for you personally and are likely to have high predictive ability. What kind of data you used together with RFM indicators and it worked well?
A Google search leads to many relevant resources that answer your question:


USING RFM DATA TO OPTIMIZE DIRECT MARKETING CAMPAIGNS: A LINEAR PROGRAMMING APPROACH http://www.thefreelibrary.com/Using+RFM+data+to+optimize+direct+marketing+campaigns%3A+a+linear...-a0272246211
Data Mining using RFM Analysis http://cdn.intechweb.org/pdfs/13162.pdf
Libby on Recency, Frequency, Monetary Value (book) http://www.amazon.com/Recency-Frequency-Monetary-Century-Library/dp/1882222067


From a data science point of view, there is nothing very special or unique about this problem. You have three independent variables and one dependent variable. Regression, clustering, and classification methods can be applied.