Using RFM to Identify Your Best Customers
Posted by Sridhar Mutyala at 05:45 PM · 4 Comments

80% of your sales come from 20% of your customers. As a small business owner, even if you’ve never heard of the Pareto Principle, you know this rule of thumb intuitively. You’re in business largely because of the support of a fraction of your customer base: your best customers.

From a marketing perspective, it makes sense to put in the effort to understand the characteristics and preferences of your best customers for at least two reasons: 1) to continue to provide this group with what they’re looking for and keep them as customers, and 2) to target your marketing efforts toward prospects who resemble your best customers.

By targeting your acquisition marketing through insights into your best customers, you attract customers who are likely to respond to the strengths of your small business and remain loyal to it. Instead of moving random customers up loyalty ladders, you focus instead on getting the right customers, customers who will be loyal from the start.

But, before you can start to understand your best customers, you first need to identify them. And that’s where a simple database marketing tool called recency, frequency, monetary analysis (or RFM) comes in handy.

RFM in a nutshell

RFM uses sales data to segment a pool of customers based on their purchasing behavior. The resulting customer segments are neatly ordered from most valuable to least valuable. This makes it straightforward to identify best customers.

The idea behind RFM is quite simple: 1) Customers who have purchased from you recently are more likely to buy from you again than customers who you haven’t seen for a while. 2) Customers who buy from you more often are more likely to buy again than customers who buy infrequently. 3) Customers who spend more are more likely to buy again than customers who spend less.

The order of the attributes in RFM corresponds to the order of their importance in ranking customers. Recency is the most important factor. Why?

Because the longer it takes for a customer to return to your business, the less likely he or she is to return at all. You can fix problems with good customers not coming in as often or spending as much, mostly because they’re still coming in. But when good customers stop coming in altogether — that problem is much harder to fix.

Recency alone won’t sort out your good customers from your new ones. You need frequency for that. Frequency measures the intensity of a customer’s relationship with your business. And good customers, by definition, do business with you more often. You’re part of their habit.

How much a customer spends on average or in total is the final measure of his or her value. The M in RFM adds another level of detail to the customer picture, helping you distinguish between relatively light and heavy spenders. Its effect is often, but not always, highly correlated with frequency.

Calculating RFM scores

To calculate RFM scores, you first need the values of three attributes for each customer: 1) most recent purchase date, 2) number of transactions within the period (often a year), and 3) total or average sales attributed to the customer (total or average margin works even better).

You then have to decide the number of categories for each RFM attribute. The number is typically 3 or 5. If you decide to code each RFM attribute into 3 categories, you’ll end up with 27 different coding combinations ranging from a high of 333 to a low of 111. Generally speaking, the higher the RFM score, the more valuable the customer.

You can assign customers to categories by sorting on RFM attributes or by applying business rules. For example, customers can be assigned to frequency category 3 if they have made 10 or more purchases in the past year, category 2 if they have made 3-9 purchases, and category 1 if they have made 1 or 2 purchases. Determining meaningful rules often requires a bit of data mining, however, so it’s common for RFM users to simply sort the customer file on each attribute and assign customers to categories from top to bottom (i.e., the top third of customers on frequency are assigned to category 3 and so on).

You also have to remember to sort your customers on recency first, then sort on frequency in each recency category, and, finally, sort on monetary value in each combination of recency and frequency categories. This way, you end up with an equal number of customers for each RFM score.

Using RFM scores

Once you’ve calculated RFM scores, it’s easy to identify your best customers — they have the highest score. You can now start to analyze the characteristics and purchasing behavior of this group and try to understand what distinguishes them from typical customers. Do they tend to buy a subset of your products or services? Do they live in demographically similar neighborhoods? Are their lifestyles and/or life stages similar? Why do they perceive more value in your business than the folks who you see once or twice?

The answers to such questions help you sharpen your understanding of your target market and be more precise in communicating with actual and potential customers. It’s a systematic approach to marketing, and it’s the approach we recommend to all our clients.

I’ve tried to suggest here that RFM is an effective and easy to implement method for segmenting a customer pool and identifying best customers. But I’ve avoided talking about using RFM to predict customer response for direct marketing campaigns.

The reason for that is that there are much more effective techniques than RFM for predicting customer behavior. The tradeoff with these more advanced methods is that, from a small business perspective, they are not easy to understand or implement.

As a predictive tool, RFM works — your best customers are more likely to respond to a direct mail offer than customers you rarely see. You should use RFM to focus your mailings on the most responsive segments in your customer database. But you should value the technique for what it is: merely the first step in using analytics to improve your decision-making.

4 Responses to “Using RFM to Identify Your Best Customers”

  1. Bill says:

    Hi Sridhar,

    Excellent article. “the longer it takes for a customer to return to your business, the less likely he or she is to return at all” I couldn’t agree more.

    At the bottom of the article you mention higher powered systems for finding top customers. Would you mind offering an example?



  2. Akshay says:

    Bill, you could try out cluster analysis. Its more complicated than RFM, but might yield better results.

  3. Ahmed Adeleke says:


    You use combination of both Descriptive modelling, to which cluster analysis belong to find and define the characteristics of your best or loyal customers and segment them. And Predictive modeling such as Decision Tree, Regression Analysis or Neural network to predict those prospective customers that are likely to buy or respond to your marketing mails.
    These processes are combined together to achieve RFM

  4. sivakumar says:

    Hi Sridhar,

    Nice Article!
    My question is suppose 3categories from each in RFM. Can i assume customers who get score > 300 are high valued customers. if not, what is score range for High valued customers.


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