RFM clustering

This algorithm enriches the customer profile with data and scores about their purchase Recency, Frequency and Monetary value (RFM).

This is a classic model for identifying your best customers, which assumes that customers who:

  • Have made a purchase recently.
  • Make regular or frequent purchases from you.

and:

  • Spend a large amount with you.

are more likely to respond positively to future engagement activities and product offers.

Description

We use a simple, yet effective clustering algorithm based on the RFM model. The model assigns a score (1 – 10) to each variable, to identify top customers or lost customers.

Example of the RFM model

Customer ID R F M Meaning
11223344 10 10 10 High recency, High frequency, High monetary Top customer.
22331144 1 8 8 Low recency, High frequency, High monetary Lost customer.

 

You must provide data about the customer’s transactions as input. For example, the amount of money spent, the discount received and the purchase date for each transaction.

The output includes information and a score for each Recency, Frequency and Monetary variable.

Customer RFM clustering input data and output

Input data Output
Transactional data

Customer_id.

Date of purchase.

Order_id.

Order_line_id.

Amount.

Discount.

 

Recency.

Frequency.

Monetary.

Recency score.

Frequency score.

Monetary score.

 

Output examples

The output is provided as new fields that are associated with each customer profile.

Example of the output

Customer ID 11223344
Recency 2013-12-25
Frequency 12
Monetary €175.00
Recency Score 1
Frequency Score 6
Monetary Score 7

 

Tags:

  • Clustering.
  • Transactional.

 

 

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