Customer churn is one of the most important problems for subscription and recurring-revenue businesses.
A business may know how many customers are leaving, but that does not necessarily tell them which customers are likely to leave next.
That is where machine learning can help.
What is churn prediction?
A churn prediction model estimates the probability that an existing customer will stop using a company’s product or service.
For example:
| Customer | Churn probability |
|---|---|
| Customer A | 8% |
| Customer B | 31% |
| Customer C | 84% |
Instead of treating every customer the same, the company can focus its retention efforts on customers showing a high probability of leaving.
What data can be useful?
Useful variables may include:
- Product usage
- Login frequency
- Purchase frequency
- Customer tenure
- Support interactions
- Payment history
- Recent changes in activity
Which machine learning models can be used?
Several models can work well for churn prediction.
Common approaches include:
- Logistic regression
- Random forest
- XGBoost
- LightGBM
- Neural networks
The best model depends on the size and characteristics of the data.
Prediction alone is not enough
A churn model only creates value when the prediction is connected to an action.
For example, high-risk customers could receive:
- Personalized outreach
- Retention offers
- Product recommendations
- Additional onboarding
- Customer support
The goal is therefore not simply to predict churn.
The goal is to identify customers where an intervention can change the outcome.