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RFM Segmentation – Recency, Frequency, Monetary (RFM)

RFM Segmentation – Recency, Frequency, Monetary (RFM)

Recency Frequency Monetary RFM Segmentation explained

RFM (recency, frequency, monetary) analysis or segmentation is a marketing technique used to determine which customers are considered best by examining how recently a customer has purchased (recency), how often they purchase (frequency), and how much they spend (monetary).

RFM segmentation is sometimes referred to as RFV or Recency, Frequency, Value in this case.

We use RFM as part of visit scheduling for our field-sales territory clients and also increasingly for franchisees wishing to understand customers in a deeper way.

RFM analysis is based on the marketing principle that “80% of your business comes from 20% of your customers” and is a very effective way to start to develop more productive and profitable sales and marketing strategies!

[pullquote style=”left” quote=”dark”]80% of your business comes from 20% of your customers[/pullquote]

For more than 40 years, direct marketers have deployed RFM to improve their targeting – often using an informal RFM analysis to target their communications to individuals most likely to make purchases (or donations).

The reasoning behind RFM is simple: people who purchased (or donated) once were more likely to purchase (or donate) again. With the advent of e-mail marketing campaigns and customer relationship management software, RFM ratings have become an important tool.

The reasoning behind RFM segmentation is simple: people who purchased (or donated) once were more likely to purchase (or donate) again. With the advent of e-mail marketing campaigns and customer relationship management software, RFM ratings have become an important tool.

Calculating RFM scores

There are many ways that RFM can be calculated and deployed.  One approach is to assign customers with a ranking number of 1,2,3,4, or 5 (with 5 being highest) for each RFM parameter.

The three scores together are referred to as an RFM “cell”. The database is sorted to determine which customers were “the best customers” in the past, with a cell ranking of “555” being ideal.

Whilst RFM analysis is an extremely powerful tool, a company must be careful not to over communicate to customers with the highest rankings. Also, customers with low cell rankings should not be neglected, but instead cultivated to become better customers. This information should be part of every good sales territory design.

Also, where you are developing strategies to promote specific products or services, the RFM analysis approach can be developed into more detailed segmentation.

For instance, your database could first be segmented by product (or service) group, then a Pareto split is applied to each segment to form two new segments (80% and 20% for example), each of which can be subjected to RFM categorisation.

Then, within the top RFM bands, customers can be scored using ‘savage’ scores that produce a ranking that takes account of the variance of customer spend within a segment. This approach will deliver a much more precise targeting solution.

Recency Frequency Monetary analysis RFM segmentation works

The value of RFM (Recency, Frequency, Monetary) analysis as a method to identify high-response customers in sales and marketing promotions, and to improve overall response rates, is well known and is widely applied today. Less widely understood, however, is the value of applying RFM segmentation and scoring to a customer database and measuring how customers migrate from cell to cell over time.

RFM Key facts

  • Customers who have purchased from you recently are more likely to respond to your next promotion than those whose last purchase was further in the past. This is a universal principle which has been found to be true in almost all industries: insurance, banks, cataloguing, retail, travel, etc. It is also true that frequent buyers are more likely to respond than less frequent buyers.
  • Big spenders often respond better than low spenders. These are the three simple principles lying behind RFM analysis. What skilled marketers have done is to take these three ideas and quantify them. They code all customers into RFM cells and examine the response rates of the customers in each cell when exposed to the same promotion.It is true, of course, that only a percentage of customers will make an additional purchase based on a new promotion. But, of those that do respond, the responses usually come from customers in higher ranking RFM cell

For more information about how RFM analysis and segmentation works please contact us for more information. To find out more about our data analysis services click here

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