RFM is a method used for analyzing customer value. It is commonly used in database marketing and direct marketing and has received particular attention in retail and professional services industries. RFM stands for Recency - How recently did the customer purchase? Frequency - How often do they purchase? Monetary Value - How much do they spend? Most businesses will keep data about customer purchases. All that is needed is a table with the customer name, date of purchase and purchase value. One methodology is to assign a scale of 1 to 10, whereby 10 is the maximum value and to stipulate the a formula by which the data suits the scale. For example in a service based business you could have: Recency = 10 - the number of months that have passed since the customer last purchased Frequency = number of purchases in the last 12 months (maximum of 10) Monetary = value of the highest order from a given customer (benchmarked against $10k) Alternatively, one can create categories for each attribute. For instance, the Recency attribute might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may be arrived at by applying business rules, or using a data mining technique, such as CHAID, to find meaningful breaks. Once each of the attributes has appropriate categories defined, segments are created from the intersection of the values. If there were three categories for each attribute, then the resulting matrix would have twenty-seven possible combinations (one well-known commercial approach uses five bins per attributes, which yields 125 segments). Companies may also decide to collapse certain subsegments, if the gradations appear too small to be useful. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value). Identifying the most valuable RFM segments can capitalize on chance relationships in the data used for this analysis. For this reason, it is highly recommended that another set of data be used to validate the results of the RFM segmentation process. Advocates of this technique point out that it has the virtue of simplicity: no specialized statistical software is required, and the results are readily understood by business people. In the absence of other targeting techniques, it can provide a lift in response rates for promotions.