RFM Model

RFM model is an analytical model, which is used to segment the customers of the company. It is also known as marketing technique which is used to identify which customers are the best ones.

In this blog we are going to cover:

  1. Data requirement for RFM Model
  2. RFM Model
  3. Interpretation of results
  4. Benefits of RFM Model

1)  Data Requirement

If your data can answer following three questions, than you are ready to build RFM Model:

  1. How recently a customer has purchased?
  2. How often they purchase?
  3. How much the customer spends?

2)  RFM Model

Following are some of the assumptions we take when building RFM model. This assumptions are mostly same for most companies/industries.

  • Customers who purchased recently are more likely to purchase again than the customers who purchased a long time ago.
  • Customers who have purchased more in the past are more likely to respond than the customers who have made fewer purchases.
  • Customers who have spent more (in total for all purchases) in the past are more likely to respond than the customer who have spent less.

3)  Interpretation

Now lets take a dummy dataset and try to understand the Interpretation of RFM Model.

 

(Note: Recency depends on the data you have. It can be in days, months or even years)

Result:

  1. Joey belongs to the “Best Customer” segment. Joey has purchased recently, that is 4 days ago. His Frequency and Monetary values are also high.
  2. Chandler belongs to the “Lost Customer” segment. Chandler has made purchased 50 days ago and then he never returns.
  3. Gunther belongs to the “Risky Customer” segment. Gunther has made purchase 45 days ago, but his monetary value per frequency is high so you can provide offers to retain him.

This are my interpretations, but many Interpretations can be made using the same RFM model technique.

4)  Benefits

  • Increased Revenue
  • Increased Customer Retention
  • Increased Response Rate

 

Hope you enjoyed reading my blog and It was worth reading your time. Thank You 🙂

 

 

 

 

Leave a comment