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Recency-Frequency-Monetary Value Modell

Segmentation and evaluation of customer potential - Part 1

Evaluation of customers and customer relationships has always been a challenge for marketing departments. At the same time, it is an important steering element for communication strategy, media planning and budgeting. In this series of articles, we will describe different approaches to customer evaluation and highlight their advantages and disadvantages.

In the first part, we describe the Recency-Frequency-Monetary Value model (RFM), which offers a quantitative approach to customer segmentation.

In short

1. valuation of customers is substantial for both planning and communication strategy.

2. Recency-Frequency-Monetary Value models (RFM) provide a streamlined and simple approach to assessing these segments. 

3. Recency describes the time since the last purchase, frequency the frequency of repurchase and monetary value the user’s shopping basket.

4. Based on the purchase data of users, clear segments can be created, which can be tailored to the respective business case.

The famous Pareto principle states that 20% of customers are responsible for 80% of a company’s turnover. Determining which of the customers are this 20% is helpful on many levels. From cluster analyses to complex grouping methods, there are various methods to achieve this goal. The Recency-Frequency-Monetary Value (RFM) principle is a clear and well thought-out variant that covers the essential aspects of customer classification and enables segmentation.

As can already be deduced from the name, RFM combines three different factors of customer evaluation. Recency refers to the period of time since a customer’s last purchase. A possible measure of this is the number of weeks that have passed since the purchase or alternatively a standardised scale of 1-10 such as “10 minus the number of months since the last purchase”, whereby a higher rating classifies the customer as “more important”.

Frequency is the measure of the number of purchases within a certain period of time and is intended to measure the loyalty of a customer. Which period is chosen here depends on the company and the product; for example, cars are bought much more irregularly than electronics. For example, electronics are bought much more irregularly than cars. While purchases within the last year could be used for electronics, the period for car sales should be longer.

The monetary value aspect includes the value of the “shopping basket” in the segmentation. One possible measure is the average price of a product purchased or the most expensive product purchased within a period of time. For the purpose of standardisation, this value could be compared with a company-specific benchmark value (e.g. benchmark = monetary value 5; the more, the higher score, below lower).

The RFM analysis combines the scores of the three factors for each customer and ranks them accordingly. A customer who scores high in all three categories, i.e. buys frequently, recently and with a high value of goods, is therefore a “premium” customer who has a high loyalty to the company and a high demand for the products.

The model does not make a value judgement between the factors. Two customers with the same monetary value, one with higher frequency and the other with higher recency, are rated as equally important by the model. The simpler the scoring system of the individual factors, the easier it is to group customers, for example into important customers, growth customers, inactive customers, vulnerable customers and less important customers who rarely buy products for little money. Divided into high and low recency, the following two graphs show exemplary segmentations, which serve as a template and need to be adapted depending on the company.

Conclusion

RFM analysis is a good way to use existing purchase data to divide customers into clear segments. Based on the three evaluation factors, a transparent and comprehensible customer grouping is created, on the basis of which marketing measures can be adapted to the customer groups. Although the model only offers a rough overview, it can be supplemented with further factors without much effort and if the data is available, and thus contribute to an even more precise customer categorisation. For companies that have not yet carried out any customer group segmentation, the RFM analysis offers an excellent starting point.

Sources

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