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?
CLV input data and output
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
|Actual spending segment
|Future spending segment
- Statistics-based model.
Previous page: Dynamic Customer Engagement