Productivity and Quality in Smart Manufacturing Systems

A multi layer recency frequency monetary method for customer priority segmentation in online transaction

Andreas Handojo,  Nyoman Pujawan, Budi Santosa & Moses Laksono Singgih

Extended Abstract

Customer segmentation is a critical step toward appropriately differentiating services to different customers. One common way of segmenting customers is by using what is called the Recency, Frequency, Monetary (RFM) approach, where customers are classified based on the recency of their transactions as well as how often they purchase goods and services and how much money they spent. However, this approach is not able to fairly differentiate customers especially when it comes to cases where old customers have decreased or stopped their purchases and new customers just started buying. In order to overcome this, we proposed what is called Multi-Layer Recency, Frequency, and Monetary (MLRFM) approach. In this approach, we divide time periods into multiple layers, and the recency, frequency, and monetary values are analyzed considering these different segments. Our numerical examples show that this multi-layer approach can provide a good alternative for companies that sell products online and customers are behaving very dynamically.

KeywordsCustomer priorityCustomer Segmentation; Online transaction; Recency frequency monetary


The Recency, Frequency, Monetary (RFM) model was first proposed by Hughes (1996) to analyze and predict customer behavior. RFM is a fairly effective and simple method that can be applied in market segmentation (Birant, 2011). RFM analysis is widely used to determine customer ratings based on customer purchase history information that has been recorded in the past. This method is used in various applications that involve large numbers of customers, such as online purchases, retailing, and others (Christy et al., 2021). In this method, customers will be grouped based on three dimensions, namely Recency (R), Frequency (F), and Monetary (M).


Step 1: Transaction data
Step 2: Determine the period of the Multi-Layer Frequency Monetary
Step 3: Calculate MLRFM and summarize the value
Step 4: Normalize MLRFM value
Step 5: Determine the segmentation value
Step 6: Determine the weight
Step 7: Calculate MLRFM scoring result

Figure: 7 Step Multy Layer Recency Frequency Monetary Methods


The results of the proposed method will be compared with the results of the RFM model values. The same transaction data (Table 2) is processed using a normalization mechanism then the results are divided into five parts (Table 3) but this time it does not use layer division or only takes the overall part (all) of the Recency, Frequency, and Monetary factors


Customer segmentation in a business is very important. Companies in general must determine which customers have priority over the other. One of the methods used in the customer segmentation process is the RFM (Recency, Frequency, and Monetary) method. This method considers when was the last time the customer made a transaction/order (Recency), how many orders have been made (Frequency), and how much the customer has spent (Monetary). This method has a weakness in considering the Frequency and Monetary factors, where the consideration factors used are the total number of Frequency and the total number of Monetary only. The general weakness of this method is that the new customers cannot rank higher than old customers, which of course generally have larger Frequency and Monetary scores. This could happen even if the old customer has not been actively placing orders in recent times. For example, the customer has moved to another supplier.


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