RFM stands for Recency, Frequency and Monetary Value.

• Recency - When was the last order?
• Frequency - How many orders have they placed with us?
• Monetary Value - What is the value of their orders?

It has been used by direct marketers for over 40 years as a segmentation tool to increase Return on Marketing Investment. The basic premise of RFM is that customers who have purchased more recently, more frequently and have spent more with your company are your best prospects for future direct marketing campaigns.

Interestingly, if you attend direct and interactive marketing seminars or conferences around the world, you will hear marketers of all sorts namedrop RFM. However, I - for one - am convinced that most have never bothered to actually understand what RFM really is all about, let alone applied this highly effective model in practice.

Like data mining/response modeling, the goal of RFM is to increase Return on Marketing Investment (ROMI - also known as Marketing ROI) by communicating only with the customers that are Most Likely to Respond (MLR - that’s I own that acronym now).

Creating an RFM analysis
To create an RFM analysis, one creates 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.

RFM has the virtue of simplicity: no specialized statistical software is required, and the results are readily understood by marketing people. In the absence of other targeting techniques, it can provide a lift in response rates for direct marketing and promotions.

Done well, you increase your ROMI as you attain almost the same number of sales by contacting only a fraction of your customer base.

MORE RESSOURCES:

If you want to learn more, why not start by downloading Mr. Jim Stafford’s RFM Whitepaper right here (no registration required).

Go to Fokus Integrated to see what they have to say about the matter

Read this highly interesting article “Quick profits with RFM Analysis by Arthur Middleton Hughes found at the Database Marketing Institute

or this article: RFM Migration Analysis: A New Approach to a Proven Technique by Jim Sellers and Arthur Middleton Hughes