Why do we do RFM analysis?

RFM analysis is a powerful tool in marketing that helps marketers make the best of their advertising budget. Instead of simply using an overall RFM average value to identify the best customers, business can use RFM analysis to identify clusters of customers with similar values.

How do you analyze RFM scores?

To calculate RFM scores, you first need the values of three attributes for each customer: 1) most recent purchase date, 2) number of transactions within the period (often a year), and 3) total or average sales attributed to the customer (total or average margin works even better).

What is RFM in data mining?

RFM stands for Recency, Frequency and Monetary value. Association rule mining based on RFM measures analyzes the relationships of product properties and customers’ contributions / loyalties to provide a better recommendation to satisfy customers’ needs.

What is RFM clustering?

RFM stands for Recency, Frequency, and Monetary. The evaluation results showed that the optimal number of clusters for the k-Means method applied in the RFM analysis consists of three clusters (segmentation) with a variance value of 0.19113.

How does RFM help in segmenting the market?

RFM segmentation allows marketers to target specific clusters of customers with communications that are much more relevant for their particular behavior – and thus generate much higher rates of response, plus increased loyalty and customer lifetime value.

How are Rfv scores calculated?

Then, we calculate the RFV score in order to quantify customer behavior. Each customer has a three-digit number out of 125 possible combinations from 111 to 555. We can concatenate the 3 scores or use this formula RFM Score = Recency Score * 100 + Frequency Score * 10 + Volume Score.

How is recency calculated?

Recency = the maximum of “10 – the number of months that have passed since the customer last purchased” and 1. Frequency = the maximum of “the number of purchases by the customer in the last 12 months (with a limit of 10)” and 1.