Recency, Frequency, Monetary Value (RFM) Analysis

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A Recency, Frequency, Monetary Value (RFM) Analysis is a customer analysis that creates a customer recency score, customer frequency score and customer monetary value score.

References

2022

  • (Wikipedia, 2022) ⇒ https://en.wikipedia.org/wiki/RFM_(market_research) Retrieved:2022-5-2.
    • RFM is a method used for analyzing customer value. It is commonly used in database marketing and direct marketing and has received particular attention in retail and professional services industries. [1] RFM stands for the three dimensions: *Recency – How recently did the customer purchase? *Frequency – How often do they purchase? *Monetary Value – How much do they spend?

      Customer purchases may be represented by a table with columns for the customer name, date of purchase and purchase value. One approach to RFM is to assign a score for each dimension on a scale from 1 to 10. The maximum score represents the preferred behavior and a formula could be used to calculate the three scores for each customer. For example, a service-based business could use these calculations:

      • Recency = the maximum of "10 – the number of months that have passed since the customer last purchased" and 1
      • Frequency = the maximum of "the number of purchases by the customer in the last 12 months (with a limit of 10)" and 1
      • Monetary = the highest value of all purchases by the customer expressed as a multiple of some benchmark value
    • Alternatively, categories can be defined for each attribute. For instance, Recency might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may be derived from business rules or using data mining techniques to find meaningful breaks.

      Once each of the attributes has appropriate categories defined, segments are created from the intersection of the values. If there were three categories for each attribute, then the resulting matrix would have twenty-seven possible combinations (one well-known commercial approach uses five bins per attributes, which yields 125 segments ). Companies may also decide to collapse certain subsegments, if the gradations appear too small to be useful. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value). Identifying the most valuable RFM segments can capitalize on chance relationships in the data used for this analysis. For this reason, it is highly recommended that another set of data be used to validate the results of the RFM segmentation process.

      Advocates of this technique point out that it has the virtue of simplicity: no specialized statistical software is required, and the results are readily understood by business people. In the absence of other targeting techniques, it can provide a lift in response rates for promotions.

  1. ↑ Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430.

2021

  • https://www.investopedia.com/terms/r/rfm-recency-frequency-monetary-value.asp
    • QUOTE: Recency, frequency, monetary value is a marketing analysis tool used to identify a company's or an organization's best customers by measuring and analyzing spending habits.
    • Key Takeaways
      • Recency, frequency, monetary value (RFM) is a marketing analysis tool used to identify a firm's best clients based on the nature of their spending habits.
      • An RFM analysis evaluates clients and customers by scoring them in three categories: how recently they've made a purchase, how often they buy, and the size of their purchases.
      • RFM analysis helps firms reasonably predict which customers are likely to purchase their products again, how much revenue comes from new (versus repeat) clients, and how to turn occasional buyers into habitual ones.
    • …
    • The RFM model is based on three quantitative factors:
      • Recency: How recently a customer has made a purchase
      • Frequency: How often a customer makes a purchase
      • Monetary Value: How much money a customer spends on purchases
    • RFM analysis numerically ranks a customer in each of these three categories, generally on a scale of 1 to 5 (the higher the number, the better the result). The "best" customer would receive a top score in every category.
    • These three RFM factors can be used to reasonably predict how likely (or unlikely) it is that a customer will do business again with a firm or, in the case of a charitable organization, make another donation.
    • The concept of recency, frequency, monetary value (RFM) is thought to date from an article by Jan Roelf Bult and Tom Wansbeek, "Optimal Selection for Direct Mail," published in a 1995 issue of Marketing Science.1 RFM analysis often supports the marketing adage that "80% of business comes from 20% of the customers."
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