Product history is easier to understand, and widely used. The most typical use is for affinity analysis. You look at customer purchases of some products, and draw conclusions from these product purchases as to the customer's likelihood to buy other products. Airlines study customer travel patterns to make vacation offers Booksellers learn what books interest their customers from their existing purchases Transaction history is also very predictive, but less well understood. Frequent buyers respond better than less frequent buyers.
Big spenders respond better than small spenders.
Paul Wang and I give seminars several times a year for The Database Marketing Institute. Over the years, we have worked with many companies who have been experimenting with intelligent use of the RFM data contained in their customer databases. They have discovered some fascinating information through the simple graphing of customer RFM behavior. Let me give you a concrete example. I do consulting work with an art gallery in Amsterdam, Holland that is using RFM software developed at the Institute.