How to Use RFM Models in Data Driven Marketing
When data driven marketing was first coming into prominence, analysts developed a relatively simple targeting technique that is still widely used today. The technique was first developed for the catalog sales business. The motivation was that catalogs are expensive to print and ship, so it’s important to mail them to people who might actually use them.
The RFM framework in data driven marketing
The recency, frequency, monetary technique, known as RFM modeling, is based on looking at three facts about customer transactions.
R is for recency. How long ago did the customer last buy from you?
F is for frequency. How often or how many products did the customer buy?
M is for money. Well, actually it’s for monetary value but it means money. How much did the customer spend?
The basic idea is that each one of these factors individually is somewhat predictive of response rates. Combining them makes those predictions even better.
RFM models are developed using summarized transaction data. Transaction counts, purchase totals, and recent transaction dates are grouped into ranges. A simple RFM model might only distinguish high, medium, and low transaction volumes, for instance.
Each customer is ranked on each of the three attributes. Customers are then segmented based on their combined ranking. For example, one segment is made up of customers who fall into the low category on all three attributes. There is another segment for very recent, low volume, and high monetary value. And so on.
The number of segments gets big fast. If each attribute is split into three ranges, you end up with 27 distinct RFM groups. If you split them ten ways, you end up with 1,000 segments.
How to build the RFM model in data driven marketing
The real insight comes when you apply these segments to customers who have received marketing campaigns from you in the past. You look at the response rates for each of the RFM segments. Typically, some segments dramatically outperform others.
Like all models, you should test RFM models before using them in defining target audiences. The standard way of testing a model involves splitting up the customers you’re analyzing into two randomly defined groups.
You might be analyzing the response rates of 100,000 customers who received your spring campaign. You want to randomly split that group in half. You use the first half to do your analysis and define your high-performing segments. Then you use the second half to confirm (or not) that those segments really do perform better than the others.
You can accomplish this random split with a random number generator. Database software, analytic software, and even spreadsheets have functions that will produce random numbers between 0 and 1. The idea is that you generate a random number for each customer record. If the number is less than .5, you put the record in your analysis file. The rest of the records go into your confirmation or test file.
By focusing future campaigns on the high-performing segments, you can achieve higher response rates while reducing campaign costs. You should consider some technical issues when implementing any type of analytic model. For one thing, you don’t want to assume that your segments will perform as well in the future as they did in the past.