# Quick Profits with RFM Analysis

It often comes as a shock to people new to direct marketing that the response rates are so low. Successful, profitable promotions often result from sales to 2% or less of the mailed universe. Database marketers, today however, are finding that they can greatly increase these response rates in marketing to their existing customers by use of Recency, Frequency, Monetary (RFM) analysis. The results are nothing short of amazing. Let me give you one example.
An educational products company in the South had a two million name customer database, built up from sales over a five year period. Every spring they mailed their entire list with an attractive video offer which regularly got a response rate of about 1.3%. It did not produce much profit, but moved a lot of product. Last year, one of their marketing officers went to a seminar where he learned about RFM. On his return he directed his programmers to code the customer database for RFM, creating 125 RFM “cells”. He did a promotion to a representative test sample of 30,000 which produced a net loss. From that test, however, he learned the response rates of each of the 125 “cells”. For his rollout, he mailed only 554,000 of the two million who were in the 34 cells that did better than break even on the test. The result: a response rate of 2.76% and a net profit of \$307,000.
His experience is not unique. All across America, database marketers are waking up to the gold mine in their customer databases that can be opened up using RFM. In this article, we will explain the principles behind RFM, and detail some of the research that is currently being conducted in this field.
How RFM Works
RFM has been used in direct marketing – particularly by non-profits – for more than thirty years. It is based on both appropriate reasoning and empirical evidence of customer behavior. People who have bought from you recently are much more likely to respond to a new offer than someone who had made a purchase in the distant past. This can easily be illustrated by anyone with a customer database that includes purchase history. The database has to keep one piece of information in every customer record: the most recent discretionary purchase date. The database is sorted by that date, and the top 20% (in terms of recency) is given a code of “5”. The next 20% in terms of recent purchases is coded as “4”, etc. Everyone in the database now is either a 5, 4, 3, 2, or 1 in terms of recency. If you now make a test promotion to a representative sample, you will get a response that looks like this:
This graph, like the others in this article, is taken from actual results obtained on mailings done in 1995. It shows that the response rate from the top quintile (20% group) was 3.49%, while the next quintile responded at a rate of 1.29%. This clear trend of response rates by RFM quintile is almost universal across all products and services, all industries and all types of customers. It is one of the few “constants” in the marketing world. Someone who has just purchased insurance from you is much more likely to buy another policy than someone whose last insurance purchase was many months or years in the past.
Frequency Is Less Powerful
If your database keeps track of the number of transactions with your customers, you can also code your customers by frequency. Sorting the database by this number – from the most to least frequent, coding the top 20% as “5”, and the less frequent quintiles as 4, 3, 2, and 1. A promotion to your customer base will produce the following results:
You will notice right away that frequent buyers respond better than less frequent buyers, but the differences are much less pronounced than those for Recency. That is why RFM is RFM instead of FRM or some other combination. Notice in particular that the lowest quintile in frequency did better than quintile number 2. Why should that be? For a simple reason. Brand new customers have a recency code of “5” but a frequency code of “1”. So the lowest frequency quintile always contains the new customers – who are your best responders.
Monetary Is Almost Flat
When you code customers by the total dollar sales (average by month, year, or since the beginning of time) giving the big spenders a “5”, and the others, 4, 3, 2, and 1, you will get a response rate that resembles this:
Monetary, you see, does show differences between quintiles, but they are far from as dramatic as those for Recency. Is this true for all products and services? Not necessarily. If you are selling mutual funds, you might get a much better response rate from your big spenders than the small spenders, simply because they may be in a position to buy more. But that is not necessarily a firm rule. Response does not measure ability to respond as much as willingness to open the envelope and read the contents. That willingness is not necessarily a function of the size of one’s bankroll. Why should a customer with a million dollars respond better than a customer with \$10,000? It is unlikely that she will. The average millionaire is deluged with more offers than the average person, so getting through to her is really much tougher.
Putting The Three Codes Together

RFM analysis depends on recency, frequency and monetary measures, but the real power of the technique comes from combining them into a three digit RFM “cell code”. Using the quintile system explained above, all customers end up with three digits in their database records. They are either 555, 554, 553, 552, 551, 545,…down to 111. There are 125 different “cells” in all. If the coding is done correctly (see side bar on sorting methods), all cells will have virtually identical numbers of customers. If your database has one million customers, each cell will contain exactly 8,000 customers. Using these three digit codes you can turn any test into a highly profitable rollout. Here’s how it is done.
Using An Nth As A Test

From your RFM coded database, pick out a test group. Let’s say that you select 30,000. If you have 125 cells, each cell will contain 240 customers. Mail an offer to these 30,000 customers, and keep track of the response rate of each cell. Here are the results of a mailing in 1995 to 30,000. The response rates varied from 8.33% down to 0.00%. The top ten cells looked like this:
 555 8.33% 554 6.66% 553 5.42% 552 4.17% 551 4.58% 545 3.75% 544 5.00% 543 2.50% 542 4.17% 541 2.92%
When all the responses from all the cells were graphed and indexed for break-even, the responses looked like this:
Only 34 out of the 125 cells did better than break even. Break even means that the net revenue (profit) from sales to members of the cell exactly paid for the cost of mailing to the cell members. Once you know how each cell on the test responded to your offer you have some very powerful information: you know how each cell in your unmailed database will respond to the same offer. You make your profits by not mailing to the losing cells. Depending on the circumstances, you can double, triple or quadruple your response rates.
Contrast RFM To Demographic Modeling

Probably the greatest single advantage to RFM analysis is that anyone can do it. You don’t need to be a statistician, or to hire a modeler to do the analysis. It can all be done on a spreadsheet. The results are amazingly accurate. Take a look at the following graph which compares the predicted response rates of the 34 cells that were mailed in the rollout with the actual response rates achieved:
 Only 34 out of the 125 cells did better than break even. Break even means that the net revenue (profit) from sales to members of the cell exactly paid for the cost of mailing to the cell members. Once you know how each cell on the test responded to your offer you have some very powerful information: you know how each cell in your unmailed database will respond to the same offer. You make your profits by not mailing to the losing cells. Depending on the circumstances, you can double, triple or quadruple your response rates. Contrast RFM To Demographic Modeling Probably the greatest single advantage to RFM analysis is that anyone can do it. You don’t need to be a statistician, or to hire a modeler to do the analysis. It can all be done on a spreadsheet. The results are amazingly accurate. Take a look at the following graph which compares the predicted response rates of the 34 cells that were mailed in the rollout with the actual response rates achieved:

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Arthur Middleton Hughes has published over 200 articles on Database and E-mail Marketing. Click Here to read them.

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