Building RFM model on customer purchase behavior to estimate Customer Life Time Value (CLV)

To increase their market share, firms in these days focus more on customer loyalty and profitability. To build a successful long term customer relationship, a firm needs to identify the true value and loyalty of its customers to incorporate a more targeted and personalized approach of marketing. In this article the customer segmentation is done by estimating the customer life time value (CLV) from customer’s past purchase behavior. In order to estimate CLV, RFM (recency, frequency, and monetary) marketing analysis methods is used. To demonstrate how RFM model works, an online retail data set is used to calculate customer’s recency, frequency, and monetary scores. K-means clustering method is used to unsupervised clustering of the data set based on recency, frequency, and monetary scores.

Online Retail shopping data set of customers
Data table of RFM score
Table showing centroid of the clusters
Table showing centroids of the clusters



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