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A Few Words about RFM

What is it? How is it used?

RFM is a method used for analyzing customer value. It is commonly used in database marketingand direct marketing and has received particular attention in retail and professional services industries.

RFM stands for the three dimensions:

  • Recency – How recently did the customer purchase?
  • Frequency – How often do they purchase?
  • Monetary Value – How much do they spend?

Customer purchases may be represented by a table with columns for the customer name, date of purchase and purchase value. One approach to RFM is to assign a score for each dimension on a scale from 1 to 10. The maximum score represents the preferred behavior and a formula could be used to calculate the three scores for each customer. For example, a service-based business could use these calculations:

  • Recency = the maximum of "10 – the number of months that have passed since the customer last purchased" and 1
  • Frequency = the maximum of "the number of purchases by the customer in the last 12 months (with a limit of 10)" and 1
  • Monetary = the highest value of all purchases by the customer expressed as a multiple of some benchmark value

Alternatively, categories can be defined for each attribute. For instance, Recency 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 derived from business rules or using data mining techniques 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.

Source: Wikipedia

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