RFM method

RFM stands for recency, frequency, and monetary value. This technique utilizes these three metrics to evaluate customer behaviour and customer value. The RFM method is based on retrospective non-monetary (recency and frequency) and monetary (monetary value) data. This model is used widely in many areas, particularly in direct marketing (Wei, Lin, & Wu, 2010). The RFM model is easy to understand and can be quickly implemented by companies (Bacila & Radulescu, 2014) if the appropriate data is available. The main advantage is that customer behaviour can be investigated without using expensive and difficult software programs (Kahan, 1998; McCarty & Hastak, 2007; Yeh, Yang, & Ting, 2009).

• Recency is a measure of the amount of time since a customer last placed an order with the company.

• Frequency is a measure of how often a customer orders from the company within a defined period.

• Monetary value is the amount that a customer spends during an average transaction. Lumsdena, Beldona, and Morrision (2008), Marcus (1998), and Fader, Hardie, and Lee (2005) explained the three points as follows. A high score in the field of recency signifies that the customer is very likely to make a repeat purchase. A high frequency rate indicates a high degree of customer loyalty. A higher monetary value is certainly an advantage for the company as well. The monetary value can be the value in currency that the customer has spent in a defined period, the average amount per purchase, or all purchases till now. It is best to use the average purchase amount to reduce the co-linearity of frequency and monetary value. Research has shown that customers with lower recency and higher frequency tend to have lower purchasing potential (Fader, Hardie, & Lee, 2005; Marcus, 1998; Lumsdena, Beldona, & Morrison, 2008). This outcome is very useful for companies.

This method can be used in different ways.

a) Common RFM method

A common RFM method is a simple RFM calculation that splits each recency, frequency, and monetary value into three or more categories. These categories, including timeframe, can be selected freely.

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Table: 9 Recency, frequency, monetary value model (Homburg & Wieseke, 2011)

For example, a customer made the last purchase a month ago, and has placed 12 orders with a monetary value of EUR 450 in the last 12 months. In this case, the customer value number is 123—1 for recency, 2 for frequency, and 3 for monetary value.

The number represents the customer segment. A lower number denotes a more important customer segment. The number 111 indicates the most important customer segment while 333 points at the segment with the least significant customers. Thus, there could be 27 different customer segments, which is too high because each segment has to be treated in a special way. For instance, if the score of one customer is 231 and that of another customer is 211, the second customer should be more valuable for the SME because his score is lower, though both customers have the same recency and monetary value. The difference is that the second customer has ordered more than 20 times and the first one, fewer than 10 times. This means the second one needs more effort and cannot be considered more valuable if the SME focuses only on these figures. The customer score from the common RFM method provides useful information, but is not a good basis for segmenting customers in a proper way.

b) RFM pointing system

Another RFM method is based on a point system. Every customer starts with a fixed number of points, e.g. 25, and the same customer from the above example is used to demonstrate this method.

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Table: 10 Recency, frequency, and monetary value model based on points (Krafft & Albers, 2000)

The customer from the above example gets 132 points. The more points a customer gets, the more valuable he is for the company. The customer segments are classified by points, for example:

Table: 11 Example of customer segments

This kind of RFM method is used mainly in the classic mail order business or internet retail business (Krafft, 2007). Like the common RFM method, this method also has certain disadvantages, such as the frequency being weighted very high. This means that the customer order process in the SME is not fully automatic and every order requires effort, which leads to more cost. Accordingly, fewer orders, or fully automatic orders, would be better for the company. In the classic mail order business or internet retail business, this method is very common and often two additional kinds of criteria are used (Krafft & Albers, 2000).

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Cumulated returns are an important criterion for mail order or internet retail businesses like Zalando (German retailer) or Amazon, because every return, whether entitled or not, is an effort and is linked to costs.

Other RFM methods include the customer quintile method and behaviour quintile scoring method (Miglautsch, 2001). These methods are derived from the first RFM method described in this paper.

c) Customer quintiles

A common method for RFM scoring is to divide customers into five equal segments, ranging from best to worst. Each group has the same number of customers, which makes the analysis easier (Miglautsch, 2001). Finally, the company has three tables—one each for recency, frequency, and monetary value.

Customers with a lower number of days since the last purchase get a higher score for recency. A higher number of purchases generate a better frequency score and a higher amount spent is reflected in a better score for monetary value.

Table: 13 Customer quintile segmentation

The top segment is labelled 5; the one after it is 4, and so on. Every customer is presented with a three-digit number. Customers in the best segment have the number 555 while their counterparts in the worst segment have 111 (Wei, Lin, & Wu, 2010). This method generates 125 equal segments, and it would be quite difficult to evaluate and treat each segment independently (Miglautsch, 2001). One possibility is to club some segments together and treat them similarly. But this is a disadvantage of this method, as it tends to group together customers who have totally different behaviours and needs (Yang, 2004).

d) Behaviour quintile scoring

This method was developed by John Wirth, PhD and founder of Woodworker’s Supply of New Mexico—a leading hand tool retailer. This method also involves five cells, but each cell has a

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different number of customers. However, every cell in the monetary value column reflects the same amount of money.

The frequency and monetary table is sorted by a mean method. Every single purchaser (buy only once) is placed in the lowest cell. Every customer with a total purchase amount under the mean gets a score of two. All customers above the mean are placed in the top three cells.

