Customer Lifetime Value (CLV)

This algorithm results in clusters of customers, based on their actual and predicted spending levels, to determine their importance to your company. It also predicts the following for each customer:

  • Whether they will still be your customer one year from now.
  • How many transactions can be expected from this customer in the next X weeks?
  • Is this customer likely to churn?
  • Will this customer be among those who will provide the most value to the company going forward?

Description

CLV input data and output

Input data Output
Transactional data

Customer_id.

Date.

Order_id.

Order_line_id.

Amount.

Discount.

 

Churn probability.

Expected amount.

Actual spending segment.

Future spending segment.

 

 

 

Details and output examples

The CLV algorithm is based on a complex statistical model that employs the inter-purchase time and purchase regularity. The model also takes the ‘frequency paradox’ into account, meaning that after each high frequency purchase cycle, there is a period of inactivity.

The model computes:

  • Churn probability.
  • Expected spending.
  • Actual spending segment.
  • Future spending segment.

Example of the output

Customer ID 11223344
Churn probability
0.21
Expected amount
€140.23
Actual spending segment
Silver
Future spending segment
Silver-bronze

 

Tags:

  • Statistics-based model.
  • Predictive.
  • Clustering.

 

 

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