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  • Writer's pictureAditya Somaiya

RFM Analysis - Using Customer Databases for D2C Brands Retention Strategies

I first came across RFM Analysis - Recency, Frequency, Monetary Value in b-school through Professor Shivram Apte. At that time, point of sales data from organised retail was helping FMCG brands drive decision making and RFM was once such tool that was taught to us.

RFM is a segmentation tool that used percentiles to group consumers on 3 axis

  • When did they last purchase - Recency

  • How frequently do they purchase - Frequency

  • How much do they purchase - Monetary Value

Depending on the size of the dataset and the actions that they brands wants to take, the percentile cuts off can vary, usually clustering is done at the 33rd percentile, each cluster is allocated a code - 3,2,1 and it creates 27 customer segments (illustrated in the table).

Now that the data is sorted, the fun starts!We can group these segments basis the kind of campaigns and actions we want to take.

Use cases:

  • The top spenders who are in the top 2 recency segments (recency - 2,3; monetary value - 3), these consumers can be invited into a loyalty program for the brand

  • High frequency purchasers can be offered subscription services

  • Erstwhile, heavy users for the brand can be targeted with latest offerings for the brand and be purchased to come back into the brand franchise. The cost of re-acquisition will be lower than new acquisition.

These segments can be used as customer lists for email and audience based targeting on Facebook.

The use cases are many - insights, brand objectives and a bit of creativity can make this a powerful tool to engage brand purchasers.

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