RECIPE || Customer Lifetime Value (CLV)

[[Work In Progress]]

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Calculating Customer Lifetime Value (CLV) with Recency, Frequency, and Monetary (RFM)

CLV, "the present value of future cash flow attributed to a customer's tenure with a company", can be determined by variety of approaches. Based on a simple equation, one can calculate the cumulative profit (value) from a customer based on assumptions such as retention rate and historical profit margin from customers. However, a customer's retention can be influenced by factors such as demographics (age, geography, education background), behavior (Recency, Purchase Frequency, Monetary Contribution), competitions, peer influences, etc.. As a result, a dynamic approach should be taken to account for such variations.

In this repo, I calculated CLV by predicting the retention rate (r) of customers' future purchasing cycle using Logistic Regression based on his/her Recency of purchase, purchase Frequency, and Monetary contribution from past purchases.

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RFM

By definition, RFM represents:

• R(ecency): how recently did customer purchase?
• F(rquency): how often do customer purchase?
• M(onetary Value): how much do they spend (each time on average)?

The determination of Recency, Frequency, and Monetary of a customer, should provide insights to the following:

1. How to segment customers to determine who are more likely to response to ads / to purchase?
2. Which type of customers to send ads in order to breakeven and make profit?

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Data

The CDNow data set (download at here), consists of 69,659 transaction records by 23,570 unique customers, captures the purchase records between Jan 1997 and June 1998.

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Code

• Source code (R)

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References

This project was largely based on the work done by Jack Han at Data Apple. However, new libraries/functions are used to update data transformation and visualization of data such as dplyr and ggviz.

Other references used:

• Customer Lifetime Value (CLV) – A Methodology for Quantifying and Managing Future Cash Flows, David C. Ogden

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