Recency, Frequency, and Monetary Analysis This RFM score, displayed in the table below, is simply the average of the individual R, F, and M scores, obtained by giving equal weights to each RFM attribute. These F and M scores are summarized below:įinally, we can rank these customers by combining their individual R, F, and M rankings to arrive at an aggregated RFM score. For the monetary factor, the top 20% of customers (big spenders) will be assigned a score of 5 and the lowest 20% a score of 1. Similarly, we can then sort customers by frequency from most to least frequent, assigning the top 20% a frequency score of 5, etc. Since customers are assigned scores from 1-5, the top 20% of customers (customer 12, 11, 1) receive a recency score of 5, the next 20% (the next 3 customers 15, 2, 7) a score of 4, and so on. ![]() Let’s begin with ranking customers on recency first, as shown in the below table:Īs seen in the above table, we have sorted customers by recency, with the most recent purchasers at the top. To conduct RFM analysis for this example, let’s see how we can score these customers by ranking them based on each RFM attribute separately.Īssume that we rank these customers from 1-5 using RFM values. ![]() Table 1 contains recency, frequency, and monetary values for 15 customers based on their transactions. Table 1: Example Customer transactions dataset Let’s demonstrate how RFM works by considering a sample dataset of customer transactions:
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |