Customer segmentation using RFM in SAS Enterprise Guide

Recency, Frequency, and Monetary Analysis (or RFM) is a popular customer segmentation technique employed by database marketers everywhere. Marketers use RFM to identify which customers are most likely to respond to a direct marketing campaign. The model takes into account three simple metrics:

How recently did the customer buy from you?

How frequently does the customer buy something from you?

How much money does the customer spend on your products?

Each metric receives a value of 1 through 5. The result is 125 "bins" of customers (because 125 is 53). Those with higher RFM scores are considered more likely to respond to a campaign...potentially.

For years, SAS customers have used special tools like SAS Enterprise Miner to compute RFM. With SAS 9.4, the RFM algorithms are built into Base SAS, and there's an easy-to-use task in SAS Enterprise Guide. (The task is also available in the SAS Add-In for Microsoft Office.) You can find the task in the menus at Tasks->Data Mining->Recency, Frequency, and Monetary Analysis.

Note: To use the task, you must have SAS 9.4 and SAS Enterprise Guide 6.1 (or SAS Add-In for Microsoft Office 6.1). Despite the "Data Mining" category, this task does not require SAS Enterprise Miner.

As an example, suppose you have transaction data that looks like the following. You need only the 3 fields -- a customer ID, a transaction date, and a transaction amount (value):

From this, RFM calculates "scores" for each customer. The customers with the highest scores will probably be those that spent the most with you, across the most recent and frequent dates. The idea behind RFM is that a minority of customers are responsible for a majority of your business. RFM scores provide visibility into who those valuable customers are. Here's an example of the scored data, summarized at the customer level:

The RFM task supplies several useful charts. Here's a "monetization map", which summarizes the monetary values for each combination of frequency and recency scores. You might use this to help identify a "sweet spot" of customers that you want to target.

Next, let's look at a paneled bar chart of the Frequency by Recency segments. The bar on the lower right corner indicates that there are a handful of customers who made several purchases in the past (high frequency), but that was a long time ago (not recent). Perhaps that's a good target segment for a "Come back and see us -- we miss you" campaign. Contrast this with the bar on the upper right, which shows the 60 superfans: the customers who bought lately and often. You can decide whether to "go back to the well" with this group in the next campaign, or save the campaign expense as they might buy from you anyway, without prompting.

RFM scores are just one small part of planning a campaign. The "Recency, Frequency, and Monetary Analysis" task is a good start, but eventually you might want to factor in other criteria.

After all, direct marketing has many nuances, such as cross-referencing with opt-out lists and taking steps to avoid "overmarketing" to any one segment. Tracking response rates, testing campaigns, and the actual campaign workflow are also essential elements. When you're ready, SAS Customer Intelligence offers an integrated set of applications for all of these aspects.