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The RFM analysis, or recency, frequency and monetary value, has been an essential technique for customer information in the database marketing world for ages, but so many marketers continue to forget the fundamentals regarding the method. The RFM model seeks to increase profits from the targeted consumer base through consistently updating and reorganizing customer information. It functions from a recency code, or five numbers showing a client’s purchasing history, and is traditionally found in direct mail marketing. Although there are newer techniques, such as employing demographic models and cluster coding, RFM continues to be one of the most important components of a database for driving sales and improving consumer knowledge.


Understanding the last time a particular client bought products is essential to the RFM technique. According to marketing experts, customers who purchased within the last 30 days will traditionally be the most likely to buy again and should be the first step within database marketing. Many marketing professionals define the most recent consumers as those who bought within the past three months and tend to develop a recency code on this idea.

However, according to the Arthur Hughes of the Database Marketing Institute, creating a recency code on the 0-3 month model can cause future problems due to seasonal buying. Hughes suggests marketers think about constructing the recency code around customer responses after surveying clients on how often they are likely to buy. But many database marketers have continued to find that keeping to the traditional idea of recency is true throughout all industries.


Marketing to existing customers who have interacted with the firm more than once within a given time period, such as 12 months, can also increase sales productivity through database marketing. Those one-time buyers can eat up valuable database space and marketing time, while current consumers with more than one purchase and new leads within the target audience deserve more attention. The frequency element of RFM looks at the list of clients and the frequency code determines which customers are most likely to return. Hughes recommends marketers shape the frequency code after the recency code, as they determine each other.

“Recency is a more powerful predictor of customer response than frequency,” Hughes writes in an article. “In your business, that may not be true, but you will certainly be the exception to the general rule.”

The two different codes rank customers and allow for marketers to see not only who purchases the most but also when they buy and what drives them to choose a specific company. Recency and frequency assists in the collection of customer information and knowledge for businesses and can be easily understood.

Monetary value

Every marketing campaign comes down to finances, and the RFM model drives sales through the acquisition of customer knowledge. Gathering consumer behavior data assists marketers in identifying the target audience and may uncover surprises. Ellis advises businesses employing database marketing to measure consumers to see which marketing initiatives attract and keep the best buyers. Hughes also suggests developing the monetary code as a three digit code that updates once a month to help companies discern which customers have increased buying frequency and to track monthly improvements.

Database marketing continues to be a code driven technique, but understanding the RFM method can increase the efficiency of direct mail initiatives and drive sales. Businesses investing in database marketing might want to consider improving how often codes are being updated and analyze how the target audience is changing.

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