Table: 14 Behaviour quintile scoring—frequency and monetary table

In some cases, recency is the most powerful of the three variables because recent customers are the most reliable and important for a certain period. During this period, the customer is often contacted heavily. But after this period, the customer is nearly left alone (Miglautsch, 2001). A long-term customer, who has bought an average amount, moves up in frequency and monetary table (Table 14) from cell 1 to 2. In recency, he can move from 1 to 5. In other cases, the variables of frequency and monetary value are more important (Bacila & Radulescu, 2014; Tsai & Chiu, 2004).

Weighting of RFM figures can compensate for this issue.

Monetary, frequency, and recency values can also be added together (Libey, 1998). An option is to add the scores of the three tables. The best customer has a score of 15 (5+5+5), while the worst scores 3 (1+1+1). But many customers have a medium score of 7 or 8, and it is difficult to segment these customers in a proper way (Miglautsch, 2001). A better option is to have a composite weight score. Each company can individually decide on the most important among recency, frequency, and monetary values. The most important parameter has the highest weight. According to Tsai and Chui (2004), the sum of the weights of each RFM calculation should be equal to one. One such calculation could be:

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Score = (recency score*weight)+(frequency score*weight)+(monetary value score*weight)

4.3 = (5*0.5) + (4*0.3) + (3*0.2)

Through the weighted RFM method, an SME can give more power to the more recent names and hence has the opportunity to boost the most relevant score.

A classic RFM calculation looks like the calculation below: Ttoday = Today

Tlast_buy = Days since last order

Tperiod = Number of days in a certain period

buyn = Number of orders

rn = Turnover

Table: 15 Classic recency, frequency, monetary value model (Homburg & Wieseke, 2011)

RFM = R x F x M = 189 x 0.0328 x 105.389 = 646.508

Two methods are generally used to compute RFM. The first method involves sorting customer data from the customer database based on the RFM criteria, grouping them in equal quintiles and analysing the resulting data. However, this number (646.508) does not say anything about the customer value because a high number could be either good or bad for the company. If there is a lower frequency, which is good for the company because of less effort, the total number is lower. In accordance with the assertion of Lumsdena, Beldona, and Morrison (2008) mentioned above, a higher frequency could also be good for the company because loyalty is

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higher. If the monetary value is higher, which is also good for the company, the total number will be higher.

The second method is more useful but still unsatisfactory. It involves the computation of relative weights for R, F, and M by using regression techniques and then using these weights to calculate the combined effects of RFM. In other words, RFM can be considered as the sum of the weighted recency, frequency, and monetary value scores for a customer (Homburg & Werner, 1998).

According to Mühlbacher (2013), one way to derive customer treatment strategies from the RFM method is to design a cube with three dimensions.

Recency: The customer’s last order was either a long time ago or in the recent past. Frequency: The customer buys from our company either seldom or often.

Monetary: The value of the order is either high or low.

For each of these criteria, a sensible measurement variable must be found. This could be, for example, the industry average or the in-house average. This value is then defined as the average value.

e) RFM cube

From this three-dimensional matrix, a cube with eight different small cubes is formed. There should be a marketing and sales strategy for each or these plains. For example, if a customer hardly ever buys from the company, the gap between purchases is long, and the revenue per sale is low as well, the customer will fall in the minimal e-business category. On the other hand, if a customer buys at short or regular intervals and the revenue per sale is also higher than the average, then the customer must be retained.

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Figure: 18 RFM Cube (Freter, 2008)

The RFM method has some great advantages compared with the two methods mentioned previously. Some researchers (Bacila & Radulescu, 2014; McCarty & Hastak, 2007; Wang, 2010) claimed that the RFM technique and its results are easy to understand. Kahan (1998) explained that the RFM method is cost-effective to understand the behaviour of important customers. Miglautsch (2001) argued that it is easy to quantify customer behaviour, too, through this method. Customer data can be evaluated and stored in an accessible electronic form (Lumsdena, Beldona, & Morrison, 2008). This is helpful for a company that needs to handle several different customers. For the success of an SME, it is very important for the selected method to be able to predict responses and boost profits in a short time (Baecke & Van der Poel, 2011), and this method satisfies both these criteria. This is because only a few variables are needed (Wei, Lin, & Wu, 2010) to get meaningful results, which, in turn, are absolutely necessary to target a particular customer (Kaymak, 2001).

The main disadvantage of the method is that it does not always reflect the real customer value, because it identifies only currently valuable customers; if a customer does not buy often, spends less, or shops at longer intervals, then this method accords the customer no

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value (Wang, 2010; Wei, Lin, & Wu, 2010). The RFM method also ignores the analysis of new customers. However, new customers and customers with a low score have a great deal of untapped potential (Miglautsch, 2001). Depending on the industry, socio-demographic and psycho-demographic variables have great influence on customer values, but these indicators are not considered (McCarty & Hastak, 2007). The final disadvantage of the RFM technique is that it is a quantitative method that does not consider qualitative information from different industries (Yeh, Yang, & Ting, 2009). If an SME has a large number of customers ranging from huge corporations to small handcraft businesses, it is not easy to find the right weightage. This is especially a problem for B2B retail companies that often sell their products to a whole range of customers.